Abstract:
A system and method for achieving constant magnification of a scanned three-dimensional item without the use of special optics or other specialized hardware. The system includes the dynamic computation of a camera parameter-dependent factor and applying that factor in real-time to the sampled signal resulting from the scan of the item.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
   The present application is a continuation-in-part of U.S. patent application Ser. No. 10/361,350, entitled SYSTEM AND METHOD FOR INCREASING TEMPORAL AND SPATIAL CAPACITY OF SYSTEMS THAT AMPLITUDE QUANTIZE DATA PRIOR TO PROCESSING filed on Feb. 10, 2003 now U.S. Pat. No. 7,162,098, which claims priority to U.S. Provisional Application No. 60/412,650 filed Sep. 20, 2002, entitled SYSTEM AND METHOD FOR INCREASING TEMPORAL AND SPATIAL CAPACITY OF SYSTEMS THAT AMPLITUDE QUANTIZE DATA PRIOR TO PROCESSING which is incorporated herein in its entirety by reference. The present application claims priority to U.S. Provisional Application No. 60/432,768 filed on Dec. 12, 2002, entitled FIRMWARE SOLUTION FOR CONSTANT MAGNIFICATION IMAGING which is incorporated herein in its entirety by reference. 

   BACKGROUND OF THE INVENTION 
   This invention relates generally to high resolution amplitude data collection, and in particular, to a constant magnification imaging system and method based on firmware up-sampling without specialized optics. 
   When an image is taken using a standard camera lens objects appear larger when they are closer to the camera than when they are further from the camera. This is due to optical perspective, which causes images of rectangular objects to appear trapezoidal when viewed with one edge of the rectangle closer to the camera than the opposite edge. An example, although not limited thereto, where such a problem is of particular significance is in applications in which parcels are aligned, traveling down a conveyor belt and imaged by a line scan camera, in which the top, bottom, left and right side images are rectangular. However, if an attempt is made to scan the leading or trailing surface of the box, the result is a trapezoidal image. This causes complications with contextual analysis software that is trying to determine if a particular block of text is a destination address, using relative position as one of the deciding factors. The problem is complicated further if the parcels are not aligned on the conveyor belt as they pass the scan line and four out of the six sides are in various degrees of distortion. 
   To simplify the contextual analysis algorithms required for this function, and thereby accelerate the overall assignment process, methods have been developed to produce “constant magnification” images. These techniques remove the optical perspective property and images of leading and trailing sides of a box appear as if they were perfectly aligned side surfaces, resulting in undistorted rectangular images. However, the current methods for producing constant magnification images are either large and unwieldy, such as telecentric optics, or are made up of high speed mechanisms, such as dynamic zoom/focus lens, that are prone to failure. These devices also add significant cost to the camera system. 
   SUMMARY OF THE INVENTION 
   The problems set forth above as well as further and other problems are solved by the present invention. The solutions and advantages of the present invention are achieved by the illustrative embodiment of the present invention described hereinbelow. 
   In general terms, the constant magnification imaging system and method of the present invention provide for constant magnification through dynamic rescaling. Thus, in the system of the present invention, standard optics can be used to acquire the image and generate a constant magnification result. The constant magnification imaging system and method of the present invention perform asynchronous and real-time spatial up-sampling and amplitude quantizing for live operations that collect relatively high resolution amplitude data at the spatial and temporal capacity of the data collection device (herein referred to as relatively low characteristic scan resolution), but only process an amplitude quantized version of the data. 
   The method of present invention includes the step of choosing a target scan resolution. The method further includes the steps of receiving object dimensions and orientation, and building a dynamic focus profile to focus at least one camera based on the received object dimensions and orientation. The method also includes the steps of sampling, by the at least one camera, a signal source&#39;s reflected light amplitude at a relatively high resolution, and sampling, by the at least one camera, the signal source&#39;s surface area at a relatively low characteristic spatial/temporal frequency or scan resolution. The method further includes the steps of receiving the sampled relatively low characteristic scan resolution, relatively high resolution amplitude data as input data, and identifying a conventional rescaling algorithm and conventional quantizing algorithm, from a group of pre-selected algorithms, according to the received input data. The method further includes the step of applying a pre-determined focus-to-resolution curve to the dynamic focus profile and the target scan resolution, to create a dynamic resealing profile. The method further includes the steps of applying the rescaling algorithm to the combination of at least one rescaling factor from the dynamic rescaling profile and at least one line of the input data in real-time to create intermediate data of target scan resolution. The method further includes the step of applying the quantizing algorithm to the intermediate data in real-time to compute relatively low resolution amplitude output data of constant magnification at the target scan resolution. The target scan resolution, constant magnification, relatively low resolution amplitude output data are of substantially the same quality as if the signal source&#39;s amplitude were sampled at a relatively high resolution and a relatively high characteristic scan resolution, which is equivalent to the target scan resolution, using constant magnification optics, and then without changing the scan resolution, quantized into relatively low resolution amplitude output data. 
