System and method for evaluating symbols

Abstract

Evaluating the symbol on the object may include acquiring a first image of the object, including the symbol. The second image may be derived from the first image based on determining a saturation threshold for the second image and possibly scaling pixel values ​​to a reduced bit depth of the second image.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] not applicable.

[0003] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0004] not applicable. Technical Field

[0005] The present invention relates to evaluation symbols, including verification bar codes. Background Art

[0006] In many situations, it may be useful to evaluate a symbol using an imaging system. For example, in direct part marking ("DPM") and other environments, images acquired for a barcode may be analyzed to verify that the barcode has been properly printed or marked, or for other reasons. In some cases, the symbol to be evaluated may be moving. For example, verifying a DPM barcode may be useful when an object marked with a barcode is moving along a production line or other conveying equipment. Summary of the invention

[0007] Some embodiments of the present invention provide a system for evaluating a symbol on an object. An imaging system may include an imaging device and a processor device in communication with the imaging device. The imaging device may be configured to acquire a first image of the object, including the symbol. The processor device may be configured to generate a second image derived from the first image based on determining a saturation threshold for the second image and mapping pixels of the first image to the second image, including mapping pixels of the first image having values ​​exceeding the saturation threshold to a maximum value of the second image, such that a brightness measurement of the second image exhibits a predetermined target relationship to the saturation threshold of the second image.

[0008] Some embodiments of the present invention provide a system for evaluating a symbol on an object. An imaging device may be configured to acquire a first image of the object, including the symbol, at a first bit depth. A processor device may be configured to generate a second image derived from the first image based on: determining a saturation threshold of the second image based on a predetermined target relationship between the saturation threshold of the second image and a brightness measurement of at least one of the first image or the second image; and mapping pixels of the first image to the second image based on the saturation threshold of the second image.

[0009] Some embodiments of the present invention provide a method for evaluating a symbol on an object, which method can be implemented at least in part by using an imaging device and a processor device. The imaging device can be used to obtain a first image of the object, including the symbol. Using the processor device, a second image derived from the first image can be generated in the following manner, including: determining a saturation threshold for the second image, which saturation threshold can be different from the saturation threshold of the first image; and mapping pixels of the first image to the second image, including mapping pixels of the first image having values ​​exceeding the saturation threshold of the second image to a maximum value of the second image. The saturation threshold of the second image can be determined based on a target relationship between the saturation threshold of the second image and a brightness measurement of at least one of the first image or the second image. The processor device can be used to evaluate the second image to determine at least one attribute of the symbol.

[0010] Some embodiments of the present invention provide a system for evaluating a symbol on an object. An imaging system may include an imaging device and a processor device in communication with the imaging device. The imaging device may be configured to acquire a first image of the object, including the symbol, at a first bit depth. The processor device may be configured to: generate a second image from the first image in a manner including mapping pixels of the first image to pixels of the second image, the second image having a second bit depth equal to or less than the first bit depth; and evaluate the second image to determine at least one quality attribute of the symbol.

[0011] Some embodiments of the present invention provide a system for evaluating a symbol on an object. The system may include an imaging device and a processor device in communication with the imaging device. The imaging device may be configured to acquire a first image of the object, including the symbol, at a first bit depth. The processor device may be configured to: determine at least one of a saturation threshold and a bright pixel average for the first image, and determine a mapping of pixels of the first image to a second image having a second bit depth less than or equal to the first bit depth based at least in part on at least one of the saturation threshold and the bright pixel average. The processor device may also be configured to generate the second image having the second bit depth from the first image, and to evaluate the second image to determine at least one quality attribute of the symbol.

[0012] Some embodiments of the present invention provide a method for evaluating a symbol on an object, which method can be implemented at least in part using an imaging device and a processor device. The imaging device can be used to acquire a first image of the object, including the symbol, at a first bit depth. The processor device can be used to generate a second image from the first image in a manner that includes mapping pixels of the first image to pixels of the second image, and the second image has a second bit depth that is less than or equal to the first bit depth. The processor device can be used to evaluate the second image to determine at least one attribute of the symbol.

