Abstract:
The invention provides, in one aspect, a computerized method for graphically marking an animal for display to a user. The method includes retrieving, with a first computing device (e.g., a smartphone), one or more animal metrics (e.g., weight) from a data store, and capturing, with a second computing device (e.g., Google Glass or similar device) coupled to the first computing device, an image of an animal. The method further includes transmitting, from the second computing device to the first computing device, the captured image of the animal, and identifying, with the first computing device, an animal in the captured image. The method associates, with the first computing device, the identified animal with one or more of the metrics retrieved from the data store, and displays, with the second computing device, one or more visual cues (e.g., colors) based upon the associated one or more metrics retrieved from the data store.

Description:
TECHNICAL FIELD 
       [0001]    The present invention relates to animal management, and more particularly, to systems and processes for determining animal metrics (e.g., weight) based upon analysis of one or more images of the animal, and displaying animal metrics (e.g., weight) to a user. 
       BACKGROUND 
       [0002]    Animal weight is an indicator of animal health, development, and yield. Knowledge of its weight is also useful before administering medicine or other forms of treatment. Typically, weight is measured using weight scales, e.g., a weight scale in the floor of a pen. Scales are expensive, can be inefficient, as they require a time delay to zero, and require maintenance to avoid build up or corrosion from farm debris. Furthermore, moving an animal to a scale can be stressful for both producer and animal. 
       SUMMARY 
       [0003]    The invention provides, in one embodiment, a computerized method for graphically marking an animal for display to a user. The method includes retrieving, with a first computing device (e.g., a smartphone), one or more animal metrics (e.g., weight) from a data store, and capturing, with a second computing device (e.g., Google Glass or similar device) coupled to the first computing device, an image of an animal. The method further includes transmitting, from the second computing device to the first computing device, the captured image of the animal, and identifying, with the first computing device, an animal in the captured image. The method associates, with the first computing device, the identified animal with one or more of the metrics retrieved from the data store, and displays, with the second computing device, one or more visual cues (e.g., colors) based upon the associated one or more metrics retrieved from the data store. 
         [0000]    related embodiments, the second computing device comprises Google Glass or other heads-up display device. In further related embodiments, the first computing device and second computing device are embodied in a single computing device. 
         [0004]    In some embodiments, the one or more visual cues include color markings. In related embodiments, the one or more visual cues are overlayed on top of the image of the animal. In further related embodiments, the one or more animal metrics include any of weight, volume, mass, shape, size, or gender. 
         [0005]    The invention, in another embodiment, features a process for determining a metric (e.g., volume, mass, or weight) and/or characteristic (e.g., gender or species) of an animal based on analysis of one or more images of the animal. By way of overview, the process can include a model with three coefficients related to height, length, and depth. A library of animal models can be used. The library can be created by measuring the weights of known animals, and categorizing the animals into subsets. The categories can include sex, size, shape, and/or age. A weight can be determined by selecting a model from the library, and adjusting the model until it “fits” the image of the animal (or vice versa). The weight of the animal is proportional to the factor by which the model is adjusted relative to the image (or vice versa). The proportional differences between height, length, and depth can be individually adjusted for more accurate weight estimation. 
         [0006]    In another aspect, the invention provides a computerized method for estimating a weight of an animal. The method includes acquiring an image of an animal and comparing, by at least one computing device, the image to a plurality of models to determine a selected one of the plurality of models that optimally matches a size or shape of the animal, wherein each of the plurality of models has a known weight. The method further includes adjusting, by the at least one computing device, either (i) the image relative to the selected model or (ii) the selected model relative to the image. One or more differential adjustment parameters are determined, by the at least one computing device, based upon the adjustment of the image or model; and a weight of the animal is determined, by the at least one computing device, by adjusting the known weight of the selected model based upon the one or more differential adjustment parameters. In some embodiments, the image comprises a plurality of images. 
         [0007]    In some embodiments, the image includes a plurality of cloud points representing the animal in three-dimensions, and each of the plurality of models includes a plurality of cloud points representing an animal of a known weight in three-dimensions. 
         [0008]    In some embodiments, the method involves determining, by the computing device, the selected model by (i) calculating a deviation in cloud points between the image and each of the plurality of models, and (ii) selecting the model having the smallest deviation in cloud points. In related embodiments, the method involves determining, by the computing device, the model by (i) calculating an iterative closest point (ICP) error between the image and each of the plurality of models; and (ii) selecting the model having the smallest ICP error. 
         [0009]    In some embodiments, the method involves adjusting, by the at least one computing device, along any of an x-axis, y-axis, or z-axis, at least one cloud point of (i) the image relative to the selected model or (ii) the selected model relative to the image. 
         [0010]    In some embodiments, the method involves determining, by the at least one computing device, a gender of the animal by comparing a region of the image representing a gender of the animal to a corresponding region of a model having a known gender. In related embodiments, the method involves determining, by the computing device, the selected model based upon a gender of the animal. 
         [0011]    In some embodiments, the method involves determining an additional differential adjustment parameter by comparing one or more cloud points in a region of the image to one or more cloud points of a corresponding region of the selected model, and altering the determined weight of the animal based upon the additional differential adjustment parameter. In related embodiments, further additional parameter(s) are similarly determined for other region(s). In related embodiments, the region of the image represents a depth of the animal, and the corresponding region of the selected model represents a depth of the selected model. In other related embodiments, the region of the image represents one or more body parts (e.g., rump, shoulder, back, head, feet, etc.) of the animal, and the corresponding region of the selected model represents one or more corresponding body parts (e.g., rump, shoulder, back, head, feet, etc.) of the selected model. 
         [0012]    In further related embodiments, the method involves determining an additional differential adjustment parameter by comparing a thickness (or “depth”) of the image relative to the model, and altering the determined weight of the animal based upon the additional differential adjustment parameter. 
         [0013]    In another aspect, the invention provides a computerized method for estimating a weight of an animal. The method includes acquiring an image of an animal, wherein the image includes a plurality of cloud points representing the animal. The image is compared to a plurality of models, by at least one computing device, to determine a selected one of the plurality of models that optimally matches a size and/or a shape of the animal, wherein each of the plurality of models includes a plurality of cloud points representing an animal of a known weight. The method further includes adjusting, by the at least one computing device, at least one of height, length or depth of at least one cloud point of (i) the image relative to the selected model or (ii) the selected model relative to the image. One or more differential adjustment parameters are determined, by the at least one computing device, based upon the adjustment of the at least one cloud point; and a weight is determined, by the at least one computing device, for the animal by adjusting the known weight of the selected model based upon the one or more differential adjustment parameters. 
         [0014]    In another aspect, the invention provides a data processing system for estimating a weight of an animal. The system includes a data store coupled to at least one computing device, wherein the data store stores a plurality of models each representing an animal having a known weight. A fitting engine that executes on the at least one computing device, wherein the fitting engine (i) compares an image of an animal to the plurality of models to determine a selected one of the plurality of models that optimally matches a size and/or a shape of the animal, (ii) adjusts either (i) the image relative to the selected model or (ii) the selected model relative to the image, (iii) determines one or more differential adjustment parameters based upon the adjustment of the image or model, and (iv) determines a weight of the animal by adjusting the known weight of the selected model based upon the one or more differential adjustment parameters. 
         [0015]    In some embodiments, the image includes a plurality of cloud points representing the animal in three-dimensions, and each of the plurality of models includes a plurality of cloud points representing an animal of a known weight in three-dimensions. 
         [0016]    In some embodiments, the fitting engine determines the selected model by calculating a deviation in cloud points between the image and each of the plurality of models, and selecting the model having the smallest deviation in cloud points. In related embodiments, the fitting engine adjusts at least one cloud point of (i) the image relative to the selected model or (ii) the selected model relative to the image, along any of an x-axis, y-axis, or z-axis. 
         [0017]    In another aspect, the invention provides a computerized method for displaying animal metrics with a graphical user interface (GUI). The method includes determining, by at least one computing device, a daily weight for each of one or more animals for each of a plurality of days; determining, by the at least one computing device, an average daily weight (or, “interpolated” daily) for each of the one or more animals, wherein the average daily weight for an animal is determined based upon the plurality of daily weights for the animal; storing, in a data store coupled to the at least one computing device, the average daily weight for each of the one or more animals; and rendering, by a remote computing device coupled to the data store, a graphical user interface (GUI) window displaying the average daily weight for at least one of the animals. 
         [0018]    In some embodiments, the method involves associating each of the one or more animals with any one of a plurality of barns. In related embodiments, the method involves displaying, in the GUI window, the average daily weight for each animal associated with a selected barn, wherein the barn is selected in response to user interaction with the GUI window. In further related embodiments, the method involves (i) associating each of the one or more animals with any one of a plurality of pens; (ii) associating each of the plurality of pens with any one of the plurality of barns; (iii) determining an average pen weight for at least one of the plurality of pens, wherein average pen weight is determined by averaging the daily weight of the one or more animals associated with that pen; and (iv) displaying, in GUI window, the average pen weight for a selected pen, wherein the pen is selected in response to user interaction with the GUI window. 
         [0019]    In some embodiments, the method involves displaying, in the GUI window, a plurality of identifiers, wherein each identifier is uniquely associated with one of the animals. In some embodiments, each identifier comprises an RFID number. 
         [0020]    In some embodiments, the remote device comprises any of (i) a desktop computer, (ii) laptop computer, (iii) tablet computing device, or (iv) other mobile device. In related embodiments, the remote device is coupled to the data store via any of (i) the Internet, (ii) local-area network (LAN), or (iii) wide-area network (WAN). In further related embodiments, the GUI comprises a web page, and the GUI window comprises a region of the web page. 
         [0021]    In some embodiments, the method involves determining an average daily weight gain (ADG) for at least one of the one or more animals, wherein the ADG for an animal is based upon a plurality of daily weights for that animal. In related embodiments, the ADG is determined using a best-fit line method. 
         [0022]    In some embodiments, the method involves determining a forecasted weight for at least one of the one or more animals, wherein the forecasted weight for an animal is based upon the ADG for that animal. In some embodiments, the forecasted weight is determined by multiplying an ADG (e.g., 2 lbs.) for an animal for a current day (e.g., “today”) by a number of selected days (e.g., 14-days) after the current day, and adding the result to a current weight of the animal (e.g., 200 lbs). In some embodiments, the number of selected days is programmable or otherwise customizable (e.g., by a user interacting with the GUI or other feature of the remote device, or a systems administrator, etc.). 
         [0023]    In some embodiments, the method involves determining a number of animals having a forecasted weight within a range of weights. In some embodiments, the method involves displaying the number of animals having a forecasted weight within the range of weights. 