   For example, in a more specific application of the methodology of the present invention, the method of the illustrative embodiment includes the step of receiving dimensions of an object such as, for example, an upstream parcel as it travels down a transporting means such as, for example, a conveyor belt from an up-stream dimensioning device, and receiving a target scan resolution. The method further includes the step of uploading the dynamic focus profile to the at least one camera specific to the type of camera and its position with respect to the parcel. The method also includes the step of dynamically focusing the at least one conventional line scan camera by applying to the at least one line scan camera the dynamic focus profile. In what is known as open loop mode, the method still further includes the steps of creating at least one dynamic rescaling profile containing at least one rescaling factor derived from a pre-determined focus-to-resolution curve and target scan resolution, and uploading the at least one dynamic rescaling profile to a conventional programmable frame grabber device. The method also includes the step of selecting at least one appropriate conventional rescaling algorithm and at least one appropriate conventional quantizing algorithm and using them in the conventional programmable frame grabber. The method further includes the step of sampling the reflected light amplitude at a relatively high resolution, and the spatial frequency of the reflected light at a relatively low characteristic scan resolution, to produce gray image data of the parcel as it passes by the at least one camera. The method still further includes the steps of transferring the gray image data from the camera to the programmable frame grabber, and applying, in the programmable frame grabber in real-time, the conventional rescaling algorithm with the at least one rescaling factor, from the dynamic rescaling profile, to the relatively low characteristic scan resolution, relatively high resolution amplitude input data in real-time to create target scan resolution, relatively high resolution amplitude intermediate data, and applying the conventional amplitude quantizing algorithm to the intermediate data to create the target scan resolution, relatively low resolution amplitude output data. 
   The method of the present invention, in what is known as closed loop mode, differs from the open loop mode in that no dynamic rescaling profile is generated because the information required for dynamic rescaling is embedded in the image itself. In either open loop or closed loop mode, the camera requires focus information. In the closed loop mode, the focus information is imbedded in each image line that is sent to the frame grabber. The frame grabber uses the embedded focus data, the focus-to-resolution curve, and a target scan resolution to create at least one rescaling factor. The frame grabber applies the selected rescaling algorithm, with the at least one rescaling factor, to the image line to create a constant magnification image line of target scan resolution, which is then amplitude quantized using the selected quantization algorithm to relatively low resolution amplitude data. 
   In a general sense, the system of the present invention includes, but is not limited to, a data collection subsystem, an algorithm selection subsystem, and a data processing subsystem. More specifically, the system of the present invention includes a data collection device which may be part of the data collection subsystem. The data collection device is utilized for sampling a signal&#39;s amplitude at a relatively high resolution, but at a relatively low characteristic scan resolution. The relatively low characteristic scan resolution is based upon the spatial and temporal capacity of the data collection device. The data collection device has a characteristic scan resolution which, in the illustrative embodiment of the present invention, is, for example, relatively low in comparison to the target scan resolution of the resulting image. 
   The algorithm selection subsystem can choose an appropriate resealing algorithm and amplitude quantizing algorithm for an object that is being scanned and provide the resealing algorithm and the amplitude quantizing algorithm to the data processing subsystem. The algorithm selection subsystem establishes appropriate algorithms for processing the relatively low characteristic scan resolution, relatively high resolution amplitude input data in real-time. 
   The data processing subsystem executes, in real-time, the algorithms chosen by the algorithm selection subsystem to generate an amplitude quantized undistorted image of constant magnification and at the target scan resolution from relatively low characteristic scan resolution, relatively high resolution amplitude input data of a distorted image. The data processing subsystem is operably connected to the data collection subsystem and the algorithm selection subsystem. The data processing subsystem of the present invention builds a dynamic rescaling profile from external input means or pre-defined parameters including, for example, a pre-determined focus-to-resolution curve and a target scan resolution. The data processing subsystem executes the rescaling algorithm and the amplitude quantizing algorithm against the sampled input data and the dynamic rescaling profile such that the resulting image has an undistorted constant magnification. Further, the data processing subsystem is capable of executing algorithms as above in parallel with other on-going computer operations. In addition, the data processing subsystem is also capable of transferring the amplitude quantized image to a conventional CPU or another system for further processing. 