[0013] To achieve the above and related purposes, embodiments of the present invention may include one or more features fully described below. The above and following descriptions and the accompanying drawings set forth in detail certain exemplary aspects of the present invention. However, these aspects are only indicative of a few of the various ways in which the principles of the present invention may be employed. Other aspects, advantages, and novel features of the present invention will become apparent from the detailed description herein, as shown in conjunction with the accompanying drawings. Figure 1 Consider it. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The features and advantages of the present invention will become apparent from the following description taken in conjunction with the accompanying drawings, in which:

[0015] Figure 1 is a schematic diagram of a machine vision system for DPM verification according to an embodiment of the present invention;

[0016] Figure 2 is a schematic diagram of various aspects of a method for DPM verification according to an embodiment of the present invention;

[0017] Figure 3 and Figure 4 is a schematic diagram of various aspects of a method for DPM verification according to an embodiment of the present invention;

[0018] Figure 5 is a schematic diagram of a training process for DPM verification according to an embodiment of the present invention;

[0019] Figures 6 to 8 is a diagrammatic view of a method of symbol evaluation according to an embodiment of the present invention; and

[0020] Fig. 9 is a diagrammatic view of a training method for symbol evaluation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In the following detailed description, reference is made to the accompanying drawings, which form a part thereof. In the drawings, like reference numerals generally identify like parts, unless the context dictates otherwise. The example embodiments described in the detailed description, drawings, and claims are not intended to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It is readily understood that aspects of the present disclosure as generally described herein and shown in the drawings may be arranged, substituted, combined, separated, and designed in a variety of different configurations, all of which are expressly contemplated herein.

[0022] The term "article of manufacture" as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier (e.g., non-transient signal), or medium (e.g., non-transient medium). For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, etc.), optical disks (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., cards, sticks). In addition, it will be understood that a carrier wave may be employed to carry computer-readable electronic data, such as data for sending and receiving e-mail or for accessing a network such as the Internet or a local area network (LAN). Those skilled in the art will recognize that many modifications may be made to these configurations without departing from the scope and spirit of the claimed subject matter.

[0023] Also as used herein, unless otherwise specified or limited, the terms "component", "system", "module", etc. are intended to refer to a computer-related system that includes hardware, software, a combination of hardware and software, or software in execution. For example, a component can be, but is not limited to, a processor device, a process running on a processor device, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a computer and the computer can be a component. One or more components (or systems, modules, etc.) can reside within a process and / or thread of execution, can be located on one computer, distributed between two or more computers or other processing devices, and / or included in another component (or system, module, etc.).

[0024] Also as used herein, unless otherwise specified or limited, the term "symbol" refers to an indicia that carries information, including indicia that can be imaged by an imaging device. In some implementations, the symbol can be formed, such as by printing, deposition, laser etching, etc., including in the form of a DPM symbol, such as a DPM 2D (or other) barcode. In some implementations, the symbol can be formed in other ways, such as by printing using ink.

[0025] Some embodiments of the present invention may be implemented as systems and / or methods, including computer-implemented methods. Some embodiments of the present invention may include (or utilize) devices, such as automatic devices, special-purpose or general-purpose computers including various computer hardware, software, firmware, etc., consistent with the discussion below.

[0026] In some embodiments, various aspects of the present invention (including computerized implementations of methods according to the present invention) can be implemented as systems, methods, apparatuses, or articles of manufacture using standard programming and / or engineering techniques for generating software, firmware, hardware, or any combination thereof to control a computer or other processor device to implement various aspects described herein. Accordingly, for example, embodiments of the present invention can be implemented as an instruction set tangibly implemented on a non-transitory computer-readable medium, thereby enabling a processor device to implement instructions based on reading instructions from the computer-readable medium.