         [0024]    In another aspect, the invention provides a computerized method for displaying animal metrics with a graphical user interface (GUI), including determining, by at least one computing device, a daily weight for each of one or more animals for each of a plurality of days; determining, by the at least one computing device, an average daily weight gain (ADG) for the one or more animals, wherein the ADG for an animal is based upon a plurality of daily weights for that animal; determining, by the at least one computing device, a forecasted weight for the one or more animals, wherein the forecasted weight for an animal is based upon the ADG for that animal; determining, by the at least one computing device, a number of animals having a forecasted weight within a range of weights; rendering, by a remote computing device coupled to the at least one computing device, a graphical user interface (GUI) displaying the number of animals having a forecasted weight within the range of forecasted weights. 
         [0025]    In some embodiments, the forecasted weight is determined by multiplying an ADG for a current day by a number of days after the current day and adding the result to a current weight (e.g., a daily weight) of the animal. 
         [0026]    In some embodiments, the method involves associating each of the one or more animals with any one of one or more groups. In related embodiments, each group represents a farm. In some embodiments, the method involves determining, with the at least one computing device, a number of animals associated with a selected group that have a forecasted weight within a selected weight range. 
         [0027]    In some embodiments, the method involves (i) associating one or more sub-groups with any one of the one or more groups, and (ii) associating each of the one or more animals with any one of the one or more sub-groups. In related embodiments, each sub-group represents a pen. In some embodiments, the method involves determining, with the at least one computing device, a number of animals associated with a selected sub-group that have a forecasted weight within a range of weights. In some embodiments, the method involves displaying, with the GUI, the number of animals in the selected sub-group that that have a forecasted weight within the range of weights. 
         [0028]    In another aspect, the invention provides a computerized method for predicting animal metrics, comprising determining, by at least one computing device, a daily weight for each of one or more animals for each of a plurality of days; determining, by the at least one computing device, an average daily weight gain (ADG) for the one or more animals, wherein the ADG for an animal is based upon a plurality of daily weights for that animal; and determining, by the at least one computing device, a forecasted weight for the one or more animals, wherein the forecasted weight for an animal is based upon the ADG for that animal. 
         [0029]    In some embodiments, the forecasted weight is determined by multiplying an ADG (e.g., for a current day) by a number of days (e.g., 14-days), and adding the result to the daily weight (e.g., 200 lbs). 
         [0030]    In another aspect, the invention provides a method for displaying livestock metrics (e.g., with a graphical user interface, or “GUI”). More specifically, the method can include displaying on a single screen (e.g., a single web page) a farm name and metrics associated with that farm (e.g., average weight of the animals in that farm, average daily gain of all animals in that farm, etc.). 
         [0031]    In some embodiments, the method involves displaying on the same screen a collapsible list of barns associated with that farm in response to user input (e.g., clicking on a graphical icon next to the farm name). In a related aspect of the invention, the method can include sorting the order in which metrics are displayed in response to user input (e.g., clicking on a header such as barn name, average weight, ADG, etc.). 
         [0032]    Further related aspects of the invention can provide displaying on the same screen a collapsible list of pens associated with a selected barn (e.g., by clicking on a graphical icon next to the barn name). This display can show metrics associated with each pen (e.g., average weight of all animals in each pen, average daily gain of all animals in each pen, etc.). In a related aspect of the invention, the method can include sorting the order in which metrics are displayed in response to user input (e.g., clicking on a header such as pen name, average weight, ADG, etc.). 
         [0033]    Yet further related aspects of the invention can provide displaying on the same screen a collapsible list of animals (e.g., identified by RFID numbers) associated with a selected pen (e.g., by clicking on a graphical icon next to the pen name). The display can show metrics associated with each animal (e.g., valid weights, average daily gain, etc.). In a related aspect of the invention, the method can include sorting the order in which metrics are displayed in response to user input (e.g., by clicking on a header such as RFID, weight, ADG, etc.). 
         [0034]    Further related aspects of the invention can provide collapsing (or “compressing”) an expanded list in response to user input (e.g., clicking on a graphical icon next to the name of an entity (e.g., pen, barn, farm, etc.). In a related aspect of the invention, when a list is expanded or collapsed, the associated graphical icon changes (e.g., from a right-facing arrow to a down-facing arrow, etc.). 
         [0035]    In one aspect of the invention, a method is provided for displaying on a single screen a number of livestock within user-specified weight ranges across all barns and pens. In a related aspect of the invention, a user can specify the weight ranges by inputting minimum and maximum weights into a dialogue box. 
         [0036]    In one aspect of the invention, a system is provided for marking livestock (e.g., hogs) that are within a user-defined weight range. Generally, each pen can be equipped with one or more paint sprayers that can be positioned near a water spout where the livestock go to drink water. If a hog accesses the water spout, the system can determine if that livestock&#39;s current weight is within a pre-defined weight range for being sprayed. If so, then it will be sprayed accordingly. 
         [0037]    In one aspect of the invention, a method is provided for configuring one or more paint sprayers with a graphical user interface. More specifically, the method can include setting the weight ranges for the paint sprayers in response to user input. Weight ranges can be set on a per a farm basis, and all pens within a farm can have their paint sprayer range set to operate across the same weight range. 
         [0038]    In a related aspect of the invention, the paint sprayers can be configured via the GUI to spray paint individual animals according to their determined weight range. Thus, for example, hogs in a first weight range can be painted blue, hogs in a second weight range can be painted green, hogs in a third weight range can be painted red, and so forth. This can, for example, allow a person physically entering a barn or pen to quickly recognize weight ranges for individual animals. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0039]    A more complete understanding of the invention can be attained by reference to the drawings, in which: 
           [0040]      FIG. 1  depicts a system and environment for determining and displaying animal metrics (e.g., weight) based upon one or more images of an animal according to one implementation of the invention; 
           [0041]      FIG. 2  depicts a flowchart showing an exemplary process for determining animal weight based upon one or more images of an animal according to one implementation of the invention; 
           [0042]      FIG. 3A  depicts an exemplary three-dimensional (3D) image of an animal according to one implementation of the invention; 
           [0043]      FIG. 3B  depicts an exemplary cropped image of an animal according to one implementation of the invention; 
           [0044]      FIG. 3C  depicts an exemplary fitting model according to one implementation of the invention; 
           [0045]      FIG. 3D  depicts an exemplary height adjustment of a selected model relative to a scanned image according to one implementation of the invention; 
           [0046]      FIG. 3E  depicts an exemplary length adjustment of a selected model relative to a scanned image according to one implementation of the invention; 
           [0047]      FIG. 3F  depicts an exemplary fitting model adjusted for height and length versus a scanned image according to one implementation of the invention; 
           [0048]      FIG. 3G  depicts exemplary fine-tuning adjustments for length and depth according to one implementation of the invention; 
           [0049]      FIG. 3H  depicts exemplary fine-tuning adjustments for depth according to one implementation of the invention; 
           [0050]      FIG. 3I  depicts an exemplary fine tuning adjustment for a rump region of the animal according to one implementation of the invention; 
           [0051]      FIG. 4  depicts an exemplary process for displaying animal metrics (e.g., weight) with a graphical user interface (GUI) according to one implementation of the invention; 
           [0052]      FIG. 5  depicts an x-y axis chart showing exemplary animal daily weights versus date, with weights on the y-axis and date on the x-axis, according to one implementation of the invention; and 
           [0053]      FIGS. 6-13  show exemplary GUI displays according to one implementation of the invention. 
           [0054]      FIG. 14  depicts an exemplary GUI display including a weight range table according to one implementation of the invention. 
           [0055]      FIG. 15  depicts an exemplary GUI display including a forecast weight range table according to one implementation of the invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0056]      FIG. 1  depicts a system and environment  100  for determining and displaying metrics (e.g., weight, volume, or mass) and/or characteristics (e.g., gender or species) of an animal based upon an analysis of one or more images of the animal according to one implementation of the invention. 
         [0057]    As shown in the illustrated embodiment, an animal positioning system (e.g., a chute system)  101  positions an animal  50  (e.g., a livestock animal, such as a pig, hog, cow, or any other kind of animal) such that an imaging system  110  can capture one or more images  111  of the animal  50 . A control system  185  can analyze the image(s)  111  to determine any of a variety of metrics (e.g., weight, size, depth, height, length, thickness, volume, mass, etc.) or other characteristics (e.g., gender, species, etc.) of the animal  50 . The metrics and/or other characteristics can be displayed to a user device  195  via a graphical user interface GUI  196 . 
         [0058]    In the illustrated embodiment, the chute system  101 , control system  185 , and remote device  195 , or any sub-components thereof, are connected to each other via one or more data links (e.g., data link  194 ), such as the Internet, a local-area network (LAN), a wide-area network (WAN), system bus, other type of data link, or any combination thereof. 
         [0059]    Chute System 
         [0060]    Referring to  FIG. 1 , the animal positioning system (e.g., a chute system)  101  used for positioning the animal (e.g., a livestock animal, such as a pig, a cow, or other animal)  50  for analysis (e.g., determining and measuring metrics associated with an animal) can be a closed-ended chute. That is, the chute system  101  can be configured, for example, to allow the animal (e.g., only one animal)  50  to voluntarily enter and stand within the chute system for analysis and then exit the chute system (e.g., after being analyzed). For example, the animal can typically enter and exit the chute system through one (only one) entryway. 
         [0061]    As illustrated, in some embodiments, the chute system  101  includes a frame structure that is formed of a generally rectangular framework having one or more wall structures including a datum structure (e.g., a first sidewall)  102 , a positioning member (e.g., a second sidewall)  104 , and an end, control wall  106  disposed at an end of the chute system that generally forms an end boundary between the first sidewall  102  and the second sidewall  104 . That is, in some aspects, the first sidewall  102  is used as a datum structure along which the animal can be positioned within the chute system, and the other components of the chute system are positioned relative to the datum structure for properly positioning and imaging the animal. Use of such a datum structure in this manner helps to more easily and more consistently position the animal within the chute system. 
         [0062]    A chute entryway  108  is positioned at an end of the chute system  101  opposite the control wall  106  so that animals can enter and exit the chute system  101 . In some embodiments, the entryway  108  includes a door configured to open manually or automatically (e.g., when an animal approaches the chute system  101 ). Alternatively, in some embodiments, the entryway  108  is in the form of an opening (i.e., without a door) through which the animals can enter and exit the chute system  101 . 
         [0063]    The frame structure can be of various sizes based on the type of animals with which the chute system will be used. For example, for some types of pigs, the frame structure can be about 20 inches wide (i.e., the entryway  108  can be about 20 inches wide) and about two feet tall. 
         [0064]    The second sidewall  104  typically includes a visual analysis system (e.g., an imaging system)  110  attached thereto for analyzing an animal positioned within the chute system  101 . As discussed herein, the imaging system  110  can be configured to capture an image  111  (or multiple images  111 ) of the animal in order to determine metrics (e.g., size or weight) and/or characteristics (e.g., gender or species) of the animal  50 . 
         [0065]    The second sidewall  104  is typically angled (i.e., angled away from the first sidewall  102 ) to enable the imaging system  110  to better capture a side view of the animal. For example, the second sidewall  104  is angled so that a lower portion of the second sidewall  104  can be positioned close enough to a lower portion of the first sidewall  102  to properly position the animal, for example, by limiting (e.g., restricting) the available floor space on which the animal can stand within the chute system  101 . That is, when an animal walks into the entryway  108 , the spacing between the lower portion of the second sidewall  104  and the lower portion of the first sidewall  102  directs or guides (e.g., as a result of the limited floor space) into a consistent, desired location that is preferred for imaging the animal. As discussed below, the consistent positioning of animals within the chute system  101  by the second sidewall  104  can help to enable the imaging system  110  to consistently capture images of different animals so that the different animals can be compared to one another (e.g., for further processing). 