   In open loop mode, the purpose of the dynamic rescaling profile is to vary the magnification of the input data. The dynamic resealing profile contains factors for processing one or more lines of input data. In open loop mode, a rescaling factor from the dynamic rescaling profile is used by the data processing subsystem to apply to a certain line of input data based on the distance of the data collection device from the signal source (the focus value), and on the target scan resolution. The resulting rescaling factors are independent horizontal and vertical ratios that are applied to each line of input data. In closed loop mode, the dynamic rescaling that results in constant magnification is accomplished by deriving the rescaling factors from the focus information, passed from the data collection subsystem to the data processing subsystem within the input data itself, along with the focus-to-resolution curve and the target scan resolution, as is done in open loop mode. 
   The system of the present invention can further include a first storage location and a second storage location, both operably connected to the data processing system. The first and second storage locations can be one in the same, and both can be computer memory, computer mass storage, or computer networked storage. The data processing subsystem is capable of storing in real time, in the first storage location, intermediate data that result from the application of the selected rescaling algorithm. Further, the data processing subsystem is capable of storing in real time, in the second storage location, the output data that result from the application of the selected amplitude quantizing algorithm. 
   For a better understanding of the present invention, reference is made to the accompanying drawings and detailed description. The scope of the present invention is pointed out in the appended claims. 

   
     BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING 
       FIG. 1  is a schematic block diagram of the constant magnification system of the present invention; 
       FIGS. 2A-C  are flowcharts of the method of the illustrative embodiment of the present invention; 
       FIG. 3  is a schematic representation of the illustrative embodiment of the present invention; 
       FIG. 4  is a schematic, pictorial representation of some of the components of the present invention, illustrating an object being transported on a conveying or moving system, past dimensioners and line scan cameras, which may be utilized with the present invention; 
       FIG. 5  is a control and data flow schematic diagram of the algorithm selection system of the illustrative embodiment of the present invention; and 
       FIGS. 6A and 6B  are photographic representations of the prior art and the result of applying the method of the illustrative embodiment of the present invention to printed sheets mounted on the front and on one side of an object aligned parallel to the direction of travel as the object travels through the system of the present invention. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   The present invention is now described more fully hereinafter with reference to the accompanying drawings, in which the illustrative embodiment of the present invention is shown. 
   The components of system  10  of the present invention are schematically shown in  FIG. 1 . A schematic, illustrative embodiment of the present invention is also presented in the description accompanying  FIG. 3 . A practical application of the present invention is shown in  FIG. 4 . Referring now primarily to  FIG. 1 , system  10  generally includes a signal source  13 , a data collection subsystem  15 , an algorithm selection subsystem  25 , a data processing subsystem  27 , a scanning device  36 , and an external input means  32 . Scanning device  36  scans an object (such as parcel  61 ,  FIG. 4 ) and sends scanned image  34  to algorithm selection subsystem  25  for classification of the object. The algorithm selection subsystem  25  classifies the image (see  FIG. 5 ) and sends the appropriate rescaling algorithm  49 B and amplitude quantizing algorithm  49 A to the data processing subsystem  27 . Conventional dimensioning device  11  provides dimensional data  44 A to data processing subsystem  27 . Data collection subsystem  15  receives dimensional data  44 A either directly from conventional dimensioning device  11  or as dynamic focus profile  44 B through data processing subsystem  27 . No matter how dimensional data  44 A are provided to the data collection subsystem  15 , the data are used to set up the data collection subsystem  15  to properly focus on the signal source  13 . The data collection subsystem  15  samples reflected light amplitude of signal source  13  at a relatively high resolution and samples the surface area of signal source  13  at a relatively low characteristic scan resolution after signal source  13  passes triggering sensor  35  which triggers data collection subsystem  15  to begin sampling. The data collection subsystem  15  transmits in real-time, through operable connection  33 , the relatively low characteristic scan resolution, relatively high resolution amplitude input data  19  on a line-by-line basis to data processing subsystem  27 . While the data collection subsystem  15  is designed for real-time operation, batch processing is not precluded by this invention. Operable connection  33  can include, but isn&#39;t limited to, any or all of the following: a data bus, an internet connection, a local area network connection, an ANSI/TIA/EIA-644 interface, a Cameralink™ specification compliant physical interface, or any other type of electrical connection that allows the transmission of electronic image data. 