[0027] Certain operations of the methods according to the present invention or certain operations of the systems for performing these methods are schematically represented in the accompanying drawings. Unless otherwise specified or limited, the representation of specific operations in a specific spatial order in the accompanying drawings is not intended to require that these operations be performed in a specific order. Certain operations represented in the accompanying drawings or otherwise disclosed herein may be performed in different orders that are appropriate for a specific embodiment of the present invention. Further, in some embodiments, certain operations may be performed in parallel, including by a dedicated parallel processing device, or by a separate computing device configured to interoperate as part of a large system.

[0028] As also discussed above, it may be useful to evaluate symbols (including 2D barcodes, etc.) using an imaging system. For example, in the context of DPM, it may be useful to use an acquired image of a DPM barcode (e.g., a 2D barcode) to verify the quality of the barcode. In this way, for example, a user can ensure that a DPM (or other) symbol exhibits suitably high print quality and overall fidelity.

[0029] Likewise, in some cases, it may be useful to evaluate symbols on objects that are in motion or that are stationary only for a short period of time. For example, it may be useful to verify DPM symbols while the tagged object is moving along a production line. This can significantly complicate the evaluation operation.

[0030] Evaluating symbols may also be useful in environments where lighting conditions may vary or be otherwise non-ideal, or for objects that may produce relatively specular reflections. This can also significantly complicate the evaluation operation.

[0031] Some conventional approaches aim to improve symbol evaluation by performing an iterative process, including through the use of a machine vision system, wherein the iterative process includes the acquisition and evaluation of multiple images. For example, a machine vision system may be configured to evaluate symbols based on various aspects of Technical Report 29158 (also known as the "AIM DPM" guide) published by the International Organization for Standardization ("ISO") and the International Electrotechnical Commission ("IEC").

[0032] The AIM DPM Guide describes an assessment of DPM symbol quality based on the acquisition of multiple images and the analysis of multiple images. Specifically, AIM DPM requires a specific system response (e.g., gain, aperture value, or shutter time setting) for the assessment of the barcode in the image, and provides an adjustment of the imaging device system response if the assessment of the initial image indicates incorrect exposure. For example, under AIM DPM, if the initial image is oversaturated or exhibits insufficient resolution, the system response and the subsequently captured image should be appropriately adjusted. Subsequent images can then be evaluated to determine whether the adjusted system response has achieved the correct system response (i.e., so that the ratio of the average bright pixel brightness to the saturation matches the expected value). In this way, through multiple continuous image acquisition processes, an operating system response can be obtained that properly balances saturation and resolution, and in particular maintains certain relationships between the overall image brightness and the brightness level at which saturation occurs. The operating system response can then be used for runtime image capture and symbol evaluation. In this way, for example, symbol evaluation can be uniformly applied for a given application or environment.

[0033] Symbol evaluation under the AIM DPM principle may be useful, but the acquisition of multiple images for evaluation may be impractical or undesirable in some environments. For example, on an active production line, it may not be feasible to acquire and process multiple images of a particular object with a DPM symbol without stopping the line operation. Accordingly, it may be useful to provide a system that can support accurate evaluation of DPM (or other) symbols without necessarily requiring multiple image capture.

[0034] Embodiments of the present invention can solve the above-mentioned problems, or otherwise provide improved systems or methods for symbol evaluation. For example, in some embodiments, to evaluate a symbol, a first image of the symbol may be acquired using an imaging device. A second image may then be derived from the acquired first image without necessarily requiring any further acquisition of images of the symbol (at least for the current evaluation cycle).

[0035] Specifically, in some embodiments, it may be useful to derive a second image from a first image based on determining a saturation threshold for the second image that will maintain a target relationship between the saturation threshold and image brightness. For example, a target ratio between a saturation threshold for the second image and image brightness may be predetermined (e.g., based on an AIM DPM or other relevant factors), and a saturation threshold for the second image may then be determined, thereby mapping pixels of the first image to the second image, including setting pixels exceeding the second image saturation threshold to a maximum value, thereby resulting in an image brightness for the second image that will substantially maintain the target ratio.