         [0066]    The chute system  101  also includes various components and devices with which the animal can interact within the chute system  101 . For example, the chute system  101  can include one or more of an animal detection system  120 , an animal identification system  140 , an animal marking system  160 , and an animal injection system (e.g., an automatic or semi-automatic injection system)  180 . The various systems and devices within the chute system are typically in communication with a control system  185 , discussed further below, that can operate the various systems to control the chute system  101 . 
         [0067]    The animal detection system  120  can include any of various systems and devices that are configured to detect that an animal is present within the chute system. For example, in some embodiments, the animal detection system  120  includes a feeder switch  122  that, when an animal enters the chute system and begins to feed (e.g., drink water or consume a food product), the feeder switch  122  can be triggered to send a signal to the control system  185  to indicate that an animal has entered the chute system  101  and processing of the animal can begin. 
         [0068]    The feeder switch  122  can be configured to activate and send a signal to the control system  185  as a result of the animal entering the chute system and feeding from any of various different sources, including drinking water or consuming a liquid or solid food source. Once the animal detection system  120  indicates to the control system  185  that an animal is present within the chute system  101 , the control system  185  can initiate any number of metric measuring routines, such as determining the animal&#39;s weight using the information obtained by the imaging system  110 . Additionally or alternatively, the animal marking system  160  or the animal injection system  180  can also be used after presence of the animal is detected. 
         [0069]    Alternatively or additionally, the animal detection system  120  can include any of various other types of devices that can suitably detect or determine the presence of an animal and indicate the same to the control system  185 . For example, in some embodiments, the animal detection system  120  includes a proximity sensor, an infrared sensor, a motion detector, photocell, or other suitable devices that, can detect the presence of an animal within the chute system, and can send a detection signal to the control system  185 . 
         [0070]    Alternatively or additionally, in some embodiments, the animal detection system  120  can include at least one device configured to view the chute system  101  and visually determine when an animal has entered the chute. For example, the imaging system  110  can be used to determine when an animal has entered the chute. 
         [0071]    In some embodiments, a temperature sensor  130  is alternatively or additionally disposed on the control wall  106  to measure an animal&#39;s temperature (e.g., the animal&#39;s internal body temperature). As illustrated, in some examples, the temperature sensor  130  is arranged just below the animal feeder  126  along the control wall  106 . The temperature sensor  130  can be in the form of any of various known temperature measuring devices that are configured to measure an animal&#39;s temperature noninvasively. Examples of such temperature sensors can include an infrared-based temperature sensing device. 
         [0072]    The imaging system  110  can include any of various imaging devices that can suitably capture one or more images  111  of the animal  50  in the chute system  101 . In some embodiments, the imaging system  110  can include a stereoscopic imaging device configured to capture multiple images (e.g., in some cases simultaneously) of the animal arranged within the chute system positioned using the first sidewall  102  and the second sidewall  104 . For example, the imaging system  110  can include one or more of a stereoscopic video camera, charged-coupled-devices (CCD), a photodiode array, a complimentary metal-oxide semiconductor (CMOS) optical sensor, a still photographic camera, a digital camera, a conventional two-dimension camera, three-dimensional (3D) camera or another type of imaging device. In some embodiments, the imaging system  110  can utilize a single camera  110 , or multiple cameras  110 . 
         [0073]    In the illustrated embodiment, the image  111  is a three-dimensional (3D) scanned image, although in other embodiments it can be one or more 3D or two-dimensional (2D) images. The image  111  can be acquired by scanning one or more pre-existing 2D images (e.g., a .JPEG image), or it can be acquired directly from a scan performed by the imaging system  110 . In the illustrated embodiment, the image  111  includes x-y-z coordinates that represent the animal  50  in three-dimensions. More specifically, the image  111  includes a point cloud representation of the animal  50  in three-dimensions (e.g., as shown in  FIGS. 3A and 3B , discussed below); the point cloud itself includes a plurality of individual cloud points (e.g., as shown in  FIGS. 3A and 3B , discussed below). In other embodiments, the point cloud can be used to create a wire-frame model of the animal  50 . 
         [0074]    In the illustrated embodiment, an image (e.g., image  111 ) of an animal (e.g., animal  50 ) can have a “length,” “height,” and “depth,” based on one or more cloud points (e.g., possibly hundreds or thousands) plotted along an x-axis, y-axis, and z-axis, respectively, although in other embodiments it can be otherwise (e.g., in embodiments using 2D images). 
         [0075]    In some cases, the imaging system  110  can include one or more of any number of filtering or lens controlling mechanisms. For example, an adapted lens can be used to limit the vertical and horizontal field-of-view of the imaging system, thereby manipulating (e.g., optimizing) an image area for image processing (e.g., for determining weight of the animal). The imaging system  110  can also include auto positioning and focusing systems or additional processing systems for performing image analysis including hardware components (e.g., an image processor) and/or software. 
         [0076]    In some embodiments, the imaging system  110  includes a lighting device to illuminate the field-of-view of the imaging system  110 . The lighting device is typically arranged to illuminate a broadside of the animal (e.g., the side view of the animal) when the animal is positioned within the chute system  101 , for example, while feeding from the animal feeder  126 . The lighting device can include one or more of any various systems or devices configured to emit suitable light to illuminate the animal. For example, the lighting device can include a linear array of lights, such as an array of monochromatic light emitting diodes (LEDs) with diffusers. In some embodiments, the lighting device is alternatively or additionally disposed on the first sidewall  102 , opposite the imaging system  110 . Such an arrangement of the lighting device opposite the imaging system  110  can enable the lighting device to backlight the animal so that the imaging system  110  can capture a well-defined, contrasted image of the animal. 
         [0077]    In some embodiments, the imaging system  110  can also be used as an animal detection device (e.g., the animal detection system  120 ). For example, the animal detection system  120  can include the imaging system  110 , which can be operated (e.g., continuously operated) to monitor the chute system  101 . Once an animal is detected, for example, when the imaging system  110  (i.e., in conjunction with the control system  185 ) detects motion of an object (e.g., an animal) within the chute system, a signal can be sent to the control system  185  that begins processing of the animal. For example, in some cases, once motion of an animal is detected using the imaging system  110 , the control system  185  can send a signal to the lighting device to illuminate the animal so that an image can be captured and the animal&#39;s metrics (e.g., weight) and/or characteristics (e.g., gender) can be determined. 
         [0078]    Additionally, in some embodiments, the chute system  101  includes an imaging calibration system that can be used to set up and calibrate the imaging system  110  for properly capturing images of an animal positioned in the chute system  101 . The imaging calibration system can be a component of the imaging system  110  or a separate component with which the imaging system  110  can interact for calibration. In some embodiments, the calibration system can include a calibration block mounted on one of the sidewalls that the imaging system view and analyze for calibration. 
         [0079]    The imaging system  110 , alone or in combination with the control system  185 , is typically used to capture one or more images of animals within the chute to determine metrics associated with the animal. For example, the imaging system  110  can capture a side view image (e.g., a three dimensional image) of an animal and, based on various algorithms executed by the imaging system  110  and/or the control system  185 , estimate (e.g., determine) the weight of the animal. 
         [0080]    While the chute systems have been described generally as having one imaging system  110  that can be used to analyze an animal present in a chute system, other configurations are possible. For example, in some embodiments, the chute system includes more than one imaging system  110  (e.g., two, three, four, five or more imaging systems) in communication with the control system  185  and/or the other imaging systems. In some embodiments, a chute system can include two imaging systems  110 , which can be positioned on the same side of an animal to capture multiple side views of the animal for image processing. Alternatively or additionally, in some embodiments, one or more imaging systems are positioned generally above the chute system in order to obtain a top view image of the animal. For example, animal metrics and/or characteristics can be determined using a combination of one or more side images and one or more top images of the animal in the chute system. Additional description and details related to this type of image processing and characteristic detection can be found below. 
         [0081]    With continued reference to  FIG. 1 , the animal injection system  180  typically includes an injection unit  182  connected to one of the chute walls (e.g., the first sidewall  102 ). The injection unit  182  can include any of various devices configured to administer (e.g., inject) a substance into an animal positioned in the chute system. For example, the injection unit  182  can include a syringe device, a repeating injector, a multi-dose syringe, or other systems or devices configured to selectively inject a fluid into an animal, for example, in response to a command from the control system  185 . 
         [0082]    As illustrated, the injection unit  182  can be connected to the chute wall via a connection mechanism (e.g., a robotic arm)  184 . The connection mechanism  184  can be configured to selectively move toward and away from an animal positioned between the first sidewall  102  and the second sidewall  104  during an injection procedure. 
         [0083]    The animal injection system  180  is typically in communication with the control system  185  to send and receive signals (e.g., injection instructions) based on signals received from one or more other systems of the chute system  101 . For example, in some embodiments, when an animal enters the chute system  101  and the imaging system  110  captures an image of the animal so that the animal&#39;s weight can be estimated (e.g., determined), an injection can be administered (based on instructions from the control system  185 ) in response to the determined weight of the animal. This can be beneficial since certain animals can be administered certain types of medical injections only if they have grown to a certain weight (e.g., a threshold weight). For example, if the chute system  101  determines that a pig positioned in the chute weighs at least 101 lbs (e.g., by capturing an image of the pig and processing the image as described above), the animal injection system  180  can inject the pig with certain substances (e.g., chemical castration substances). This can greatly increase the efficiency by which animals can be sorted and provided with necessary medications. 
         [0084]    In some embodiments, the chute system  101  has an animal identification system  140  arranged along one of the chute walls (e.g., the first sidewall  102 ). The animal identification system  140  is configured to identify a particular animal that has entered the chute system  101 . The animal identification system  140  can include one or more of various types of devices including scanners, transponder detectors, transceivers, or other types of suitable identification devices. For example, in some embodiments, the animal identification system  140  includes a radio-frequency identification (RFID) reader that is configured to communicate with and identify an RFID tag associated with an animal. For example, one or more animals in a particular area (e.g., within a pen or barn area) can each have their own RFID tag, which can be affixed to the animal, for example, affixed to the animal&#39;s ear or implanted under the animal&#39;s skin. Alternatively or additionally, in some embodiments, the animal identification system  140  can include visual identification systems, such as barcode readers (e.g., a reader that can read a barcode or marking applied to the animal using a printer (e.g., an ink-jet barcode printer or a stain printer), for example, printers manufactured by EBS Ink-Jet Systems USA, Inc of Libertyville, Ill.), configured to identify the animal based on markings applied to the animal. Alternatively or additionally, the animal identification system  140  can include a variety of other devices to read characters (e.g., numbers or letters (e.g., identification numbers)) printed on an animal. In some cases, the animal identification system  140  is configured to read any of various other types of inks or stains (e.g., semi-permanent stains (e.g., 20-24 week stains) or permanent stains) that have been applied to an animal (e.g., using a printer). Alternatively, in some embodiments, a user can manually enter the identity of the animal (e.g., by visual inspection of the animal or an identification tag on the animal). For example, the animal identification can be associated with an animal&#39;s lot or identification number, age, sex, breed, market classification, domestic information relating to growth hormones, and any other pertinent information relating to the animal. As discussed above, the animal identification system  140  is typically configured to communicate the animal identification to the control system  185  for use therein. 
