Patent Publication Number: US-10309908-B2

Title: Light field illumination container inspection system

Description:
BACKGROUND 
     Production plants for manufacturing containers (such as beverage cans) can produce a very large number of containers, with sophisticated (multicolor) decoration thereon, in a relatively short amount of time. For instance, a conventional decorator in a container production plant can decorate 2,000 containers per minute. Container decorations have intrinsic value, as consumers tend to attach perceptions of quality to a product based upon the design on the container that holds the product. 
     Conventionally, there is a lack of robust inspection of exterior surfaces of containers at these container production plants. A known process for container inspection is tasking an operator at the plant with periodically pulling containers from a conveyor for visual inspection. For instance, every so often (e.g., every 15 minutes), the operator may be tasked with pulling a small number of containers from the conveyor and visually inspecting the containers to ensure that the exterior surfaces of the containers are free of readily apparent defects (e.g., to ensure that proper colors are applied to the exterior surfaces of the containers, to ensure that the exterior surfaces of the containers are free of smears, etc.). Using this conventional approach, thousands of defective containers may be manufactured prior to the operator noticing a defect on the exterior surface of one or more of the sampled containers. In practice, these (completed) containers must be scrapped, resulting in significant cost to the container manufacturer. 
     Recently, automated systems have been developed and deployed in container production plants, wherein such systems are configured, through automated visual inspection, to detect defects on exterior surfaces of containers. These systems include multiple cameras that are positioned to capture images of exterior surfaces of a container when the container passes through an inspection region. In such systems, the images are captured while a container is illuminated by way of dark field illumination. Images of the sidewall of the container taken under dark field illumination are well-suited at depicting spatial defects, three-dimensional defects (e.g., dents, scuffs, contamination, etc.), and subtle color shifts in opaque inks on the container. A computing system analyzes the images captured by the cameras to determine whether the exterior surface of the container includes a defect. Systems that incorporate dark field illumination, however, are unable to accurately correlate measured color in the images to offline measurement systems and standards. Further, these systems are generally incapable of detecting, on containers that have been decorated with ink of dark colors, scratches or unintentional voids in decorations (where, for some reason, ink was not applied where it should have been applied). 
     SUMMARY 
     The following is a brief summary of subject matter that is described in greater detail herein. This summary is not intended to be limiting as to the scope of the claims. 
     Described herein is a container inspection system that is configured to ascertain whether a container being transported on a conveyor includes a defect on an exterior surface of a sidewall of the container. The container inspection system can detect defects that may occur in a design or label on an exterior surface of the container, such as an improper color being printed on the exterior surface of the container (e.g., a color shade is incorrect), smearing, and so forth, such that the design or label does not appear as desired. The container inspection system can detect various defects on exterior surfaces of containers, including physical defects (e.g., scratches, dents, etc.) or voids in decorations (e.g., where a portion of a container should be covered by ink but instead is bare metal). 
     The container inspection system includes an inspection dome, wherein the container is located in the inspection dome when the container is inspected. The inspection dome limits an amount of light, external to the inspection dome, that can illuminate the container (and additionally prevents light used to illuminate the exterior surface of the container from exiting the inspection dome). Further, the container inspection system includes a light source that is configured to emit light within the inspection dome. In an example, the light source can include a light emitting diode (LED) or other suitable source of light. The light source diffusely emits light, resulting in a relatively uniform light field throughout the interior of the inspection dome (such that the container is illuminated by light field illumination, rather than dark field illumination). The container inspection system includes several cameras (positioned to surround the container under inspection) that are configured to simultaneously generate images of the exterior surface of the sidewall of the container while such surface is being illuminated in the inspection dome. More specifically, the light source is strobed, such that the aforementioned container surface is illuminated for a relatively short amount of time (e.g., on the order of tens of microseconds). The cameras capture respective images of the exterior surface of the sidewall of the container while such surface is being illuminated. 
     The container inspection system also includes a computing system that is in communication with the cameras, wherein the computing system is configured to, for each container passing through the inspection dome, receive images (generated by the cameras) of an external surface of a sidewall of the container. The computing system determines whether the exterior surface of the sidewall of the container includes a defect based upon the images. It can be ascertained that the inspection dome includes several apertures therein: two apertures for container transport (a first aperture where the container enters the inspection dome on the conveyor, and a second aperture where the container exits the inspection dome on the conveyor), and an aperture for each camera. Further, containers are close to one another on the conveyor. Hence, images of the exterior surface of the sidewall of the container may include reflections of the apertures, as well as reflections of adjacent containers on the conveyor. 