[0036] In some embodiments, the saturation threshold of the derived second image may be determined iteratively. For example, an initial saturation threshold of the second image may be selected based on any of a variety of considerations. The pixels of the first image may then be mapped accordingly to the "initial" second image, and the image brightness of the resulting initial second image may be determined. If the initial image brightness exhibits an appropriate relationship to the initial saturation threshold, then a sign evaluation in the initial second image may then be performed. Conversely, if an appropriate relationship between the initial image brightness and the initial saturation threshold is not achieved, then a subsequent saturation threshold of the second image may be selected, and the pixels of the first image may be remapped accordingly to the "subsequent" second image. The image brightness of the subsequent second image, and the relationship between the subsequent image brightness and the subsequent saturation threshold, may then be determined.

[0037] As with the initially derived image, if the subsequent image brightness exhibits an appropriate relationship to the subsequent saturation threshold, then evaluation of the symbols in the subsequent second image may then proceed. However, if an appropriate relationship has not yet been obtained, then further selection of saturation thresholds, mapping of first image pixels, and evaluation of image brightness may proceed. In this regard, for example, the subsequent second image may effectively become a new initial second image, which may be succeeded by a new subsequent second image having a new subsequent saturation threshold.

[0038] In some embodiments, the saturation threshold of the derived second image may be determined without iteration (such as the example iteration described above). For example, the image brightness of the first acquired (or other) image may be determined, and then a saturation threshold that exhibits an appropriate relationship to the determined image brightness may be selected. The selected saturation threshold may then be set as the saturation threshold of the second image, and the pixels of the first image may be remapped to the second image accordingly. Notably, in some implementations, such iterations may be implemented using only a single acquired image (i.e., without requiring the acquisition of subsequent images, such as may be impossible for moving parts).

[0039] In some embodiments, the availability of a higher bit depth imaging system may make it possible, among other things, to achieve a target relationship between image brightness and saturation levels (e.g., as specified in the AIM DPM) based only on the acquisition of a single image. For example, evaluating a symbol on an object may include acquiring a first image of the object (including the symbol) exhibiting a first bit depth (e.g., 12 or 16 bits). The first image may be analyzed to determine a mapping of pixels of the first image to a second bit depth (e.g., 8 bits) less than the first bit depth. A second image having a second bit depth may then be generated based on the first image and the determined mapping, and may be evaluated to determine at least one quality attribute of the symbol. Accordingly, in these and other embodiments, it is possible to evaluate a symbol without acquiring multiple images or without iterative prior adjustment of system response, such as specified by the AIM DPM.

[0040] In some embodiments, after acquiring the first image using the first larger bit depth, the first image can be analyzed in various ways to determine the mapping of pixels to the second bit depth. In some implementations, for example, one or more of the following operations can be performed. A dynamic range can be determined for the first image, and a set of bright pixels and a set of dark pixels can be identified, such as specified by, for example, AIM DPM. An average intensity value of the bright pixels can be determined, and a saturation threshold can then be determined based on the average intensity value. Pixels with intensity values ​​exceeding the saturation threshold can be set to a specific value (e.g., a maximum brightness value), and the mapping of pixels from the first bit depth to the second bit depth can be determined based on the determined saturation threshold or the average bright pixel intensity value. Alternatively, in some embodiments, the saturation threshold can be determined so that after pixel mapping in view of the determined saturation threshold, the average intensity value of the bright pixels exhibits an appropriate relationship to the determined saturation threshold.

[0041] In some implementations, the analysis of the first image may be performed similarly to the AIM DPM iterative analysis in this regard. However, in some embodiments, in contrast to AIM DPM, the analysis according to the present invention may require only the acquisition of a single image, i.e., may not necessarily require the acquisition of a second image of the symbol to be evaluated.

[0042] Figure 1 An example system 20 for evaluating a symbol 22 on an object 24 is shown according to one embodiment of the present invention. In the embodiment shown, the symbol 22 is a flat DPM 2D barcode, and the object 24 is a parallelepiped box. In other embodiments, other configurations are possible. For example, the object to be imaged may have a variety of geometric shapes, and a variety of symbols may be imaged and analyzed, including dot-peen or other DPM symbols as well as non-DPM symbols.