   Continuing to refer to  FIG. 1 , algorithm selection subsystem  25  can use scanned image  34  from scanning device  36  to select a rescaling algorithm  49 B automatically, or the resealing algorithm  49 B could also be selected manually or by default. Algorithm selection subsystem  25  provides rescaling algorithm  49 B to data processing subsystem  27 . Algorithm selection subsystem  25  also allows the selection of at least one amplitude quantizing algorithm  49 A, either manually, by default, or automatically. The dimensional data  44 A, a target scan resolution, and a focus-to-resolution curve are processed to generate a dynamic rescaling profile  49 . The target scan resolution and the focus-to-resolution curve can be provided by, for example, external input means  32  or can be pre-defined. The data processing subsystem  27  receives relatively low characteristic scan resolution, relatively high resolution amplitude input data  19  from the data collection subsystem  15  and, in open loop mode, executes the rescaling algorithm  49 B, with rescaling factors from the dynamic rescaling profile  49 , to each line of input data  19  to convert the relatively low characteristic scan resolution, relatively high resolution amplitude, distorted, input data  19  to a line at the target scan resolution which is undistorted constant magnification relatively high resolution amplitude data (intermediate data  21 ). The line of intermediate data  21  may be stored for possible use by other systems in a first storage location  29 . In closed loop mode, data processing subsystem  27  derives the resealing factors from information located within the input data  19  itself and executes those factors against input data  19  as above. Continuing to refer to  FIG. 1 , the data processing subsystem  27  also executes for each line of intermediate data  21  the selected amplitude quantizing algorithm  49 A to convert the target scan resolution, relatively high resolution amplitude data to target scan resolution, relatively low resolution amplitude data (output data  23 ). These data may also be stored for possible use by other systems in a second storage location  31 . 
   For a better understanding of the present invention, including its methodology set forth in a series of steps provided below, reference is now made to FIGS.  1  and  2 A-C. The method of the present invention includes the step of setting a target scan resolution, a focus-to-resolution curve, and possibly manually selecting a rescaling algorithm  49 B ( FIG. 1 ) and an amplitude quantizing algorithm  49 A ( FIG. 1 ), possibly by external input means  32  ( FIG. 1 ) (method step  201 ). The method can further include the step of receiving, from an upstream conventional dimensioning device  11  ( FIG. 1 ) and possibly a scanning device  36  ( FIG. 1 ), information about an object including, but not limited to, height, width, length, transverse position, rotation, and a scanned image  34  ( FIG. 1 ) (method step  203 ). The method also includes the step of generating focus commands (focus profile  44 B ( FIG. 1 )) for at least one data collection device from the information about the object, the focus commands depending on the location of the at least one data collection device with respect to the object (method step  205 ). The method further includes the steps of receiving input data and establishing resealing algorithm  49 B and amplitude quantizing algorithm  49 A (method step  207 ). In open loop mode (decision step  209 ), the method further includes the steps of generating a dynamic rescaling profile  49  ( FIG. 1 ) for the incoming object image from parameters, such as, for example, the focus-to-resolution curve, focus profile  44 B, and the target scan resolution (method step  211 ). The method further includes the step of aligning the first data point of the dynamic rescaling profile  49  as closely as possible with the first scan line of the surface of the object within the scanned image (method step  213 ). The method still further includes the steps of applying rescaling algorithm  49 B and at least one rescaling factor from dynamic rescaling profile  49  to at least one line of input data  19  ( FIG. 1 ) to create intermediate data  21  ( FIG. 1 ) (method step  215 ). In closed loop mode (decision step  209 ), the method includes the step of embedding the current focus value in the object image data, such as, for example, in the first pixel, for each line of input data  19  (method step  217 ). The method still further includes the steps of receiving the line of input data  19 , determining a dynamic rescaling profile factor from the current focus value, and applying the dynamic resealing profile factor to that line of input data  19  to create intermediate data  21  (method step  219 ). For either mode, the method further includes the step of applying amplitude quantizing algorithm  49 A to each line of intermediate data  21  to create output data  23  ( FIG. 1 ) (method step  221 ). The method further includes the step of transmitting each line of output data  23  elsewhere for further processing (method step  223 ) and exiting. Note that lines of input data can be processed individually, or groups of lines or the whole image can be processed in batch mode. 