     In an example, several cameras simultaneously capture respective images of the exterior surface of the sidewall of the container while the container is in the inspection dome. When the container being inspected is cylindrical, the computing system can process each image, such that the portion of the exterior surface of the sidewall of the container captured in each image is “flattened”. Subsequently, the computing system can identify, in a first image of the exterior surface of the sidewall of the container (where the first image is captured by a first camera), a first region that includes reflections (e.g., from the apertures or adjacent containers on the conveyor). The computing system can replace the first region in the first image with a second region in a second image of the exterior surface of the sidewall of the container (where the second image is captured by a second camera), where the first region and the second region map to a same physical region of the exterior surface of the sidewall of the container. It can be ascertained that the second region of the second image will not depict the reflections found in the first region of the first image, since the first and second images are captured by cameras at different positions relative to the container. Moreover, these regions in the images can be identified before the images are captured based upon known geometry of the container and positions of the cameras relative to the container when the images are captured. 
     Responsive to replacing the first region of the first image with the second region from the second image, the first image becomes a reflection-free image of the exterior surface of the sidewall of the container. The computing system performs this process for each image captured by each camera, thereby creating several reflection-free images. The computing system can optionally stitch the reflection-free images together, thereby creating an image of the container as if the container were unwrapped (referred to as an unwrapped image). The computing system can then align the unwrapped image to a statistical model that represents an unwrapped container that is free of defects. 
     Thereafter, the computing system compares the unwrapped image with the statistical model. When the computing system compares the unwrapped image with the statistical model and identifies a sufficient dissimilarity therebetween, the computing system can output a signal that indicates that the container is defective. Conversely, when there is sufficient similarity between the unwrapped image and the statistical model, the computing system can output a signal that indicates that the container is non-defective. 
     The above summary presents a simplified summary in order to provide a basic understanding of some aspects of the systems and/or methods discussed herein. This summary is not an extensive overview of the systems and/or methods discussed herein. It is not intended to identify key/critical elements or to delineate the scope of such systems and/or methods. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic of an exemplary light field illumination container inspection system. 
         FIG. 2  is a top-down view of an exemplary light field illumination container inspection system. 
         FIG. 3  is another top-down view of an exemplary light field illumination container inspection system. 
         FIG. 4  depicts images of a container captured by a first camera and a second camera of a light field illumination container inspection system. 
         FIG. 5  depicts a band in a first image being replaced with a band from a second image. 
         FIG. 6  depicts a functional block diagram of a computing system of a light field illumination container inspection system. 
         FIG. 7  is a flow diagram illustrating an exemplary methodology for configuring a light field illumination container inspection system. 
         FIG. 8  is a flow diagram illustrating an exemplary methodology for operating a light field illumination container inspection system. 
         FIG. 9  is an exemplary computing device. 
     
    
    
     DETAILED DESCRIPTION 
     Various technologies pertaining to a container inspection system that incorporates light field illumination are now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. It may be evident, however, that such aspect(s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more aspects. Further, it is to be understood that functionality that is described as being carried out by certain system components may be performed by multiple components. Similarly, for instance, a component may be configured to perform functionality that is described as being carried out by multiple components. 
     Moreover, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from the context, the phrase “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, the phrase “X employs A or B” is satisfied by any of the following instances: X employs A; X employs B; or X employs both A and B. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from the context to be directed to a singular form. Further, as used herein, the term “exemplary” is intended to mean serving as an illustration or example of something, and is not intended to indicate a preference. 
     Further, as used herein, the terms “component” and “system” are intended to encompass instructions stored in computer-readable data storage that are configured to cause certain functionality to be performed when executed by a processor. The computer-executable instructions may include a routine, a function, or the like. It is also to be understood that a component or system may be localized on a single device or distributed across several devices. Further, as used herein, the term “exemplary” is intended to mean serving as an illustration or example of something, and is not intended to indicate a preference. 
     Described herein are features pertaining to identifying defects in a sidewall of a container, wherein an exterior surface of the sidewall of the container is at least somewhat reflective. Further, when reference is made to detecting defects in the sidewall of the container, such action is also intended to encompass detecting defects in labels applied to the sidewall of the container (such as a shrink-wrap label or paper label). In an example, the container may be decorated with ink and/or comprise a highly reflective material (such as bare metal). In other examples, the container glass, plastic, or paper containers, and/or have plastic or paper labels applied thereto. 
     The container inspection system described herein, on contrast to conventional container inspection systems, uses light field illumination to illuminate a container under inspection (where conventional container inspection systems employ dark field illumination to illuminate a container under inspection). With respect to light-field illumination, a specular surface appears white in an image captured by a camera, because some of the illumination reflects directly back towards the image sensor of the camera that captures the image. With respect to dark-field illumination, a specular surface appears black in an image captured by the camera, because the illumination is directed such that light reflects away from the image sensor of the camera that captures the image. Due at least partially to the use of light field illumination to illuminate containers, the container inspection system is well-suited to accurately correlate measured colors of inspected containers to offline color measure standards. Further, the container inspection system is configured to identify, for instance, scratches or (unintentional) voids in decoration on exterior surfaces of containers decorated with dark colors (blue, purple, etc.). 