         [0085]    The animal marking system  160  can also be disposed along one of the chute walls (e.g., the first sidewall  102  in the example illustrated) so that an animal within the chute system can be marked for one or various purposes. The animal marking system  160  is configured to mark or otherwise tag animals in the chute system with a visual identifier so that they can be distinguished from one another. For example, in some embodiments, the animal marking system  160  can mark animals with different colored paints or numbers for visual identification. In some embodiments, the animal marking system  160  comprises a device (e.g., a barcode printer) configured to apply a barcode to the animal. In some examples, the animal marking system  160  comprises a printer (e.g., an ink jetbarcode printer or a stain printer), for example, printers manufactured by EBS Ink-Jet Systems USA, Inc of Libertyville, Ill. In some cases, the animal marking system  160  can apply a stain (e.g., a semi-permanent stain (e.g., a 20-24 week stain) or a permanent stain) to the animal. 
         [0086]    Such visual identifiers applied by the marking systems for tagging or marking animals can be used for subsequent managing the animals (e.g., feeding or sorting the animals). The visual identifiers can be applied based upon a determined weight of an animal as determined using the imaging system  110 . For example, if an animal&#39;s weight is greater than a predetermined threshold weight, the animal marking system  160  can apply (e.g., spray) a predetermined indicator (e.g., a mark of a predetermined colored) on the animal to serve as a visual indicator that the animal has achieved the threshold weight and can be dispositioned accordingly (e.g., to receive certain medical treatments, or proceed to processing (e.g., slaughter)). 
         [0087]    In some embodiments, the animal marking system  160  includes a marking device (e.g., a painting device or barcode application device, as described above)  162 , which can be attached to the chute wall with a connection mechanism (e.g., a robotic arm)  164 . The connection mechanism  164  is typically in communication with the control system  185  and configured to selectively move toward and away from an animal positioned in the chute system for marking the animal. The connection mechanism  164  can include any of various systems or devices configured to move the marking device  162  and can be similar or substantially the same as the connection mechanism  184  discussed above. 
         [0088]    Additional description and details of chute systems can be found in co-pending application identified by Attorney Docket Number CRW-003, filed on the same day as the subject application, the contents of which are hereby incorporated by reference in their entirety. 
         [0089]    Control System 
         [0090]    Generally, the control system  185  controls the subsystems of the chute system  101 , described above, and executes a fitting engine  193  that determines animal metrics (e.g., weight, volume, mass, shape, size, etc.) and/or characteristics (e.g., gender, species, etc.) based upon one or more images of an animal, as discussed further below. The control system  185  can further store the metrics (e.g., in data store  191 ) for display to a user (e.g., via GUI  196 ). 
         [0091]    In the illustrated embodiment, the control system  185  can be one or more desktop computers, servers, laptops, mobile devices, custom computing devices, other computing devices, or any combination thereof, albeit as adapted in accord with the teachings hereof. An exemplary control system  185  is shown in  FIG. 1 , including a central processing unit (CPU)  186 , random access memory (RAM)  187 , input/output (I/O) circuitry  188 , adapters  189   a - c , a non-transitory storage medium  190 , and a fitting engine  192 . 
         [0092]    The central processing unit  186  is typically a general-purpose microprocessor or central processing unit and has a set of control algorithms, comprising resident program instructions and calibrations stored in the memory  188  and executed to provide the desired functions. The central processing unit  186  executes functions in accordance with any one of a number of operating systems including proprietary and open source system solutions. In some embodiments, an application program interface (API) is preferably executed by the operating system for computer applications to make requests of the operating system or other computer applications. The description of the central processing unit  186  is meant to be illustrative, and not restrictive to the disclosure, and those skilled in the art would appreciate that the disclosure may also be implemented on platforms and operating systems other than those mentioned. 
         [0093]    In some embodiments, the I/O circuitry  187  includes various connection ports for connecting the animal detection system  120 , the imaging system  110 , the injection system  180 , various sensors, the animal identification system  140 , and/or the animal marking system  160 . In some embodiments, the animal detection system  120 , the imaging system  110 , the injection system  180 , various sensors, the animal identification system  140 , and/or the animal marking system  160  are communications-enabled components configured to communicate via the communication adapter  189   c.    
         [0094]    In the illustrated embodiment, the adapters  189   a - c  include a display adapter  189   a  for connecting the control system  185  to a display device, a user interface adapter  189   b  for connecting the control system  185  to user input devices, such as a keyboard, a mouse, and/or a microphone, and a communications adapter  189   c  for connecting the control system  185  to a network (e.g., network  194 ). In some embodiments, the network adapter  189   c  is a wireless adapter. Other embodiments can have a greater or lesser number of such adapters. 
         [0095]    The storage medium  190  is configured to store, access, and modify a database (or “data store”)  191 , and is preferably configured to store, access, and modify structured or unstructured databases for data including, for example, fitting models  192 , relational data, tabular data, audio/video data, and graphical data. 
         [0096]    The illustrated fitting models  192  comprise a library (or, “set”) of models that represent animals with one or more known metrics (e.g., weight, volume, mass, size, shape, etc.) and/or characteristics (e.g., gender, species, age, type, etc.). The library of models  192  can be created, for example, by weighing and categorizing (e.g., by gender, species, etc.) live animals, and acquiring an image (e.g., 3D or 2D) of each of those animals (e.g., via imaging system  110  or otherwise). In the illustrated embodiment, each fitting model  192  is a scanned image of an animal having a known weight, and each model  192  represents the animal with a plurality of cloud points in three-dimensions. In other embodiments, the cloud points can be used to generate a wire-frame model. In addition to a known weight, the models  192  can each have other known metrics or characteristics as well, such as gender, animal species, age, etc. 
         [0097]    For example, the plurality of models  192  can include a set of forty-eight models; twenty-four male models and twenty-four female models. Both the male models and the female models can be, individually, broken into three tiers (or “sub-sets”)—eight small, eight medium and eight large. Once an animal (e.g., animal  50 ) is characterized as, for example, male and large, the image of the animal (e.g., image  111 ) can be compared against the eight models in that tier, instead of the 48 models total. For some animals, such as cows, models can be further split into a left side or a right side category because, for example, the left side of a cow can have a larger profile than the right side. 
         [0098]    In the illustrated embodiment, each model can have a “length,” “height,” and “depth” based on one or more cloud points (e.g., possibly hundreds or thousands) plotted along an x-axis, y-axis, and z-axis, respectively, although in other embodiments it can be otherwise (e.g., in embodiments using 2D models). 
         [0099]    In the illustrated embodiment, the models  192  are stored in data store  191  on a non-transitory storage medium  190 , although in other embodiments they can be stored otherwise (e.g., in one or more data stores executing on one or more separate computing systems). Additionally, although 3D models are used in the illustrated embodiment, in other embodiments, 2D models can be used. 
         [0100]    The illustrated fitting engine  193  executes on the control system  185  to determine a weight, or other metrics (e.g., size, shape, volume, mass, etc.) and/or characteristics (e.g., gender, type, species, etc.) of the animal  50 . Generally, the fitting engine  193  compares the image  111  to one or more of the models  192  in order to select a model  192   a , from the set of models  192 , that optimally matches a size and/or shape of the imaged animal  50 . An estimated weight can be determined, for example, based upon a relationship between the image  111  and the selected model  192 . For example, if the selected model is 5% “larger” than the image, an estimated weight can be determined by increasing the known weight of the model by 5%. An exemplary weight estimation process is discussed in greater detail below with reference to  FIG. 2 . 
         [0101]    Although the above structure and functionality of the control system  185  is shown in a single unitary system, it will be appreciated that in some embodiments, such structure and/or functionality can be contained in multiple devices. For example, there can be multiple processors, fitting engines, data stores, etc., executing on multiple devices, e.g., in a distributed computing environment, such as a “cloud computing” environment or otherwise. Additionally, it will be appreciated that in other embodiments, the functionality of the fitting engine  193  can be contained within one or more other components, e.g., the CPU  186  or otherwise. 
         [0102]    Remote Device 
         [0103]    Illustrated remote device  195  comprises one or more computing devices (e.g., desktop computer, laptop computer, server computer, tablet device, mobile device, Google Glass or other heads-up display device, etc.) connected to the control system  185  via network  194 . In some embodiments, the remote device  195  and control system  185  can be implemented in a single device (e.g., a mobile device executing one or more applications). The remote device  130  is typically operated by a user to view animal metrics (e.g., weight, etc.) via a graphical user interface (GUI)  196 , as discussed further below. For example, the GUI  131  can be a web browser, custom or generic Windows OS application, or other application designed to display and/or receive input from a user. Although only one remote device  195  is shown here, it will be appreciated that in practice many such devices  195  can be connected to the control system  185 . Further details of the GUI  196  can be found below with reference to  FIGS. 4-13 . 
         [0104]    Weight Estimation Process 
         [0105]      FIG. 2  depicts an exemplary process  200  for determining animal metrics (e.g., weight) based upon one or more images of an animal according to one implementation of the invention. Although in the illustrated embodiment the animal is a livestock animal (e.g., animal  50 ), in other embodiments, it can be another type of animal (e.g., human, domestic animal, or wild animal).  FIGS. 3A-3I  are related to the process  200  according to one implementation of the invention, and are discussed in connection with the individual process steps  205 - 260 . It will be appreciated that  FIGS. 3A-3I  are shown for exemplary purposes, and are not necessarily representative of every embodiment of the process  200 , or the invention as a whole. 
         [0106]    In Step  205 , one or more images (e.g., images  111 ) of an animal (e.g., animal  50 ) are acquired by one or more cameras (e.g., imaging system  110 ). See  FIG. 3A , discussed below, for an exemplary image. Multiple cameras, or multiple images, can be used, for example, to acquire different angles of the animal. Acquiring different angles of the animal can, for example, increase the coverage of the animal, which can increase an overall accuracy of the weight estimation process. When multiple cameras are used, the individual scans (or “images”) can be “registered” and “merged” to form a single representation (e.g., 3D model) of the animal in a manner known in the art of image computation, albeit as modified in accord with the teachings hereof. 
         [0107]      FIG. 3A  depicts an exemplary 3D image  300  of an animal (e.g., animal  50 ) according to one implementation of the invention. The image  300  includes a point cloud  310  representation of the animal in three-dimensions, wherein the point cloud includes a plurality of individual cloud points (e.g., cloud point  315 ). 
         [0108]    Returning back to  FIG. 2 , in step  210 , an animal (e.g., animal  50 ) is identified in the image (e.g., image  111  or  300 ) and the image is cropped. See  FIG. 3B , discussed below, for an exemplary cropped image. For example, the “leg” and “head” regions of the animal can be cropped, leaving just a “body” region of the animal. This still allows, for example, the process  200  to work because most of the weight of the animal is in the body, and the weight of the head and legs are assumed to be a small portion of the overall weight. Although the image is cropped in the illustrated embodiment, in other embodiments it can be cropped otherwise, or not at all. For example, in other embodiments, the process  200  can use the size of the head and/or legs, e.g., for a more accurate weight determination. 