   The method of the present invention can be, in whole or in part, implemented electronically by the system as described above with respect to  FIG. 1 . Signals representing actions taken by elements of the system can travel over electronic communications media. Control and data digital information can be electronically executed and stored on computer-readable media. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, or any other magnetic medium, a CDROM or any other optical medium, punched cards, paper tape, or any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, or any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read. 
   Whereas  FIG. 1  describes a general case of the present invention, system  20 , shown in  FIG. 3 , presents an illustrative embodiment of the present invention, wherein like components are referenced by similar reference numbers in both the general case of  FIG. 1  and the illustrative embodiment of  FIG. 3 . System  20  generally includes a line scan camera set  41  (a specific instance of data collection subsystem  15  ( FIG. 1 )) including one or more conventional line scan cameras. System  20  further includes a conventional frame grabber  45  and a conventional computer CPU  17  with computer memory  47 , collectively providing an instance of data processing subsystem  27  ( FIG. 1 ). System  20  still further includes conventional laser scanning dimensioner  51  (a specific instance of conventional dimensioning device  11  ( FIG. 1 )) that measures parcel dimensions and orientation, such as, for example the Accu-Sort™ DM-3000 which is an overhead dimensioning unit that automatically measures the length, width, and height of packages as they move along a conveyor. 
   Continuing to refer to  FIG. 3 , Lines “A” and “B” indicate the perspective of the line scan camera set  41  as it views an object across conveyor belt  43 . Since line “A” takes up the full field of view, the image appears larger at Line “A” than the image at line “B”, that takes up a much smaller portion of the field of view, even though the object, or more specifically the parcel, has not changed in size. The system and method of the present invention resize the image at line “B” based upon the relative distance of the parcel from the camera lens. Thus, both line “A” and line “B” (which has been enlarged to the size of line “C”) are the same size in the resulting image. 
   Continuing to refer to  FIG. 3 , in operation, dimensional data  44 A such as, for example, height, width, length, transverse position, and rotation, are received into conventional CPU  17  from conventional laser scanning dimensioner  51 . The conventional CPU  17  could then generate and send dynamic focus profile  44 B to line scan camera set  41  to allow dynamic focusing of the surface as it goes by line scan camera set  41 . Dynamic focusing within the present invention can be accomplished in others ways. For example, but not limited thereto, line scan camera set  41  could receive dimensional data  44 A in a streaming fashion and adjust the focus as the data are received. The invention is not limited to either configuration. For example, conventional laser scan dimensioner  51  can either transmit dimensional data  44 A to either CPU  17  (in open or closed loop mode), or line scan camera set  41  (in closed loop mode only), or both (in open or closed loop mode). Line scan camera set  41  can either use the dynamic focus profile  44 B, from CPU  17 , and/or can use dimensional data  44 A. Dynamic focus profile  44 B is created for each camera in camera set  41 . The dynamic focus profile  44 B for the particular camera in camera set  41  is sent to that camera to enable the camera to ensure that the image of the parcel surface is in sharp focus as it tracks where the surface is in relation to the camera&#39;s lens. Clearly the contents of dynamic focus profile  44 B for each camera depend on the position of the camera with respect the parcel to be scanned. In “open loop” mode, dimensional data  44 A are also used to create a dynamic rescaling profile  49 , a table of rescaling factors. The conventional CPU  17  loads dynamic rescaling profile  49  into frame grabber  45 , where resealing of the image on a line-by-line basis in real-time takes place, creating intermediate data  21  ( FIG. 1 ). Following this procedure, the selected amplitude quantizing algorithm  49 A ( FIG. 1 ) is executed against each line of intermediate data  21 . The resulting output data  23  ( FIG. 1 ) can be stored in any convenient location, including but not limited to conventional memory  47 , networked memory, or mass storage. 
   Reference is now made to  FIG. 4  for a description of examples of practical applications of the present invention. A typical object such as parcel  61  is scanned on all six sides by four line scan cameras  41 A-D as parcel  61  passes by cameras  41 A-D on conveyor belt  43 . Note that the first and second side cameras,  41 A and  41 B respectively, can be, but aren&#39;t limited to being, oriented at 45° with respect to the conveyor belt, which allows the cameras  41 A and  41 B to scan both one side of parcel  61  and either the leading side  63  or trailing side  65  of parcel  61 . For the desired results, parcel  61  is substantially aligned with respect to the edge  67  of the conveyer belt  43  (within +/−20°). Such alignment prevents the angle of incidence from becoming too steep to acquire a usable image. 