     Summarily, the container inspection system includes multiple cameras that simultaneously capture images of an exterior surface of a sidewall of a container that is under inspection. The cameras are positioned such the cameras surround the container when the images are captured. The container inspection system further includes a computing system that is configured to identify, in each of the images, at least one region that is known to include reflections. Thus, in a first image captured by a first camera, the computing system identifies a region that is known to include reflections (this region can be known based upon geometries of the container inspection system, such as size and shape of the container, positions of the cameras relative to the container, detected distance between the container and an adjacent container on a conveyor, etc.). The computing system can replace the region in the first image with a region from a second image (captured by a second camera), where the region from the second image and the region from the first image map to the same physical region on the exterior surface of the sidewall of the container. Thus, the computing system effectively causes the first image to be free of reflections. Upon the computing system performing such processing (for each image captured by the cameras), the computing system can employ conventional image processing techniques to determine whether the container is defective. 
     With reference now to  FIG. 1 , an exemplary light field illumination container inspection system  100  is illustrated. For example, the system  100  can be configured to detect defects in metal containers (e.g., metal cans), plastic containers (e.g., bottles) and/or labels (paper or plastic) appended thereto. Further, the system  100  can be configured to detect defects in exterior surfaces of sidewalls of containers that comprise material (e.g., aluminum, steel, etc.) that reflects light in a specular manner. In other words, the system  100  is well-suited to detect defects in containers that are reflective. Additionally, the system  100  can be configured to detect defects in containers (e.g., metal cans) that have been decorated with ink, particularly translucent ink. Moreover, the system  100  can be configured to detect printing defects in labels applied to containers, wherein the labels are made of material that is somewhat reflective or painted with ink that is somewhat reflective. Further, while the containers depicted herein have a cylindrical sidewall, it is to be understood that the system can be configured to detect defects in sidewalls of varying shapes. 
     Also, the system  100  can be configured to detect defects in text or graphics printed on sidewalls of containers. The system  100  can also detect defects that may occur in a design or label on an exterior surface of the container, such as an improper color being printed on the exterior surface of the container (e.g., a color shade is incorrect), smearing, and so forth, such that the design or label does not appear as desired. For example, the system  100  can be configured to detect that a container has a design printed thereon that includes an insufficient amount of a color. Additionally, the system  100  can be configured to detect defects on exterior surfaces of containers, including physical defects (e.g., scratches, dents, etc.) or voids in decorations (e.g., on containers and/or labels that have been decorated with dark colors). 
     A conveyor  102  transports a plurality of containers  104 - 106  through an inspection dome  108 . The inspection dome  108  comprises an entry aperture  110  for the plurality of containers  104 - 106  to enter the inspection dome  108 . The inspection dome  108  further comprises an exit aperture  112  for the plurality of containers  104 - 106  to exit the inspection dome  108 . Additionally, the inspection dome  108  comprises a first color camera aperture  114  and a second color camera aperture  116 . The system  100  further comprises a first color camera  118  and a second color camera  120 , wherein the cameras  118  and  120  are positioned in the apertures  114  and  116 , respectively. Thus, the cameras  118  and  120  are positioned to capture images of the plurality of containers  104 - 106  as the plurality of containers  104 - 106  are transported through the inspection dome  108  by the conveyor  102 . Details regarding operation of the first color camera  118  and the second color camera  120  are set forth below. 
     The interior surface of the inspection dome  108  is formed of material (e.g., plastic) that prevents light, external to the inspection dome  108 , from illuminating containers when the containers are in the inspection dome  108 . Additionally, the inspection dome  108  prevents light from exiting the inspection dome  108 . Due to apertures in the inspection dome  108  (e.g., the entry aperture  110 , the first camera aperture  114 , etc.), a limited amount of light (that is external to the system  100 ) from the apertures  110 - 116  can illuminate the containers within the inspection dome  108 . Further, the interior surface of the inspection dome  108  can be reflective. For instance, the interior surface of the inspection dome  108  can be white (e.g., painted white or formed of a white plastic). 
     The system  100  further comprises a sensor  122  that outputs a signal that is indicative of when a container (e.g., the first container  106 ) has reached an inspection region in the inspection dome  108 . As will be described herein, the cameras  118  and  120  are configured to simultaneously capture images of the first container  106  when the first container  106  is in the inspection region. For example, and not by way of limitation, the sensor  122  may be a presence sensor that can detect when the first container  106  has passed a particular point (e.g., when the first container  106  has entered the inspection dome  108 ). Additionally or alternatively, the sensor  122  may be a rotary sensor that is configured to output data based upon movement of the conveyor  102 . The output data, therefore, is indicative of a position of the first container  106  relative to a previous position of the first container  106  on the conveyor  102  and, thus, the position of the first container  106  relative to the inspection region in the inspection dome  108 . 