         [0109]      FIG. 3B  shows an exemplary image  400  of an animal (e.g., animal  50 ), according to one implementation of the invention, wherein the head region  420  and leg regions  425 , 430  have been cropped out, as well as the surrounding points  431 , leaving just a body region  410  of the animal More specifically, the image  400  includes a plurality of cloud points representing the regions  410 - 431  in three-dimensions, i.e., along x-axis  440 , y-axis  445 , and z-axis  450 . By way of example, the unit of measurement along the x, y, and z axis can be meters, although it need not be. The same unit of measurement can be applied to  FIGS. 3D-3I , as well, although, again it can be otherwise. 
         [0110]    Returning back to  FIG. 2 , in step  220 , a fitting model (e.g., fitting model  192   a ) is selected (e.g., by fitting engine  193  executing on computing system  185 ) from a plurality of models (e.g., models  192 ) based upon a size, shape, gender, and/or type of the animal (e.g., animal  50 ). See  FIG. 3C , discussed below, for an exemplary fitting model. More specifically the image (e.g., image  111  or  410 ) is compared to the plurality of models via a computing device (e.g., computing system  185  executing fitting engine  193 ) to determine a selected one of the plurality of models that optimally matches a size or shape of the animal, wherein each of the plurality of models has a known weight. 
         [0111]    Multiple fitting models can be initially selected, and the best fitting model among those can be selected before proceeding to the next step  230 . Such a process can be accomplished by comparing which fitting model&#39;s size or shape is the closest fit to the captured scan (e.g., image  111  or cropped image  410 ). A model can be selected, for example, by calculating the iterative closest point (ICP) error between the image and each of the fitting models (or a sub-set of the fitting models), and selecting the model with the minimum error. In other embodiments, other algorithms can be used instead of, or in addition to, ICP. 
         [0112]    More particularly, a model can be selected, for example, by calculating the ICP error between one or more cloud points of the image and one or more cloud points of each of the fitting models (or a sub-set of the fitting models), and selecting the model with the minimum error. As above, in other embodiments, other algorithms can be used instead of, or in addition to, ICP. 
         [0113]    An exemplary fitting model (e.g., model  192   a ) is depicted in  FIG. 3C . The model has at least a known weight because the scan was acquired from an animal that was previously weighed (e.g., on a scale). More specifically,  FIG. 3C  depicts a model comprised of plurality of cloud points  500  representing an animal in three-dimensions. Although a three-dimensional model is shown here, it will be appreciated that in other embodiment the models can be two-dimensional. 
         [0114]    Returning back to  FIG. 2 , in step  230 , the selected model (e.g., model  192   a  or  500 ) is adjusted until it matches, or substantially matches, the captured scan (e.g., image  111  or  410 ). Alternatively, the image can be adjusted to match, or substantially match, the selected model. In the illustrated embodiment, ICP (or other algorithm) can be used to match the scanned image and the selected model, and ICPErr (‘ICP error’) indicates when the best fit has been achieved. See  FIGS. 3D-3F , discussed below, for exemplary adjustments. 
         [0115]    In some embodiments, step  230  (or other step of process  200 , e.g., step  250 , discussed below) can adjust at least one of height (i.e., in a y-axis direction), length (i.e., in an x-axis direction) or depth (i.e., in a z-axis direction) of at least one cloud point of (i) the image relative to the model or (ii) the model relative to the image. One or more differential adjustment parameters can be calculated based on the adjustments of the one or more cloud points, as indicated in step  240 . 
         [0116]    For example, the selected model can be adjusted in both height and length directions until it matches, or substantially matches, the image. The model is adjusted by a ratio (or “differential adjustment parameter”) R 1  so it is as close to the scan size as possible. The objective is to “fit” the model to the image as best as possible in the X-Y direction (minus the depth). 
         [0117]      FIG. 3D  shows an exemplary height adjustment of a selected model (e.g., model  192   a  or  500 ) relative to a scanned image (e.g., image  111  or  410 ) plotted in a 3D graph  600 . More specifically, the scanned image is represented by rectangular clouds points (e.g., cloud point  610 ) and the selected model is represented by circular cloud points (e.g., cloud point  620 ). The points are plotted in three-dimensions along an x-axis  630 , a y-axis  640  and a z-axis  650 . To adjust for the height of the animal (e.g., animal  50 ), points are selected (e.g., by fitting engine  193 ) around the edges at the top and bottom regions of the scanned image and the selected model in order to match, or substantially match, them together (e.g., via ICP). 
         [0118]      FIG. 3E  shows an exemplary length adjustment of a selected model (e.g., model  192   a ) relative to a scanned image (e.g., image  111  or  410 ) plotted in a 3D graph  700 . More specifically, the scanned image is represented by rectangular clouds points (e.g., cloud point  710 ) and the selected model is represented by circular cloud points (e.g., cloud point  720 ). The points are plotted in three-dimensions along x-axis  730 , y-axis  740  and z-axis  750 . In order to adjust for the length of the animal (e.g., animal  50 ), points are selected (e.g., by fitting engine  193 ) around the edges at the sides of the scanned image and the fitting model in order to match, or substantially match, them together (e.g., via ICP). 
         [0119]      FIG. 3F  shows a fitting model (e.g., model  192   a  or  500 ) adjusted for height and length versus the scanned image (e.g., image  111  or  410 ) plotted in a 3D graph  800 . More specifically, the scanned image is represented by rectangular clouds points (e.g., cloud point  810 ) and the selected model is represented by circular cloud points (e.g., cloud point  820 ). The points are plotted in three-dimensions along an x-axis  830 , a y-axis  840  and a z-axis  850 . As illustrated, the adjusted fitting model is fairly close in size to the captured scan 
         [0120]    Returning back to  FIG. 2 , in step  250 , one or more fine-tuning steps are performed to increase an overall accuracy of the weight determination process  200 . Generally, the fine-tuning steps include determining one or more additional differential adjustment parameters by comparing one or more cloud points in a region of the image (e.g., image  111  or  410 ) to one or more cloud points of a corresponding region of the selected model (e.g., model  192   a  or  500 ). The determined weight of the animal can be adjusted based upon the one or more additional differential adjustment parameters. The regions can include, for example, spatial regions of the image or model (e.g., a top region, bottom region, side region, width, depth, height, length, thickness, etc.) or anatomical regions (e.g., rump, shoulder, back, legs, head, body, etc.). 
         [0121]    Although the fine-tuning steps are shown here before the weight determination step  260 , it will be appreciated that in some embodiments, the fining tuning steps  260  can be performed after or during the weight determination step shown in step  260  below. Thus for example, the fine-tunings steps  250  could be used to adjust an already determined estimated weight. 
         [0122]    In the illustrated embodiment, the fining tuning steps include the following steps, although other embodiments may include a lesser or greater number of such steps. Indeed, in some embodiments, the weight estimation process  200  can forgo the fine-tuning steps altogether. 
         [0123]    Fine-Tune Depth-&gt;Ratio-L 
         [0124]    This step is to fine-tune the length of the animal. It can, for example, measure the distance from a “back” region of the animal to a “shoulder-leg” region of the animal. This step determines more accurately the length of the animal, which can be used to adjust the length of the selected fitting model (e.g., model  192   a  or  500 ). See  FIG. 3G , for example. The “regions of interest” (or, simply, “region”) are therefore chosen to compare just the points around the back and the shoulder in this example. Therefore, if this ratio (or “differential adjustment parameter”) is different than the ratio R 1 , then we can use this to make the appropriate adjustment to the length (from head to rump) of the animal. As an example, if Ratio-L is less than W 1 , the final determined weight needs to be decreased. 
         [0125]    Fine-Tune Depth-&gt;Ratio-D 
         [0126]    In the illustrated embodiment, the depth adjustment is done in two steps, although in other embodiments it can be done with a greater or lesser number of steps. The first step is to match both the fitting model (e.g., model  192   a  or  500 ) and the scanned image (e.g., image  111  or  410 ), e.g., as shown in  FIG. 3G  (discussed below), and compare the “center line” of both the model and the image with respect to each other, e.g., as shown in  FIG. 3H  (discussed below). If the fitting model is “thicker” (i.e., has a greater depth) than the scan, then the half-line of the scan is “in-front” of the half-line of the model, and hence, has a “negative” value. Conversely, if the model is “thinner” (i.e., has a lesser depth) than the scanned image, then the half-line of the scanned image is “behind” the half-line of the model, and hence, has a “positive” value. These values are then used to further refine the weight. 
         [0127]    In  FIG. 3G , the scanned image (e.g., image  111  or  410 ) and the selected fitting model (e.g., model  192   a  or  500 ) are lined up (e.g., via the fitting engine  193  executing on computing system  185 ) using the “fat” or “thick” portions of the animal. More specifically, the scanned image is represented by rectangular clouds points (e.g., cloud point  901 ) and the selected model is represented by circular cloud points (e.g., cloud point  902 ). The points are plotted in three-dimensions along an x-axis  903 , a y-axis  904  and a z-axis  905  of graph  900 . 
         [0128]    In  FIG. 3H , the half-line of the scanned image (e.g., image  111  or  410 ) and the model (e.g., model  192   a  or  500 ) are compared to each other (e.g., via fitting engine  193  executing on computing device  185 ) with a chart  940  including an x-axis  950  and a z-axis  955 .  FIG. 3H  also happens to show that the animal is bent slightly in this example. The dotted line  905  is the half-line of the fitting model. The black line  906  is the half-line of the scanned image. A first set of points  910  and a second set of points  920  are selected points used in calculating the relative degree of “fatness” (or “thickness” or “depth”) at four different positions of the half-line  906 . In other embodiments, a lesser or greater number of points can be used to make the comparison. 
         [0129]    The depth of the animal can be fine tuned by the ratio (or “differential adjustment parameter”) D, which is calculated by comparing how much thicker the scan is to the “fitting model” (or vice versa) by looking at whether one or more points at the top of the scan is/are behind or in front of the half-line  905  of the fitting model. For example, if the scan points at the top of the scanned image are behind the half-line  905  (i.e., to the left of the dotted line  905 ) of the fitting model, then it means the scanned image is thicker (i.e., has a greater depth) than the fitting model and the final weight needs to be accordingly adjusted.  FIG. 3H  shows “depth of left bin” and “depth of right bin” (or points at top of the scans) behind the half-line  906  of the scanned image, and are therefore positive. Therefore, the weight of the animal can then be properly adjusted. 
         [0130]    Fine-Tune Rump-&gt;Ratio-R (and/or Other Body Parts) 
         [0131]    Individual sections (e.g., rump, shoulder, back, head, leg(s) or other body part) of the animal can similarly be compared and further refinements can be made. By doing so, adjustments can be made to the determined weight based on individual body parts. 
         [0132]      FIG. 3I  shows an exemplary comparison of a size of an animal&#39;s rump region in a scanned image (e.g., image  111  or  410 ) and a selected model (e.g., model  192   a ) according to one implementation of the invention. More specifically, the regions of the scanned image are represented by rectangular clouds points (e.g., cloud point  1001 ) and the regions of the selected model are represented by circular cloud points (e.g., cloud point  1002 ). The points are plotted in three-dimensions along an x-axis  1010 , a y-axis  1015  and a z-axis  1020  of graph  1000 . 
         [0133]    As shown, the region of interest is chosen just around the rump area. This ratio (or “differential adjustment parameter”) can be used to further adjust the weight of the animal. This is important, for example, since the rump can account for 30 to 40% of the weight of the animal. Although not shown here, many more fine-tuning steps can similarly be added by defining additional regions of interest. 
         [0134]    In the illustrated embodiment, other parts of the body (e.g., shoulder, back, head, leg(s)) can be adjusted for using a similar process as the one described above with respect to the rump. 
         [0135]    Returning back to  FIG. 2 , in Step  260 , an estimated weight is determined (e.g., by fitting engine  193  executing on computing system  185 ) for the animal (e.g., animal  50 ). Generally, the weight is determined by adjusting the known weight of the selected model (e.g.,  192   a ) based upon the one or more differential adjustment parameters. More specifically, since the weight of the selected model is already known, and its relative size to the scanned image (e.g., image  111  or  410 ) is now known, the weight of the scanned animal can be derived by applying R 1  and all the “fine-tuning” adjustment ratios in horizontal (L), vertical (H), and/or depth (D) directions as well as any adjustment ratios for each individual body parts. 
         [0136]    The following is an example of an algorithm for determining the weight from a selected model (e.g., model  192   a  or  500 ). 
         [0137]    W 1 =Weight (“Fitting Model”)
       1) Initial adjustment—The weight difference due to the R 1  ratio can be calculated using the following formulae. This is derived from a general formula for calculating volume of a cylinder with slight change to accommodate good results based on experimentation. Similar type of an equation could be used to approximate the difference in weight as well.       
 