   Still referring to  FIGS. 1 ,  3 , and  4 , after being notified of an upstream parcel  61  ( FIG. 4 ) by sensor  35  ( FIGS. 1 ,  4 ), conventional laser scanning dimensioner  51  ( FIGS. 3 ,  4 ) supplies position, size and orientation data of parcel  61  through computer CPU  17  ( FIG. 3 ) to the line scan cameras  41 A-D ( FIG. 4 ). Optionally, scanning device  36  ( FIG. 4 ) can supply scanned image  34  ( FIG. 1 ) to algorithm selection subsystem  25  ( FIG. 1 ) to facilitate automated algorithm selection as depicted in  FIG. 5 . CPU  17  sends to line scan camera  41 C a message, such as, to “focus to the height of the parcel”. CPU  17  sends to cameras  41 A and B the dynamic focus profile  44 B ( FIG. 3 ) that defines the corners of parcel  61  as viewed from above. From the corner coordinates of parcel  61 , the focus profile can be determined by the camera. In the illustrative embodiment, line scan camera  41 D is a constant focus camera, since parcel  61  sits upon conveyor belt  43  surface and does not change in distance from line scan camera  41 D lens, and thus dynamic rescaling profile  49  for camera  41 D is constant and data from line scan camera  41 D are rescaled by a constant value. CPU  17  can supply dynamic focus profile  44 B to line scan cameras  41 A, B, and C, and thus resealing factors can be chosen either as a function of the focus profile data in open loop mode or from the embedded data received from line scan cameras  41 A, B, and C in closed loop mode (see  FIGS. 2A-C ). 
   Continuing to refer to  FIGS. 1 ,  3 , and  4 , in the system of the present invention, in open loop mode, CPU  17  ( FIG. 3 ) provides frame grabber  45  ( FIG. 3 ) with dynamic rescaling profile  49  ( FIG. 3 ). Dynamic rescaling profile  49 , containing, for example, rescaling factors, varies according to the part of the parcel  61  that is being viewed. For example, when parcel  61 , aligned as shown in  FIG. 4 , traveling in direction of travel  38 , comes into view of line scan camera  41 B ( FIG. 4 ), the front of parcel  61 , leading side  63  ( FIG. 4 ), is scanned. This scanning results in an image of trapezoidal shape with the furthest edge  63 B ( FIG. 4 ) from line scan camera  41 B appearing smaller than the closest edge  63 A ( FIG. 4 ) to line scan camera  41 B. If a constant rescaling factor is applied to the image, as in prior systems, the resolution is improved, but the trapezoidal distortion remains. However, using dynamic rescaling profile  49  not only improves the resolution of the resulting image, but also removes the trapezoidal distortion by applying gradient up-sampling factors on a line-by-line basis, which magnify the elements from the furthest edge  63 B to the closest edge  63 A of the image to match the size of the elements of closest edge  63 A within the resulting image. Camera  41 A performs a similar operation on trailing side  65  ( FIG. 4 ). The final images of leading side  63  and trailing side  65  are rectangular in shape, as if they had been scanned at a constant distance from the lens of cameras  41 B, in the way that the right side  63 C of the parcel is imaged. 
   Continuing to refer to  FIGS. 1 ,  3 , and  4 , the system of the present invention can be operated in either a closed loop or open loop manner. In open loop mode, frame grabber  45  receives a copy of dynamic resealing profile  49  ( FIG. 3 ) specific for line scan cameras  41 A-D and applies the rescaling factors from dynamic rescaling profile  49  accordingly. The software executing in CPU  17  ( FIG. 3 ) generates or builds a dynamic rescaling profile  49  ( FIG. 3 ) based on the combination of the dynamic focus profile  44 B, the focus-to-resolution curve, and the target scan resolution. 