     The system further comprises a computing system  124  that receives the signal output by the sensor  122 . The computing system  124  can receive the signal from the sensor  122  by way of a wireless or wireline connection. The system further comprises a light source  126  that is configured to cause a sidewall of the first container  106  to be illuminated when it is within the inspection region of the inspection dome  108 . The light source  126  can include an array of light emitting diodes (LEDs), wherein each LED emits white light. More particularly, the light source  126  diffusely emits light, resulting in a relatively uniform light field throughout the interior of the inspection dome  108  (such that the first container  106  is illuminated by way of light field illumination, rather than dark field illumination). By way of example, as the light source  126  diffusely emits light, the light reflects off the reflective interior wall of the inspection dome  108 , resulting in a relatively uniform light field throughout the inspection dome  108  (e.g., light is incident upon the exterior surface of the sidewall of the first container  106  at various angles due to the light being diffusely emitted from the light source  126  and “bouncing around” in the inspection dome  108 ). The computing system  124  controls the light source  126 , such that the light source  126  strobes responsive to the computing system  124  ascertaining that the first container  106  is in the inspection region in the inspection dome  108 . 
     Position and operation of the first color camera  118  and the second color camera  120  are now set forth in greater detail. The first camera  118  and the second camera  120  are placed external to the inspection dome  108  and directed radially inwards towards a central axis of the inspection dome  108  through the first camera aperture  114  and second camera aperture  116 , respectively. The first camera  118  and the second camera  120  are in communication with the computing system  124 . More specifically, the first camera  118  and the second camera  120  are controlled by the computing system  124 , such that the first camera  118  and second camera  120  (simultaneously) capture images of the exterior surface of the sidewall of the first container  106  when the central axis of the first container  106  is aligned with the central axis of the inspection dome  108 . Likewise, the computing system  124  causes the light source  126  to emit light when the central axis of the first container  106  is aligned with the central axis of the inspection dome  108 . 
     Since the light field is approximately uniform throughout the inspection dome  108  when the cameras  118  and  120  capture images of the exterior surface of the sidewall of the container  106 , the images are taken under light field illumination. Accordingly, (1) color in the images correlates to offline measurement systems and standards; and (2) scratches or voids in decorations on the first container  106  when the first container  106  is decorated using dark colors (blue, purple, etc.) are visible in the images. Because, however, the exterior surface of the sidewall of the first container  106  is at least partially reflective, unwanted reflections may appear in images captured by the cameras  118  and  120 . For example, an image of the exterior of the sidewall of the first container  106  captured by the camera  118  may include a reflection of the entry aperture  110  and a reflection of the camera aperture  114 . Additionally, while  FIG. 1  depicts the first container  106  as being the only container in the inspection dome  108 , there may be multiple containers in the inspection dome  108  when the first container  106  is in the inspection region, and containers adjacent to the first container  106  on the conveyor  102  may be very close to the first container  106 . Therefore, for instance, the image of the exterior sidewall of the first container  106  captured by the first camera  118  may include an unwanted reflection of the second container  105 . As will be described below, the computing system  124  can be configured to process images captured by the cameras  118  and  120  to remove regions of the images that include unwanted reflections and replace such regions with regions of images captured by other cameras, where the replacement regions do not include unwanted reflections (and where the replacement regions map to the same physical locations on the exterior sidewall of the first container  106  as the replaced regions). 
     With reference to  FIG. 2 , an overhead view of the exemplary light field illumination container inspection  100  is illustrated, wherein the first container  106  is in the inspection region of the inspection dome  108 . As depicted in  FIG. 2 , due to the relative positions of the first camera  118 , the first container  106 , and the first camera aperture  114 , a first reflection  202  of the first camera aperture  114  is captured in an image of the exterior sidewall of the first container  106  taken by the first camera  118 . Similarly, due to the relative positions of the second camera  120 , the first container  106 , and the first camera aperture  114 , a second reflection  204  of the first camera aperture  114  is captured in an image of the exterior sidewall of the first container  106  taken by the second camera  120 . While not illustrated here, it is to be understood that other reflections may be captured in images taken by the cameras  118  and  120  (e.g., the image taken by the first camera  118  may include a reflection of the entry aperture  110  and a reflection of the second camera aperture  116 , while the image taken by the second camera  120  may include a reflection of the exit aperture  112  and a reflection of the second camera aperture  116 ). As illustrated, the reflections  202  and  204 , in the different images, map to different physical locations on the exterior surface of the first container  106  (due to the cameras capturing the images of the first container  106  from different perspectives). Moreover, the reflections  202  and  204  appear at different locations in the images captured by the cameras  118  and  120 . It can also be ascertained that since relative positions between the cameras  118  and  120  and the apertures  110 - 116  (as well as adjacent containers, if any) are known, regions in images captured by the cameras  118 - 120  that include reflections can be known a priori. 