[0043] exist Figure 1 In the illustrated environment, the object 24 is disposed on a conveyor 26 that is configured to move the object 24 through the system 20 at a predictable and sustained rate. In other embodiments, the object may be moved into or through the system in various ways in accordance with the present invention. In some embodiments, the object may be stationary relative to the evaluation system. In some embodiments, the evaluation system may be configured to be mobile relative to the object and symbol to be evaluated.

[0044] In general, a system according to the present invention may include an imaging device and a processor device. Figure 1 , system 20 includes an imaging system 28 that includes a processor device 30 and an imaging device 32 having a field of view (“FOV”) 34 that includes a portion of conveyor 26. Accordingly, imaging system 28 may be configured to capture one or more images of object 24 as it is moved by conveyor 26.

[0045] The imaging device 32 and the processor device 30 can be configured in various ways, including an arrangement of one or more electronic imaging sensors and one or more lens assemblies, and an arrangement of a programmable general purpose computer device or a machine vision computer, respectively. In some embodiments, the imaging device and the processor device can be part of a physically separate system. For example, the imaging device can be configured to communicate with a remotely located processor device (e.g., a cloud-based computing system) to facilitate the execution of certain operations in the implementation of the present invention.

[0046] In some embodiments, an imaging device may be configured to acquire an image having a first bit depth, and an associated processor device may be configured to analyze the image having a second bit depth less than the first bit depth, including evaluating symbols within the image. In different embodiments, different relative scales for the first bit depth and the second bit depth are possible. For example, in Figure 1 In the embodiment shown in , the imaging device 32 is configured to acquire a 12-bit image of the FOV 34, and the processor device 30 is configured to analyze an 8-bit image. Even if a particular implementation includes analyzing an image having a bit depth less than that acquired by the imaging device, in some implementations, the processor device may be able to analyze an image having the same bit depth as might be acquired by the associated imaging device or to analyze an image having a bit depth greater than that might be acquired by the associated imaging device. In some embodiments, different acquisition and processing bit depths are possible, including in arrangements where the processor device is configured to process the full bit depth of the associated imaging device (e.g., such that a derived second image may exhibit the same bit depth as the acquired first image).

[0047] In some embodiments, it is useful that the bit depth for image acquisition and the bit depth for image analysis be selected so that images can be easily processed over a wide range of imaging parameters (e.g., dynamic range, average brightness, etc.). For example, as also discussed below, the bit depth for image acquisition, along with appropriate system response, can be selected so that the expected brightest images can be captured without over-saturation, and so that the expected darkest images can be captured with sufficient resolution to allow for their proper analysis. Similarly, the bit depth for image analysis can be selected so that images with a range of brightness distributions can be usefully analyzed, including after scaling the images down to a relatively smaller bit depth.

[0048] In this regard, for example, the imaging device 32 may be configured to acquire a 12-bit image of the object 24 including the symbol 22. For example, as the object 24 enters the FOV 34, the imaging device 32 may acquire an image having a predetermined system response (e.g., a predetermined aperture value and shutter speed setting). The processor device 30 may then generate a second (non-acquired) image from the first (acquired) image, such as a second image having an 8-bit or other reduced bit depth. For example, the processor device 30 may be configured to map relevant pixels from the first acquired image to the derived second image, including as described in further detail below, so as to provide a target relationship between the saturation level of the second image and the image brightness (e.g., a ratio as specified in the AIMDPM).

[0049] Once generated, the second (e.g., lower bit depth) image can then be analyzed to facilitate evaluation of the symbol. For example, the generated second image can be analyzed to facilitate evaluation of one or more quality attributes of the symbol therein, including quality attributes specified in the AIM DPM or other relevant guidelines.

[0050] In general, images generated under embodiments of the present invention may include the same subject matter and similar information as the acquired images. However, the generated images may exhibit somewhat lower resolution relative to the acquired images, such as may result from reduced bit depth. However, with appropriate training, sufficient resolution can generally be maintained to allow appropriate analysis of the acquired images (via the generated images), including for evaluation of symbols therein, subject to the inherent limitations of the captured bit depth, and to the minimum and maximum system responses required for the relevant application.