         [0000]        W 2= W 1*(( R 1* R 1)+( R 1−1))* C -coeff
 
         [0000]    where C-coeff is the coefficient for amount of change different for different animals and breed.
       2) Horizontal fine-tune adjustment:       
 
         [0000]        W 3= W 2*(1+(Ratio− L/R 1)* C -coeff- H ))
 
         [0000]    where C-coeff-H is the coefficient for adjusting the weight change due to the horizontal difference.
       3) Vertical fine-tune adjustment:       
 
         [0000]        W 4= W 3*(1+(Ratio− H/R 1)* C -coeff- V ))
 
         [0000]    where C-coeff-V is the coefficient for adjusting the weight change due to the vertical difference.
       4) Depth fine-tune adjustment:       
 
         [0000]        W 5= W 4*(1+(Ratio− D/R 1)* C -coeff- D ))
 
         [0000]    where C-coeff-D is the coefficient for adjusting the weight change due to the depth difference.
       5) Stretch Adjustment       
 
         [0000]        W 6= W 5+(1+half-line curvature* C -coeff-Curve)) 
         [0000]    where C-coeff-Curve is the coefficient for adjusting the weight change due to the ‘bending’ of the animal.
       6) Overall adjustment:       
 
         [0000]        W 6= W 5* C -adjust-total 
         [0000]    where C-adjust-total is the overall coefficient adjustment to adjust for breed, region or other such factors. 
         [0144]    Those skilled in the art will appreciate that the C-coefficients above can be determined by known regression techniques utilizing training data from a plurality of image scans, or otherwise. 
         [0145]    Gender Recognition 
         [0146]    The gender of an animal (e.g., animal  50 ) can be determined based upon the process described above. More specifically, for example, a region of an image representing the genitalia of the animal can be compared to a region of a fitting model (e.g., model  192   a ) having a known gender (i.e., male or female). If the regions match, or more particularly, if the corresponding cloud points match (e.g., as determined via ICP or otherwise), then the animal has the same gender of as the selected model. Alternatively, if the regions do not match (e.g., as determined by ICP or otherwise), then the animal is presumed to have the opposite gender as the selected model. 
         [0147]    Graphical User Interface (GUI) 
         [0148]      FIG. 4  depicts an exemplary process  1100  for displaying animal metrics (e.g., weight) with a graphical user interface (GUI) according to one implementation of the invention. Those skilled in the art will appreciate this is meant for example purposes only, and in other embodiments the metrics can be displayed otherwise. 
         [0149]    In step  1105 , a daily weight is determined (e.g., via process  200 ) for one or more animals (e.g., animal  50 ). For example, when the animal goes into a chute (e.g., chute  101 ) to drink water, a scan (e.g., image  111 ) of the animal is captured by an imaging system (e.g., imaging system  110 ). The scan is then checked to make sure it is of good quality. This scan is then processed by a weight algorithm (e.g., process  200 ) to produce a “scan weight” which is a weight calculated for that scan. The scan weight sometimes has a value of “4,” which means that the scan was of poor quality so that a valid weight could not be calculated. 
         [0150]    In any given day, the animal can come into drink water multiple times, and therefore can be scanned multiple times and have multiple estimated weights determined. In order to get a more accurate daily weight, the multiple scan weights can be average within a given day to generate a daily weight which represents the best estimate of the weight for that particular date. An exemplary formula for calculating daily weight can be: 
         [0000]      Daily weight=Sum(Scan weights of the same day)/ N,    
         [0151]    where N is the number of scans with valid weights 
         [0152]    Unfortunately, the animal does not always come into drink water every day. In addition, the weight from a single scan is not necessarily accurate since the animal can stretch and bend sideways at any given time. Therefore, these errors are averaged-out over the course of multiple scans. However, the animal can grow (e.g., around 2 to 3 pounds per day) which means weights are typically averaged over a single day, although not over several days, due to their rapid growth. 
         [0153]    As shown by way of example in  FIG. 5 , daily weight  1205  versus date  1210  can be charted on an x-y axis  1215 ,  1220  with weights on the y-axis  1220  and date on the x-axis  1215 . In this exemplary  FIG. 5 , the 15 th  day  1230  is the “current” day (or “today”). A best-fit line between these points  1240 - 1247  can be created. Using this best fit line, the weight of the animal can be “interpolated” in any given date, even into the future. This method of interpolating the weight can be deemed to be more accurate, since it uses many data points in order to produce the weight estimate for the current day. This interpolated weight is the “WEIGHT” of the animal for the current day. 
         [0154]    In the illustrated embodiment, to get a “valid” WEIGHT, at least five daily weights must be acquired over a fifteen-day period. In other words, in order to calculate the weight of the animal, we take data from the current day going back fifteen days. If there is less than five “daily weights” within these fifteen days, the current day&#39;s WEIGHT is invalid (e.g., having a value of “−1”). If there are at least five daily weights over these fifteen days, then the WEIGHT is valid and can, for example, have a weight anywhere between forty and three-hundred pounds. 
         [0155]    Although at least five daily weights must be acquired over a fifteen-day period in the illustrated embodiment to get a “valid” WEIGHT, in other embodiments it can be otherwise, e.g., a greater or lesser number of required weights (e.g., at least seven daily weights, at least 3 daily weights, etc.) and/or over a greater or lesser period of time (e.g., over a forty-five day period, over a ten-day period, etc.). 
         [0156]    In step  1110 , one or metrics are determined based upon individual daily weights. For example, Average Daily Gain (or “ADG”) represents how fast the animal is growing in pounds per day. In the illustrated embodiment, the ADG is the slope of the best fit line, and can be the same line that is used for interpolating the WEIGHT above. 
         [0157]    In the illustrated embodiment, individual animals can be associated with one or more groups and/or subgroups. For example, an animal can be associated with a group (e.g., a “farm”), and the groups can have one or more associated sub-groups (e.g., a “barn”). Additionally, the sub-groups (e.g., a “barn”) can have additional associated sub-groups (e.g., a “pen”). In the illustrated embodiment, the following metrics are calculated based on the daily weights of step  1105 , although other embodiments can calculate a lesser or greater number of such metrics. 
         [0158]    Average Pen Weight (APW) 
         [0159]    In the illustrated embodiment, the Average Pen Weight can be an average of all the animals within a pen calculated by adding the weights of all the animals in a pen and dividing it by the number of animals in that pen with valid weights. 
         [0160]    In the illustrated embodiment, if an animal&#39;s weight is invalid, it is not included in the calculation of APW, although in other embodiments it can be otherwise. 
         [0161]    If there are no valid weights in a pen, the APW value for that pen can be displayed as “---”. 
         [0162]    Average Barn Weight (ABW) 
         [0163]    In the illustrated embodiment, the Average Barn Weight is the average of all the animals within a barn calculated by adding the weights of all the animals in a barn and dividing it by the number of animals in that barn with valid weights, although in other embodiments, it may be calculated otherwise. 
         [0164]    In the illustrated embodiment, if an animal&#39;s weight is invalid, it is not included in the calculation of ABW, although in other embodiments it can be otherwise. 
         [0165]    Note that ABW is not the same as calculating the average of all the “Average Pen Weights” since the number of animals in each pen will vary. 
         [0166]    If there are no valid weights in a barn, the ABW value for that barn can be displayed as “---”. 
         [0167]    Animal Daily Weight Gain (ADG) 
         [0168]    In the illustrated embodiment, the Animal Daily Weight Gain is the average gain of “Animal Weight” over a 14-day period using a best-fit line method over that period, although in other embodiments it can be calculated otherwise. 
         [0169]    In the illustrated embodiment, if an animal&#39;s weight is invalid, it is not included in the calculation of ADG, although in other embodiments it can be otherwise. 
         [0170]    In the illustrated embodiment, an animal&#39;s weight needs to be valid on at least different 10 days out of the 14-day period in order for ADG to be considered valid, although other embodiments may utilize different requirements. 
         [0171]    If ADG is invalid, then the value for ADG can be displayed as “--.-”. 
         [0172]    Pen Daily Weight Gain (PADG) 
         [0173]    In the illustrated embodiment, the Pen Daily Weight Gain is the average gain for all the animals within a pen, which can be calculated by adding all the valid ADG (Average Daily Gain) values of all animals in a pen and dividing it by the number of animals with valid ADG values in that pen. 
         [0174]    In the illustrated embodiment, if an animal&#39;s ADG value is invalid, it is not included in the calculation of PADG, although in other embodiments it can be otherwise. 
         [0175]    If there are no valid ADG values in a pen, the PADG value for that pen can be displayed as “--.-”. 
         [0176]    Barn Daily Weight Gain (BADG) 
         [0177]    In the illustrated embodiment, the Barn Daily Weight Gain is the average gain for all the animals within a barn, which can be calculated by adding all the valid ADG (Average Daily Gain) values of all animals in a barn and dividing it by the number of animals with valid ADG values in that barn. 
         [0178]    In the illustrated embodiment, if an animal&#39;s ADG value is invalid, it is not included in the calculation of BADG, although in other embodiments it can be otherwise. 