     Returning to  FIG. 1 , the computing system  124  receives the image captured by the first camera  118  and the image captured by the second camera  120  and determines whether the exterior surface of the sidewall of the container  106  includes a defect based upon the images. As will be described in greater detail below, the computing system  124  can process each image, such that the portion of the exterior surface of the sidewall of the first container  106  captured in each image is “flattened”. Subsequently, the computing system  124  can define, in a first image of the exterior surface of the sidewall of the first container  106  (captured by the first camera  118 ), a first region that includes the first reflection  202  (and other regions that include other reflections that may be captured in the first image). The computing system  124  can replace the first region in the first image with a second region in a second image of the exterior surface of the sidewall of the first container (captured by the second camera  120 ), where the first region and the second region map to a same physical region of the exterior surface of the sidewall of the first container  106 . It can be ascertained that the second region of the second image does not depict the reflections found in the first region of the first image, since the first and second images are captured by cameras at different positions relative to the first container  106 . This process can be repeated for every region in the first image that depict reflections, such that these regions are replaced with regions of other images that do not depict the reflections. Moreover, as noted above, these regions in the images can be identified before the images are captured based upon geometries of the container inspection system  100 . Finally, it can be ascertained that regions in the first image (captured by the first camera  118 ) can be replaced with regions of several images captured by several different cameras (including the second camera  120 ). 
     When the regions of the first image that include reflections have been replaced with regions of other images that do not include reflections, the first image becomes a reflection-free image of the sidewall of the first container  106 . The computing system  124  performs this process for each image captured by each camera, thereby creating several reflection-free images. The computing system  124  can optionally stitch the reflection-free images together, thereby creating an image of the first container  106  as if the first container  106  were unwrapped (referred to as an unwrapped image). The computing system  124  can then align the unwrapped image of the first container  106  with a statistical model that represents a container that is free of defects. Subsequently, conventional approaches can be employed to ascertain whether the sidewall of the first container  106  includes defects (where, as noted above, a defect may include an improper color hue, a bare metal defect, etc.). Additionally, as the first container  106  has been illuminated by way of light field illumination when the cameras  118  and  120  captured images of the first container, colorimetric analysis can be undertaken on the resultant unwrapped image. Therefore, in addition to identifying physical defects, the computing system  124  can identify color-related defects on the exterior sidewall of the first container  106 . When the computing system  124  determines that the container is defective, the computing system  124  can output a signal that causes, for instance, the first container  106  to be removed from the conveyor  102 , such that the first container  106  is prevented from being populated with content and further prevented from being made available to a consumer. 
     While the inspection system  100  is depicted as including the first camera  118 , the second camera  120 , and the single light source  126 , it is to be understood that the inspection system  100  may include multiple cameras (and respective camera apertures in the inspection dome  108 ) positioned around the inspection dome  108 . For example, the system  100  can include six cameras (and six respective camera apertures) directed radially inwards towards the center axis of the inspection dome  108 . The six cameras can be symmetrically arranged about the center axis. In an example, the six cameras each capture images of the first container  106  when the center axis of the first container  106  is aligned with the center axis of the inspection dome  108 , which is also when the first container  106  is illuminated by way of light field illumination. The captured images: 1) each depict portions of the sidewall of the first container  106 ; and 2) may include reflections of conveyor apertures or camera apertures. 
     Further, the inspection system  100  can be configured to perform both light field and dark field inspection of containers. For instance, the inspection system  100  can include a second light source (not shown), where the second light source can be configured to direct collimated light towards the exterior surface of the sidewall of the first container  106  (at a steep angle relative to the exterior surface of the sidewall of the first container  106 ). Thus, when the second light source is used to illuminate the exterior surface of the sidewall of the container  106 , the exterior surface of the sidewall of the container  106  is illuminated by way of dark field illumination. In such an embodiment, the cameras  118  and  120  can each capture two images: a first image when the exterior surface of the sidewall of the first container  106  is illuminated by way of light field illumination, and a second image when the exterior surface of the sidewall of the first container  106  is illuminated by way of dark field illumination. These images can be captured closely in time (within milliseconds), wherein the container  106  is in the inspection region of the inspection dome  108  for both images. In an alternative embodiment, separate sets of cameras can be used to capture images when the container is illuminated using light field illumination and dark field illumination, respectively (where, optionally, a set of cameras used with light field illumination includes more cameras than a set of cameras used with dark field illumination). As discussed above, images of the sidewall of the first container  106  taken under dark field illumination are well-suited for use when identifying spatial defects, three-dimensional defects (e.g., dents, scuffs, contamination, etc.), and subtle color shifts in opaque inks on the first container  106 . The computing system  124  can be further configured to identify these defects when the first container  106  is illuminated under dark field illumination using conventional approaches. 