[0051] As also mentioned above, in some embodiments, the processor device may be configured to determine a mapping from a first acquired (e.g., larger bit depth) image to a second derived (e.g., smaller bit depth) image based on an analysis of the acquired image. For example, the processor device 30 may be configured to determine a mapping from the acquired image to the generated image based on determining an appropriate saturation threshold for the derived image, setting pixels of the acquired image that exceed the saturation threshold to a maximum value, and then mapping pixels of the acquired image that are below the saturation threshold to corresponding brightness levels in the derived image. In this way, for example, the processor device 30 can help ensure that relevant information from the acquired image is mapped to the generated image while giving less emphasis to potentially redundant pixels.

[0052] As also mentioned above, in some embodiments, an appropriate saturation threshold for the derived image may be determined based on image brightness analysis, the appropriate saturation threshold causing pixels in the acquired image that exceed the determined saturation threshold to be mapped to a maximum value in the derived image. Figure 2 As shown in FIG. , the imaging device 32 (see Figure 1 ) has acquired a first image as represented in the histogram 40. Using the processor device 30 (see Figure 1 ), an initial saturation threshold 42 may be determined for the derived second image. Pixels of the first image may then be mapped to the initial derived image, as represented in a histogram 44, where pixels 46 of the first image that exceed the saturation threshold 42 (also as shown in histogram 40) have been mapped to the maximum value of the initial derived image (as shown). The image brightness (such as the average brightness 48 of the bright pixels 50) of the initial derived image (including the mapped pixels 46) may then be determined and compared to the saturation threshold 42 to assess the relationship between these factors.

[0053] If an appropriate relationship between average brightness 48 and saturation threshold 42 has been achieved, such as a target ratio specified by the AIM DPM (e.g., approximately 77 / 100), the initially derived image (as represented in histogram 44) may then be used to evaluate symbol 22. If not, a new saturation threshold may be determined, such as subsequent saturation threshold 52 (e.g., see histogram 40), to attempt to better establish the desired relationship.

[0054] In this regard, as also discussed below, an iterative process may be employed. For example, because pixel remapping based on a selected saturation threshold may affect the resulting average brightness (or other brightness measurement) of bright pixels, a series of operations may sometimes be iteratively performed, including: determining successive saturation thresholds; remapping pixels and calculating the resulting corresponding brightness measurements; and then evaluating whether the current saturation threshold exhibits an appropriate relationship to the brightness measurement associated with the remapped pixels, or whether another saturation threshold (and corresponding pixel mapping) should be selected.

[0055] Once the saturation threshold 52 has been determined, the pixels of the first image (including the pixels 56 that exceed the saturation threshold 52) may then be remapped to a subsequently derived image (as represented in the histogram 54), and an evaluation of the image brightness (e.g., determined as the average bright pixel brightness 58) and an evaluation of the relationship of the image brightness to the saturation threshold 52 may be performed. Likewise, if the appropriate relationship between the brightness 58 and the saturation threshold 52 has been achieved, the corresponding (subsequent) derived image may then be used to evaluate the symbol 22. If not, a similar iterative process may continue using a newly selected saturation threshold (not shown) and corresponding pixel mapping, etc.

[0056] Initial and subsequent saturation thresholds for the exported image can be determined in a variety of ways. In some implementations, for example, the system can step through a predetermined set of saturation thresholds at lower and higher ranges (e.g., within a relevant dynamic range), such as, for each saturation threshold in the predetermined set, by subsequently mapping pixels, evaluating image brightness, and evaluating the relationship of image brightness to the saturation threshold. In some implementations, the subsequent evaluation can be terminated if, for example, a target relationship is established for any particular saturation threshold, or based on other relevant criteria such as a subsequent saturation threshold would result in a worse correspondence with the target relationship than a previous saturation threshold.