         [0179]    If there are no valid ADG values in a barn, the BADG value for that barn can be displayed as “--.-”. 
         [0180]    Note that BADG is not the same as calculating the average of all the “Pen Daily Weight Gains” since the number of animals in each pen will vary. 
         [0181]    In step  1120 , the one or more metrics (e.g., average daily weight, ADG, BADG, etc.) are stored in a data store (e.g., data store  191 ) for retrieval and display to a user (e.g., using remote device  195 ) via a graphical user interface (e.g., GUI  196 ) window, as shown in step  1130 . In some embodiments, the GUI can be refined in response to user input (step  1140 ), e.g., as described in  FIGS. 6-9  below or otherwise. 
         [0182]    Exemplary GUI Displays 
         [0183]      FIGS. 6-13  show exemplary GUI displays according to one implementation of the invention. 
         [0184]      FIG. 6  shows a GUI display screen according to one implementation of the invention, with a GUI display window  1301 . After a user (e.g., using remote device  195 ) signs in to the system, a welcome screen can be presented. The user can then access the heart of the GUI by clicking on the Report link which can bring up the following screen  1300  which can show, in a display window  1301 , the farm name  1305 , average weight  1310  of all animals in the farm  1305 , and the average daily gain  1315  of all animals in the farm  1305 , a date the report was updated  1320 , and a description of the  1325  (e.g., when it was created, how many barns are in the farm, etc.). The user can click on the triangle  1306  located to the left of the farm name  1305  to show an expanded list of the barns within that farm, e.g., as shown in  FIG. 7 . 
         [0185]      FIG. 7  depicts an exemplary GUI display  1400 , according to one implementation of the invention, showing in the same display window  1301 , an average weight  1410 ,  1411  of all animals in each barn  1420 ,  1421  and the average daily gain  1430 ,  1431  of all animals in each barn  1420 ,  1421 . The user can also sort the order in which data is displayed by clicking one of the table headers such as Barn Name  1440 , Ave Weight  1441 , or ADG  1442 . The user can click on the triangle  1440 ,  1441  to the left of any barn name  1420 ,  1421  to show an expanded list of pens within that barn, e.g., as shown in  FIG. 8 . 
         [0186]      FIG. 8  depicts an exemplary display  1500  according to one implementation of the invention, showing in the same display window  1301 , an average weight  1510  of all animals in each pen  1520  and the average daily gain  1530  of all animals in each pen  1520 . The user can also sort the order in which data is displayed by clicking one of the table headers such as Pen Name, Ave Weight, or ADG. The user can click on the triangle (e.g., triangle  1521 ) to the left of any pen name (e.g., Pen Name  1520  “10000001”) to show an expanded list of animals (identified by their RFID numbers) within that pen  1520 , as shown in  FIG. 9 . 
         [0187]      FIG. 9  depicts an exemplary display  1600  according to one implementation of the invention, showing in the same display window  1301 , a list of all animals with valid weights  1610  and average daily gain values  1620  of all animals in a pen  1625 . The user can also sort the order in which data is displayed by clicking one of the table headers such as RFID, Weight, or ADG. 
         [0188]    The user can compress any expanded list by clicking on the arrow (e.g., arrow  1630 ) to the left of the name of an entity (e.g., pen, barn, or farm) with an expanded list. In the illustrated embodiment, the lists can be expanded and compressed in a single display window (e.g., screen  1301 ), such as a single web page, or a single element within a web page, in order to allow for quick and easy user navigation, although other embodiments may use multiple screens. 
         [0189]      FIG. 10  depicts an exemplary weight range table displayed in a GUI  1700  showing the number of animals within particular weight ranges according to one implementation of the invention. In the illustrated embodiment, users (e.g., using remote device  195 ) can define specific weight ranges (e.g., 155 to 199 lbs.) to show the number of animals in all pre-defined weight ranges across all pens and barns. This can provide the user with a good overview of weight distributions for the entire operation. In the illustrated embodiment, if an animal&#39;s weight is invalid, it is not included in the data displayed in the weight range table, although in other embodiments it can be otherwise. 
         [0190]    For example, to set weight ranges, the user can click on the “Set Weight-Ranges” link  1720  in the upper right hand corner, which causes to GUI to display a separate display  1800 , as shown in exemplary  FIG. 11 . The GUI  1800  allows, for example, a user to edit or delete an existing weight range or set up a new weight range according to one implementation of the invention. In the illustrated embodiment, for example, a user can create a new weight range by clicking on “Create New Weight-Range” link  1810 . 
         [0191]      FIG. 12  shows an exemplary GUI display  1900 , according to one embodiment of the invention, wherein a user can enter desired minimum and maximum weight values. In the illustrated embodiment, for example, entering a minimum value in field  1905  and a maximum value in field  1910 , and selecting the “Create” link  1920 , can create a new weight range. 
         [0192]    Paint Sprayer 
         [0193]    In the illustrated embodiment, each pen can be equipped with a paint sprayer (e.g., marking device  160 ), which can be used to paint animals (e.g., animal  50 ) that are within a user-defined weight range. The paint sprayer can be positioned near the water spout where animals go to drink water. If an animal accesses the water spout, the system (e.g., control system  101 ) can determine if that animal&#39;s current weight is within a pre-defined weight range for being sprayed. 
         [0194]    The weight ranges for the paint sprayer settings can be set on a per-farm basis. All pens within a farm can have their paint sprayer range settings set to operate across the same weight range. 
         [0195]    The weight range settings for paint sprayer control can be changed by the user via the GUI at any time, e.g., as shown in highlighted portion  2010  of the GUI display  2000  in  FIG. 13 . 
         [0196]    Forecasting Animal Weight 
         [0197]      FIG. 14  depicts an exemplary GUI  3000  displaying a weight range table according to one implementation of the invention. 
         [0198]    As discussed above, users (e.g., using remote device  195 ) can interact with the GUI (e.g., GUI  196 ) to display livestock metrics. In this example, the GUI  3000  displays a number of animals (e.g., 0, 1, 2, 3, etc.) within certain weight arranges, organized by pen, barn and farm, although in other embodiments, they may be organized by different groups and/or sub-groups. 
         [0199]    The GUI  3000  can additionally include a “Forecast” link  3010 . A user can access (e.g., display) predicted livestock metrics (e.g., weight) by using this link  3010 , although other embodiments may provide other features for generating and/or displaying such predicted metrics. For example, forecasted metrics may be automatically generated and/or displayed (e.g., by control system  185 ), or they may generated and/or displayed in response to other types of user input. 
         [0200]      FIG. 15  depicts an exemplary GUI  3100  displaying a forecast weight range table according to one implementation of the invention. This can be helpful, for example, because it can allow a manager (or other user) to see what the weight range table (e.g., the weight range table of GUI  3000 ) would look like in the future (e.g., a week later, two weeks later, etc.). Such information can help with a variety of management activities. 
         [0201]    In the illustrated embodiment, a future weight can be predicted (or “forecasted”) for one or more animals. For example, a forecasted weight for an animal can be determined based upon their growth rate (e.g., ADG). More specifically, in the illustrated embodiment, the forecasted weight is determined by multiplying an animal&#39;s ADG (e.g., the current day&#39;s ADG or most recent ADG) by the number of days ahead to predict (e.g., 7-days, 14-days, 21-days, 28-days, etc.), and adding the result to the animals current daily weight (e.g., average daily weight, “valid” daily weight, “interpolated” daily weight, etc.). The number of days ahead to predict can be customizable (e.g., by a user, systems administrator, etc.). In other embodiments, other methods can be used to determine the forecasted weight, such as methods that take into account multiple ADGs, the age or gender of the animal, or other characteristics and/or metrics described above. 
         [0202]    In this example, shown in  FIG. 15 , the GUI  3100  displays forecasted weight ranges (e.g., 0-170 lbs.) for a selected number of days in the future (e.g., 14-days). The user can select the number of days from several options in this example, including 7-days, 21-days, and 28-days, although these are shown for exemplary purposes only, and other embodiments may allow for a greater or lesser number of options, or a greater or lesser number of days in the future the system can forecast. 
         [0203]    Continuing this example, the GUI  3100  displays a number of animals (e.g., 0, 1, 2, etc.) that are forecasted to be within a certain weight range (e.g., 0-170 lbs, 171-224 lbs, etc.) for the specified amount of days (e.g., 14-days) from the current day. More specifically, the display is organized by pen, and the GUI  3100  displays the number of animals within each pen that are predicted to be within a particular weight range. In other displays, the display may be organized by other groups (e.g., a farm) or sub-groups (e.g., a barn). 
         [0204]    It will be appreciated that any of the following methods can be used in connection with calculating the values described above (e.g., ADG, forecasted weight, etc.):
       (i) best fit line method; (ii) best fit curve method; (iii) second order curve method; (iv) Random Sample Consensus (RANSAC) method; (v) ICP; (vi) or process  200 , described above.       
 