     Referring briefly to  FIG. 3 , an overhead view of an exemplary container inspection system  300  is illustrated. The container inspection system  300  comprises six cameras  302 - 312 , arranged around the exterior of the inspection dome  108 , and configured to simultaneously capture images of the container  106  when the container is illuminated in the inspection dome  108 . While not shown, it is understood that the inspection dome  108  in this example includes entry and exit apertures, as well as six camera apertures. While examples set forth herein refer to the inspection system including two cameras, it has been found to be beneficial for the inspection system to have six cameras to allow for sufficient overlap between regions of the container  106  captured in images generated by adjacent cameras in the inspection system  300 . 
     Turning now to  FIG. 4 , exemplary images  402 - 404  captured, for instance, by the first camera  118  and the second camera  120  when the first container  106  is in the inspection region of the inspection dome  108  are illustrated. The first image  402 , captured by the first camera  118 , depicts a portion of the exterior surface of the sidewall of the first container  106 , where lines  406  and  408  depict boundaries of the first container  106  in the first image  402 . The first image  402  depicts the word “DESIGN” printed on the exterior surface of the sidewall of the first container  106 . The computing system  124  can identify a band  410  in the first image  402 , where the band  410  includes a reflection of the entry aperture  110  visible in the first image  402 . Similarly, the computing system  124  can identify bands  412  and  414  in the first image  402 , where the bands  412 - 414  include reflections of the first camera aperture  114  and the second camera aperture  116 , respectively, which are visible in the first image  402 . 
     The second image  404 , captured by the second camera  120 , depicts another portion of the exterior surface of the sidewall of the first container  106 , where lines  416 - 418  depict boundaries of the first container  106  in the second image  404 . The first image  402  includes a first region  420  that depicts a portion of the sidewall of the first container  106 , and the second image  404  includes a second region  422  that depicts the same portion of the sidewall of the first container  106 . Thus, the regions  420  and  422  map to a same physical region of the exterior sidewall of the first container  106  (due to the cameras  118  and  120  having overlapping fields of view). Due to geometries of the first camera  118  and the second camera  120  relative to the first container  106 , however, the reflections of the above-described apertures (and adjacent containers) appear at different locations on the exterior surface of the first container  106  in the images  402  and  404 . 
     The second image  404  includes a portion of the word “DESIGN”. Similar to what has been described above with respect to the first image  402 , the computing system  124  can identify bands  424 - 428  in the second image  404 , where the bands  424 - 428  comprise reflections of exit aperture  112  and other camera apertures. 
     With reference now to  FIG. 5 , replacement of the band  414  in the first image  402  with a band from the second image  404  is illustrated. With more specificity, the second image  404  can include a band  502 , wherein the band  502  is free of reflections. Further, the band  502  corresponds to the same physical region of the exterior sidewall of the first container  106  as the band  414  of the first image  402  (which depicts a reflection). The computing system  124  can be programmed to identify that the band  414  of the first image  402  can be replaced with the band  502  of the second image  404  based upon known geometries of the inspection system  100 , and can further be programmed to replace the band  414  in the first image  402  with the band  502  in the second image. The bands  410  and  412  in the first image can be replaced with bands from other images that are free of reflections, in a manner similar to what has been described above with respect to the band  502  from the second image  404  replacing the band  414  in the first image  402 . 
     While  FIG. 5  is set forth to illustrate replacement of a band in one image with a band in another image, other approaches are contemplated. As indicated previously, the computing system  124  can have knowledge of locations of the bands  410 - 414  in the first image  402 . The computing system  124 , instead of replacing the bands  410 - 414 , can filter the bands  410 - 414  from the first image  402  when determining whether there is a defect in the sidewall of the first container  106 . More specifically, the computing system  124  can receive the first image  402  from the first camera  118 , perform image processing on the first image  118  to “flatten” the first container  106  in the first image  402  (such that the first image appears as shown in  FIG. 4 ), and then align the first image  402  with a corresponding portion of a statistical model of a non-defective container. When comparing the first image  402  with the statistical model, the computing system  124  can filter the bands  410 - 414  from the first image  402 , such that pixels in the bands  410 - 414  are not considered by the computing system  124  when determining whether the exterior surface of the sidewall of the first container  106  is defective. This process can be repeated for each image captured by cameras of the container inspection system  100 ; thus, even though a band that includes a region of the exterior surface of the sidewall of the first container  106  is filtered in one of the images, due to the overlapping fields of view of the cameras of the system  100 , a portion of a second image that depicts a same region of the exterior sidewall of the first container  106  will be considered by the computing system  124  when determining whether the exterior sidewall of the first container  106  is defective. 