[0057] Instead, in some implementations, the system may appropriately perform a search (such as a binary search) to streamline the determination of appropriate saturation thresholds and pixel mapping. For example, an initial saturation threshold may be determined, such as by identifying a saturation threshold at the midpoint of a relevant luminance range, and a corresponding analysis (e.g., as described above) may then be completed. Appropriately, a new saturation threshold may then be identified based on the initial analysis indicating whether a higher saturation threshold or a lower saturation threshold is required to obtain the target relationship. For example, if a higher (or lower) saturation threshold is required, a new saturation threshold may be identified that is intermediate between the previous saturation threshold and the upper end (or lower end) of the relevant range. In other implementations, other types of searches are also possible.

[0058] In some implementations, an appropriate saturation threshold may be determined based on an analysis of pixel intensities in the acquired image without necessarily considering the effect on image brightness of the derived image by mapping those pixels to the derived image. Figure 3 An example of this type of analysis, as performed by system 20 (see Figure 1 ). In the illustrated implementation, for example, the imaging device 32 has acquired a 12-bit image. Accordingly, the processor device 30 has determined the dynamic range 60 of the image and has generated a histogram of the image, such as Figure 3 (Using this and other examples, one skilled in the art will recognize that the processor device 30 may not necessarily generate a graphical representation, but may perform certain tasks that are entirely digital, such as by storing a list of pixel counts and performing calculations on them.)

[0059] for Figure 3 , the dynamic range 60 is much smaller than the possible maximum intensity 62. Accordingly, some information in the 12-bit image may not be particularly useful or relevant to the desired image analysis (e.g., symbol evaluation). Embodiments of the present invention are generally able to eliminate portions of this information from reduced bit depth (or other derived) images used for analysis (including as discussed above and below).

[0060] In some implementations, the dynamic range 60 is identified and a histogram count is generated (eg, Figure 3 ), the processor device 30 can interrogate the resulting bimodal distribution to identify a set of bright pixels 66 and a set of dark pixels 64. An average bright pixel value 68 (e.g., average intensity) can be determined from the analysis of the bright pixels 66, and a saturation threshold 70 can then be determined based on the average bright pixel value 68.

[0061] In some implementations, the saturation threshold 70 may be determined as a predetermined multiple (e.g., approximately 100 / 77) of the average bright pixel value 68. In this manner, for example, the average bright pixel value 68 may be approximately 77% of the saturation threshold 70. In other implementations, other approaches are possible.

[0062] After the saturation threshold 70 has been determined, pixels 72 exceeding the threshold 70 may be set to a predetermined value. For example, in some embodiments, the pixel 72 may be set to the maximum intensity value within the saturation threshold 70, which may result in an increase in the pixel count at the maximum intensity range under the threshold 70, as determined by Figure 3 Pixels 76 of the acquired image that are below the saturation threshold may then be mapped to a lower bit depth of a determined image that may be analyzed to evaluate symbol 22 (see Figure 1 ).

[0063] As also mentioned above, the selection of a particular value for saturation threshold 70 and the corresponding remapping of pixels 72 may result in a change in average brightness (or other measure of image brightness) relative to the initially acquired image. Accordingly, in some cases, the selection of an initial saturation threshold (such as threshold 70) may result in an average image brightness that does not properly exhibit a desired relationship (e.g., ratio) to the initial saturation threshold. In this case, for example, as also discussed above, iterative selection of one or more subsequent saturation thresholds may then be performed, along with corresponding remapping of pixels and calculation of updated brightness measurements, until an appropriate relationship has been achieved.

[0064] In some implementations, measures of image brightness other than the average (e.g., mean) intensity of bright pixels may be used. For example, in some implementations, processor device 30 may be configured to determine the magnitude of variation or other statistical measure of bright pixels, and may then determine saturation threshold 70 (or other mapping parameter) accordingly. Likewise, in some implementations where a higher dynamic range is preferred, such as for reading applications (e.g., as opposed to evaluation applications), other measures of image brightness may be beneficially used.