         [0206]    Animal Marking Process 
         [0207]    In animal farming operations, when animals (e.g., hogs, cows, etc.) reach a certain weight range, or other metric range (e.g., volume, mass, etc.), they can be ‘marked’ with paint as an indication that they are ready to be shipped. These markings can be applied by the farmer who visually estimates the size of each animal. With the advent of the ClicRweight system, e.g., as described above, a 3D camera can be used to accurately measure an animal&#39;s weight using computer vision technology. Individual animals can be uniquely identified using RFID tags or some other type of labeling system. The ClicRweight system can automatically trigger a paint sprayer when an animal hits a particular target weight. The process described below is an exemplary method for using Google Glass (or similar) technology to eliminate the need for the paint sprayer. 
         [0208]    Although animal weights are discussed here, it will be appreciated that other animal metrics (e.g., as described above) can also be used instead of, or in addition to, weight. Furthermore, the term “animal” can refer to humans as well as to hogs, cows, and/or other wild or domestic animals. 
         [0209]    Exemplary Problem: The paint sprayer can require the following components:
       USB connection from the computer   controller module to convert the USB signal to between 12V and 24V with enough current to drive the paint sprayer motor   sprayer   paint box   paint that needs to be periodically replenished       
 
         [0215]    These components can increase costs and reduce reliability. 
         [0216]    Exemplary Solution: Google Glass, e.g., as shown below, or other similar device, can be used to identify the animals using OCR/computer vision technology to virtually ‘color,’ or otherwise mark (e.g., with symbols, shapes, numbers, etc.) the animals visually for the person wearing the Google Glasses. This can allow a farmer to walk around the barn and ‘look’ to see how each animal is marked (or ‘colored’); for example, animals that are ready to be shipped can appear ‘green’, while animals that are close to their target ship weight can appear ‘yellow’, and animals that are not ready to ship can appear ‘red’. This visual effect can be achieved using a technology known as ‘Augmented Reality’. Such a system can allow a farmer to easily identify animals ready for shipment without having to employ a complicated paint sprayer system. 

 
         [0217]    Google Glass: Google Glass is a wearable computer with a head-mounted display and built-in camera developed by Google. Google Glass provides a hands-free way of displaying information to the user. It can interact with the user via voice commands or by touch. The internet connection is provided typically via a smartphone which is linked to Google Glass, although Google Glass may also directly connect via wifi. Processing can be done on Google Glass itself or it can interact with one or more other devices (e.g., a laptop, desktop, server, mobile device) executing one or more applications (e.g., mobile application, PC application, etc.). Although Google Glass is discussed here, other similar devices can also be used. 
         [0218]    Exemplary System: Animal weights can be pre-processed by the ClicRweight system and stored in a database (e.g., cloud-based or otherwise). A unique identification system (e.g., RFID tags) allows each weight entry to be uniquely mapped to a particular animal. When the image of an animal is processed, the weight associated with the identified animal can be extracted from the database via the internet or other network. The ‘color’ (or other marking) associated with the weight value can then be displayed on Google Glass (or similar device). All this can be done in real-time. 

 
         [0219]    Exemplary Method: 
         [0220]    The system can use a 3D camera to capture a 3D scan and associated JPG image, and can also process the weights (or other metrics). Using OCR or other computer vision methods, the system can identify the animal and send the weight information along with the ID to a central database that can reside in the cloud. Hence, the central database can contain the weights of all animals that are associated with this farm. 
         [0221]    When the user walks in the barn, the smartphone application can detect the user&#39;s location and can automatically register the user with a particular barn. In some embodiments, the user&#39;s location can be determined with GPS. Once the user is located in the barn, the application can download from the central database all of the weights of the animals within that barn. Within this barn, there can be a large number of animals (e.g., 1,000). When the user ‘looks’ at the animal, which can allow the camera to capture the image, the image can then be transferred to the smartphone or tablet. 
         [0222]    The smartphone can then process the animal&#39;s image to identify the animal and look up the weight already downloaded from the database. In other embodiments, the system can processes the animal&#39;s image to identify the animal and look up the weight directly in the central database. Either method can be performed in real-time. 
         [0223]    The weight information can then be transmitted to Google Glass (or similar device). The application on Google Glass can use this weight information along with a pre-programmed shipping weight to ‘paint’ the animal with different colors using an augmented reality application. The user, based on this color (green, yellow, red, for instance), can make shipping-related decisions (e.g., ship the animal, not ship the animal, etc.). In some embodiments, the shipping-related decisions can be uploaded to the central database or other location. 
         [0224]    System Hardware and Software 
         [0225]    The invention can be implemented in a compact, handheld imaging device, or in a computing system remote from an imaging device. The invention can be implemented in a closed-ended chute including a control wall having an animal feeder, an animal presence indicator and an imaging device having a field-of-view substantially unobstructed by walls of the chute. The implementation can include a control system communicatively connected to the animal presence indicator and the imaging device, and configured to control the imaging device based upon information communicated by the animal presence indicator. 
         [0226]    The above-described techniques can also be implemented in digital and/or analog electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The implementation can be as a computer program product, i.e., a computer program tangibly embodied in a machine-readable storage device, for execution by, or to control the operation of, a data processing apparatus, e.g., a programmable processor, a computer, and/or multiple computers. A computer program can be written in any form of computer or programming language, including source code, compiled code, interpreted code and/or machine code, and the computer program can be deployed in any form, including as a stand-alone program or as a subroutine, element, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one or more sites. 
         [0227]    Method steps can be performed by one or more processors executing a computer program to perform functions of the technology by operating on input data and/or generating output data. Method steps can also be performed by, and an apparatus can be implemented as, special purpose logic circuitry, e.g., a FPGA (field programmable gate array), a FPAA (field-programmable analog array), a CPLD (complex programmable logic device), a PSoC (Programmable System-on-Chip), ASIP (application-specific instruction-set processor), or an ASIC (application-specific integrated circuit). Subroutines can refer to portions of the computer program and/or the processor/special circuitry that implement one or more functions. 
         [0228]    Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital or analog computer. Generally, a processor receives instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and/or data. Memory devices, such as a cache, can be used to temporarily store data. Memory devices can also be used for long term data storage. Generally, a computer also includes, or is operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. A computer can also be operatively coupled to a communications network in order to receive instructions and/or data from the network and/or to transfer instructions and/or data to the network. Computer-readable storage devices suitable for embodying computer program instructions and data include all forms of volatile and non-volatile memory, including by way of example semiconductor memory devices, e.g., DRAM, SRAM, EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and optical disks, e.g., CD, DVD, HD-DVD, and Blu-ray disks. The processor and the memory can be supplemented by and/or incorporated in special purpose logic circuitry. 
         [0229]    To provide for interaction with a user, the above described techniques can be implemented on a computer in communication with a display device, e.g., a CRT (cathode ray tube), plasma, or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse, a trackball, a touchpad, or a motion sensor, by which the user can provide input to the computer (e.g., interact with a user interface element). Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, and/or tactile input. 
         [0230]    The above described techniques can be implemented in a distributed computing system that includes a back-end component. The back-end component can, for example, be a data server, a middleware component, and/or an application server. The above described techniques can be implemented in a distributed computing system that includes a front-end component. The front-end component can, for example, be a client computer having a graphical user interface, a Web browser through which a user can interact with an example implementation, and/or other graphical user interfaces for a transmitting device. The above described techniques can be implemented in a distributed computing system that includes any combination of such back-end, middleware, or front-end components. 
         [0231]    The computing system can include clients and servers. A client and a server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
         [0232]    The components of the computing system can be interconnected by any form or medium of digital or analog data communication (e.g., a communication network). Examples of communication networks include circuit-based and packet-based networks. Packet-based networks can include, for example, the Internet, a carrier internet protocol (IP) network (e.g., local area network (LAN), wide area network (WAN), campus area network (CAN), metropolitan area network (MAN), home area network (HAN)), a private IP network, an IP private branch exchange (IPBX), a wireless network (e.g., radio access network (RAN), 802.11 network, 802.16 network, general packet radio service (GPRS) network, HiperLAN), and/or other packet-based networks. Circuit-based networks can include, for example, the public switched telephone network (PSTN), a private branch exchange (PBX), a wireless network (e.g., RAN, bluetooth, code-division multiple access (CDMA) network, time division multiple access (TDMA) network, global system for mobile communications (GSM) network), and/or other circuit-based networks. 
         [0233]    Devices of the computing system and/or computing devices can include, for example, a computer, a computer with a browser device, a telephone, an IP phone, a mobile device (e.g., cellular phone, personal digital assistant (PDA) device, laptop computer, electronic mail device), a server, a rack with one or more processing cards, special purpose circuitry, and/or other communication devices. The browser device includes, for example, a computer (e.g., desktop computer, laptop computer) with a world wide web browser (e.g., Microsoft® Internet Explorer® available from Microsoft Corporation, Mozilla® Firefox available from Mozilla Corporation). A mobile computing device includes, for example, a Blackberry®. IP phones include, for example, a Cisco® Unified IP Phone 7985G available from Cisco System, Inc, and/or a Cisco® Unified Wireless Phone 7920 available from Cisco System, Inc. 
         [0234]    One skilled in the art will realize the technology can be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The foregoing embodiments are therefore to be considered in all respects illustrative rather than limiting of the technology described herein. All changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. The steps of the technology can be performed in a different order and still achieve desirable results. 
         [0235]    It will be appreciated that the illustrated embodiment and those otherwise discussed herein are merely examples of the technology and that other embodiments, incorporating changes thereto, fall within the scope of the invention.