     Now referring to  FIG. 6 , a functional block diagram of the computing system  124  is illustrated. The computing system  124  includes a processor  602  and memory  604 . The memory has images  606  (generated by the cameras of the inspection system  100 ) loaded therein. For instance, the images  606  can comprise: 1) the image  402  captured by the first camera  118 ; and 2) the image  404  captured by the second camera  120 , wherein the images are of the exterior surface of the sidewall of the first container  106  when illuminated by way of light field illumination. 
     Further, the memory has a statistical model  608  of a defect-free (and unwrapped) container loaded therein. For instance, the statistical model can comprise a plurality of pixels, and each pixel can have a distribution assigned thereto, where the distribution is indicative of values of the pixel that correspond to a non-defective container. 
     In an embodiment, the computing system  124  generates the statistical model  608  based upon images of a number of non-defective containers. The system  100 , prior to inspecting containers, processes a preselected number of non-defective containers. With more specificity, the first camera  118  and the second camera  120  capture images of non-defective containers as such containers pass through the inspection dome  108  of the system  100 . The computing system  124  forms unwrapped images of these containers as described above, and aligns the unwrapped images with one another. During alignment, the computing system  124  can perform any suitable image processing technique to create a pixel-by-pixel correspondence between unwrapped images, where each pixel has a value assigned thereto, with the value being indicative of color of the pixel. Using these pixel values, the computing system  124  can form the statistical model  608  of a container that is to be inspected, where the statistical model includes, for instance, a distribution of values for each pixel. In another embodiment, the computing system  124  can receive a template spectrophotometer measurement of a graphic and/or text that is on the exterior surface of the sidewall of the first container  106 . The computing system  124  can generate the statistical model  608  based upon the spectrophotometer measurement. 
     The memory  604  additionally has a defect detection application  610  loaded therein. The defect detection application  610  is generally configured to ascertain whether the exterior surface of the sidewall of the first container  106  has a defect therein based upon the images  606  and the statistical model  608 . As noted previously, the defect detection application  610  can be configured to identify color defects, scratches, or voids in decorations (particularly decorations with ink of dark colors). The defect detection application  610  comprises a replacer component  612 , which is configured to process each image, such that the portion of the exterior sidewall of the container captured in each image is “flattened”. Further, the replacer component  612  is configured to identify, for each image in the images, bands that depict reflections in the exterior surface of the sidewall of the first container  106  and bands that do not depict reflections in the exterior surface of the sidewall of the first container  106  (but that can be used to replace “reflective” bands from other images). Since the geometries of the system  100  are static (with the possible exception of distance between containers being somewhat variable), the locations of the bands in images captured by cameras of the inspection system  100  are likewise static. Responsive to identifying these bands, the replacer component  612  replaces bands in images that depicts reflections with corresponding bands from other images (as illustrated in  FIG. 4 ), thereby forming several “reflection-free” images. 
     The defect detection application  610  additionally comprises a stitching/alignment component  614 . Responsive to the replacer component  612  generating the “reflection-free” images, the stitching/alignment component  614  is configured to stitch these images together, such that a reflection-free image of the unwrapped first container  106  is formed (which can be referred to as an unwrapped image). The stitching/alignment component  614  is further configured to align the unwrapped image with the statistical model  608 . While the description above indicates that the computing system  124  performs processing relating to reflection removal prior to processing relating to stitching, it is to be understood that the computing system  124  can alternatively be configured to stitch images together prior to replacing bands that include reflections with bands that are free of reflections. Moreover, as described above, stitching images is optional. 
     The defect detection application  610  additionally comprises a comparer component  616 . The comparer component  616  is configured to compare reflection-free images of a sidewall of a container (partial or complete) with the statistical model  608 . The comparer component  616  can compare the value of each pixel in the reflection-free image with the corresponding statistics in the statistical model, and can output signal as to whether the container  106  is defective based upon such comparison. For instance, if values of the pixels of the unwrapped image correspond to the statistics in the statistical model, the comparer component  616  can output an indication that the container  106  is not defective. Contrarily, if values of the pixels of the unwrapped image do not correspond to the statistics in the statistical model, the comparer component  616  can output a signal that the container  106  is defective. 
       FIGS. 7 and 8  depict exemplary methodologies pertaining to inspection of containers. While the methodologies are shown and described as being a series of acts that are performed in a sequence, it is to be understood and appreciated that the methodologies are not limited by the order of the sequence. For example, some acts can occur in a different order than what is described herein. In addition, an act can occur concurrently with another act. Further, in some instances, not all acts may be required to implement a methodology described herein. 
     Turning solely to  FIG. 7 , an exemplary methodology  700  for configuring a container inspection system that illuminates containers by way of light field illumination is illustrated. The exemplary methodology  700  starts at  702 , and at  704  a light source is positioned relative to an inspection dome, such that the light source causes an exterior surface of a sidewall of a container that passes through the inspection dome to be illuminated by light field illumination. 