   In closed loop mode, line scan cameras  41 A-D can receive dynamic focus profile  44 B from CPU  17  or directly from dimensioner  51 . Line scan cameras  41 A-D ( FIG. 4 ) can then embed their focus information within lines of input data  19  ( FIG. 1 ) and transmit focus information and input data  19  to frame grabber  45  ( FIG. 3 ), or focus information can be transmitted any other way. This information can include the distance between the line scan cameras  41 A-D and the object upon which they focus, and can include the angle of incidence from line scan cameras  41 A-D to the object. In the illustrative embodiment of the present invention, the first two pixels of each line of input data contain focus and angle information, however there is no limitation on the positioning of this information, nor the content of the information. 
   Continuing to refer to  FIG. 4 , in the illustrative embodiment of the present invention, the digital line-scan cameras  41 A-D can each be, but are not limited to, Accu-sort® model AV3800. This type of digital line-scan camera includes a high speed transmission device that allows data rates on two channels up to forty Megabytes/second/channel. Although the illustrative embodiment includes such a camera, the invention isn&#39;t limited to a particular number of channels, a particular data rate, or a particular camera. Conventional programmable frame grabber  45  ( FIG. 3 ) may be a model Accu-sort® Accu-link frame grabber (under development by Accu-sort®), but is not limited to this make and model of frame grabber. The conventional programmable frame grabber  45  has the capability of receiving data such as those generated by digital line scan cameras  41 A-D, but in general any combination of digital or analog camera and compatible programmable frame grabber of similar or higher capability or other coupled devices can be used to perform the processing specified herein. The particular camera/frame grabber combination is described herein for illustrative purposes only. 
   The system of the present invention could be used to rescale the supplied image in any way, including reduction and enlargement of the image, to any desired magnification. The system of the present invention allows for imaging distortion-free front and trailing surfaces of objects without special optics. In addition an object such as a parcel can be situated anywhere across the width of the belt, and the system of the present invention can accommodate larger objects than systems of the prior art. 
   Referring again to  FIGS. 1 ,  3 , and  4 , for each line of input data  19  ( FIGS. 1 ,  3 ), after the algorithm selection subsystem  25  ( FIG. 1 ) (described below) allows the selection of suitable algorithms for the particular image, data processing subsystem  27  ( FIG. 1 ), including software that is either uploaded (e.g. the algorithms) or resident—executing in frame grabber  45  ( FIG. 3 ) and software executing in computer CPU  17  ( FIG. 3 ) in the illustrative embodiment—continues processing as follows. In the illustrative embodiment, data processing subsystem  27  can upload all selected algorithms that can later be identified by the algorithm selection subsystem  25  at initialization into frame grabber  45  by commercial utilities provided with the conventional frame grabber product. The present invention is not limited to a particular method of loading the algorithms into frame grabber  45 , i.e. the algorithms do not have to be loaded at initialization, but can be dynamically loaded during object processing. As an object is dimensioned, data processing subsystem  27  receives an identification number for the object, and receives dimensional data  44 A ( FIGS. 1 and 3 ), such as parcel dimension and orientation, about the object from conventional laser scanning dimensioner  51  ( FIGS. 3 and 4 ). Dimensional data  44 A ( FIG. 1 ) is used by data processing subsystem  27  to generate dynamic focus profile  44 B ( FIGS. 1 and 3 ), which is sent to cameras  41 A-D. Focus profile  44 B enables cameras  41 A-D to maintain a sharp optical image of the surface as it passes through the scan lines of cameras  41 A-D. In open loop mode, dimensional data  44 A can also be used by data processing subsystem  27  to generate dynamic rescaling profile  49  ( FIGS. 1 and 3 ), which is sent to the frame grabber  45  and applied to the image on a line-by-line basis as the camera scans the parcel surface. In closed loop mode, cameras  41 A-D embed a focus value so that frame grabber  45  can determine a rescaling factor for each scan line by reading the embedded focus value of that scan line and using a lookup table, that is generated from the focus-to-resolution curve and the target scan resolution, that can be loaded at initialization or at any other time, or computed dynamically. Intermediate data  21 , which can be saved in first storage location  29 , results from the execution of rescaling algorithm  49 B on input data  19  and the associated rescaling factor for that line of input data  19 . Output data  23 —which can be stored in second storage location  31  or can be electronically transmitted elsewhere over electronic interface  33  (FIG.  1 )—results from the application of amplitude quantizing algorithm  49 A to intermediate data  21 . Local processing can continue on in parallel in CPU  17  ( FIGS. 1 and 3 ) while intermediate data  21  and output data  23  are being computed in frame grabber  45 . 