     At  706 , a first camera is positioned relative to the inspection dome, such that a field of view of the first camera encompasses the exterior surface of the sidewall of the container when the external surface is illuminated by the light field illumination. The first camera is configured to capture an image of the exterior surface of the sidewall of the container when the container is being transported by a conveyor through the inspection dome of the container inspection system. 
     At  708 , a second camera is positioned relative to the inspection dome, such that a field of view of the second camera partially overlaps with the field of view of the first camera. The second camera is configured to capture an image of the exterior surface of the sidewall of the container when the container is being transported by a conveyor through the inspection dome of the container inspection system. 
     At  710 , the first camera and the second camera are configured to generate images of the exterior surface of the sidewall of the container when the exterior surface of the sidewall of the container is illuminated by light field illumination. 
     At  712 , a computing system is configured to: receive the images generated by the first and the second cameras; replace a band in the first image with a band from the second image; and generate an indication as to whether or not the container is defective based upon the band in the first image being replaced with the band from the second image. The methodology  700  completes at  714 . 
     Referring now to  FIG. 8 , an exemplary methodology  800  that facilitates operating a light field illumination container inspection system is illustrated. The methodology  800  starts at  802 , and at  804 , a light source is caused to emit light such that exterior surface of a sidewall of the container is illuminated by light field illumination. As described previously, this is performed while the container is being transported at a relatively high rate of speed along a conveyor. 
     At  806 , an image of the exterior surface of the sidewall of the container is captured by a camera while the exterior surface of the sidewall of the container is illuminated by light field illumination. At  808 , a band of the image that comprises reflections is filtered from the image, thereby creating a filtered image. At  810 , the container is labeled as being either defective or non-defective based upon the filtered image. The methodology  800  completes at  812 . 
     Referring now to  FIG. 9 , a high-level illustration of an exemplary computing device  900  that can be used in accordance with the systems and methodologies disclosed herein is illustrated. For instance, the computing device  900  may be used in a system that detects color defects in containers that have been decorated with ink. By way of another example, the computing device  900  can be used in a system that detects scratches or voids in decorations (particularly decorations with ink of dark colors) in containers. The computing device  900  includes at least one processor  902  that executes instructions that are stored in a memory  904 . The instructions may be, for instance, instructions for implementing functionality described as being carried out by one or more components discussed above or instructions for implementing one or more of the methods described above. The processor  902  may access the memory  904  by way of a system bus  906 . In addition to storing executable instructions, the memory  904  may also store images, defect signatures, etc. 
     The computing device  900  additionally includes a data store  908  that is accessible by the processor  902  by way of the system bus  906 . The data store  908  may include executable instructions, images, etc. The computing device  900  also includes an input interface  910  that allows external devices to communicate with the computing device  900 . For instance, the input interface  910  may be used to receive instructions from an external computer device, from a user, etc. The computing device  900  also includes an output interface  912  that interfaces the computing device  900  with one or more external devices. For example, the computing device  900  may display text, images, etc. by way of the output interface  912 . 
     It is contemplated that the external devices that communicate with the computing device  900  via the input interface  910  and the output interface  912  can be included in an environment that provides substantially any type of user interface with which a user can interact. Examples of user interface types include graphical user interfaces, natural user interfaces, and so forth. For instance, a graphical user interface may accept input from a user employing input device(s) such as a keyboard, mouse, remote control, or the like and provide output on an output device such as a display. Further, a natural user interface may enable a user to interact with the computing device  900  in a manner free from constraints imposed by input devices such as keyboards, mice, remote controls, and the like. Rather, a natural user interface can rely on speech recognition, touch and stylus recognition, gesture recognition both on screen and adjacent to the screen, air gestures, head and eye tracking, voice and speech, vision, touch, gestures, machine intelligence, and so forth. 
     Additionally, while illustrated as a single system, it is to be understood that the computing device  900  may be a distributed system. Thus, for instance, several devices may be in communication by way of a network connection and may collectively perform tasks described as being performed by the computing device  900 . 
     Various functions described herein can be implemented in hardware, software, or any combination thereof. If implemented in software, the functions can be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer-readable storage media. A computer-readable storage media can be any available storage media that can be accessed by a computer. By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc (BD), where disks usually reproduce data magnetically and discs usually reproduce data optically with lasers. Further, a propagated signal is not included within the scope of computer-readable storage media. Computer-readable media also includes communication media including any medium that facilitates transfer of a computer program from one place to another. A connection, for instance, can be a communication medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio and microwave are included in the definition of communication medium. Combinations of the above should also be included within the scope of computer-readable media. 
     Alternatively, or in addition, the functionally described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc. 
     What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable modification and alteration of the above devices or methodologies for purposes of describing the aforementioned aspects, but one of ordinary skill in the art can recognize that many further modifications and permutations of various aspects are possible. Accordingly, the described aspects are intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.