   Referring now to  FIG. 5 , for each image that is produced by scanning an object using the line scan camera  41 , for example a mail parcel, the illustrative flow of the algorithm selection system  25  ( FIG. 1 ) is shown. The method of the illustrative embodiment of the present invention includes a first step of determining an identification process for a mail type, that is, a characterization of an object that is being scanned by such as, for example, scanning device  36  ( FIGS. 1 and 4 ) (decision step  401 ). If the identification process is “default”, the method includes the step of identifying default algorithms (amplitude quantizing algorithm  49 A ( FIG. 1 ) and rescaling algorithm  49 B ( FIG. 1 )) to use for processing the image of the object and exiting (method step  418 ). If the identification process is manual, the method includes the step of identifying, possibly through an external input means, optimal algorithms for processing the image (method step  421 ) and exiting. In this step, for example, a user could be prompted for the type of object to be processed, or the user could be prompted for the actual algorithms to be used. If the identification process is automated, the method includes the steps of performing an upstream scan (such as, for example, by scanning device  36 ) of the object, for example a mail parcel, being imaged (method step  403 ) and generating an image signature in order to determine the mail type (method step  405 ). The method further includes the step of determining if the image signature exists in an image signature database (decision step  407 ). If the image signature exists in the database, the method further includes the step of retrieving from the database of mail type/algorithms the optimal algorithms for processing the image (method step  421 ) and exiting. If the image signature does not exist in the database, the method of the illustrative embodiment includes further parallel steps of identifying default algorithms (method step  418 ) and exiting, as well as determining if the quantity of images, which have signatures that are similar enough to be grouped as a unique mail type, is sufficient such that a mail type should be created, optimized, and stored in the database (decision step  409 ). If there are currently not enough images of that particular mail type to warrant optimization, the method further includes the steps of incrementing and storing a count of the candidate mail type (method step  417 ) and exiting. If there are enough images of that particular type to warrant optimization (decision step  409 ), the method includes the further steps of performing an optimization analysis on this type of image (method step  411 ), linking optimal algorithm options to the mail type/algorithm database, storing the mail type in association with an image signature and optimal algorithms (method step  413 ), and exiting. 
   Possible rescaling algorithms  49 B ( FIG. 1 ) can include, but are not limited to, linear, nearest-neighbor, Lagrange- and Gaussian-based interpolators, Blackman-Harris windowed-sinc kernels, quadratic and cubic convolution, and cubic B-spline. Descriptions of these techniques are given in  A Chronology of Interpolation: From Ancient Astronomy to Modern Signal and Image Processing , Meijering, E.,  Proceedings of the IEEE , Vol. 90. No. 3. March 2002, pp. 319-341, incorporated in its entirety herein by reference. 
   Possible amplitude quantizing algorithms  49 A ( FIG. 1 ) can include, but are not limited to, locally adaptive algorithms described in  Goal - Directed Evaluation of Binarization Methods , Trier, O. D. and Jain, A. K., citeseer.nj.nec.com/trier95goaldirected.html, 1995, pp. 47-58, incorporated in its entirety herein by reference, such as Yankowitz/Bruckstein and White/Rohrer, globally adaptive algorithms such as Otsu, and modified locally adaptive algorithms such as Niblack and Eikvil/Taxt/Moen. Other algorithms are outlined in  Evaluation of Binarization Methods for Document Images , Trier, O. D. and Taxt, T.,  IEEE Transaction on Pattern Analysis and Machine Intelligence,  17, pp. 312-315, 1995, incorporated in its entirety herein by reference. 
   In order to visualize the result of operating the system of the present invention, reference is now made to  FIGS. 6A and 6B , which are images taken of a substantially aligned box that has been imaged by a system of the prior art ( FIG. 6A ) and the system of the present invention ( FIG. 6B ). Referring now to  FIG. 6A , trapezoidal image  601  shows a front surface  605  and side surface  607  of an object rescaled with a constant factor according to system of the prior art. Note the trapezoidal distortion of front surface  605  as compared with side surface  607 . Referring now to  FIG. 6B , constant magnification image  603  illustrates how the application of the constant magnification imaging system and method of the present invention eliminate the trapezoidal distortion of corrected front surface  609  and present an image very similar to the aligned side surface  607 , which is much easier to process. 
   Although the invention has been described with respect to various embodiments, it should be realized this invention is also capable of a wide variety of further and other embodiments within the spirit and scope of the appended claims.