[0065] In some implementations, the operations discussed herein may be performed in a different order than explicitly presented. For example, in some implementations, pixel 72 may be set to a predetermined value(s) after pixel 76 is mapped to a lower bit depth (or other derived) image.

[0066] Figure 4 Another example of the analysis process is shown. Figure 1 In the illustrated implementation, the imaging device 32 acquires another 12-bit image. Correspondingly, the processor device 30 has determined the corresponding dynamic range 80 of the image and has generated a histogram of the image, such as Figure 3 (Again, those skilled in the art will recognize that the processor device 30 may not necessarily generate a graphical representation, but may perform certain tasks entirely digitally, such as by storing a list of pixel counts and performing calculations on them.)

[0067] for Figure 4 For a particular 12-bit image represented in FIG. 8 , the dynamic range 80 is approximately equal to the maximum possible intensity 82 of the image. Accordingly, the relatively small amount of information in the 12-bit image may be irrelevant or useless for the desired image analysis (e.g., sign evaluation). In this regard, for example, it can be seen that the acquisition of a larger bit depth image (which can accommodate Figure 4Intensities in the images represented in ( ) can allow for high resolution acquisition of images with a wider dynamic range. Further, as also discussed above, the reduction in bit depth used for analysis can still allow for reliable and high quality analysis of these same images, including consistency with various aspects of AIM DPM. Likewise, in contrast to conventional approaches, analysis under some embodiments of the present invention can be performed based on the acquisition of only a single image.

[0068] For example, in some implementations, the dynamic range 80 is identified and a histogram count is generated (eg, Figure 4 ), the processor device 30 can interrogate the resulting bimodal distribution to identify a set of bright pixels 86 and a set of dark pixels 84. A statistical measure of the bright pixels (such as an average bright pixel intensity 88) can be determined from the analysis of the bright pixels 86, and a saturation threshold 90 can then be determined based on the average 88.

[0069] In some implementations, the saturation threshold 90 may be determined as a predetermined multiple (e.g., approximately 100 / 77) of the average bright pixel value 88. In this manner, for example, the average bright pixel value 88 may be approximately 77% of the saturation threshold 90. In other implementations, other approaches are possible. In other implementations, other multiples (or ratios) may be appropriate. In some implementations, such as with respect to Figure 2 As discussed, a saturation threshold for a derived image may be similarly determined, but also based on a calculation of the image brightness after the pixels of the acquired image have been mapped to the derived image, and taking into account any brightness cutoff imposed by the determined saturation threshold.

[0070] After the saturation threshold 90 has been determined, pixels exceeding the threshold 90 may be set to a predetermined value that may be encompassed by the threshold 90. However, in the illustrated implementation, bright pixels are sufficiently concentrated so that only a few pixels or no pixels in the acquired image exceed the saturation threshold 90. Accordingly, it may not be necessary to set the intensity value of any pixel to a new value, except, for example, when it may be necessary to map the pixels 96 of the acquired image to a lower bit depth (e.g., in a scaling operation). The mapped lower bit depth image may then be analyzed to evaluate the symbol 22 (see Figure 1 ).

[0071] In some implementations, it may be useful to train the system for evaluating symbols prior to acquiring images for evaluation or at other times (including dynamically during runtime operation). This may be useful, for example, to prevent oversaturation of acquired images for the brightest objects.

[0072] In different implementations, different training approaches may be used, including those that appropriately optimize the balance between resolution and dynamic range. Figure 5 It is shown that the system 20 (see Figure 1 ) various aspects of examples of training operations performed. In other implementations, other operations are possible.

[0073] exist Figure 5 In the implementation shown in FIG. 1 , the imaging device 32 (see Figure 1 ) may be configured to acquire at least two training images: a bright image training image and a dark image training image. Specifically, the bright image training image (as represented in histogram 100) may be configured to acquire an image that is approximately representative of the maximum expected image brightness of the environment of interest. For example, the training objects or scenes may be arranged so that the imaging device 32 will be exposed to the brightest specular reflections or overall lighting that can reasonably be expected during runtime operation of the system 20. Conversely, the dark image training image (as represented in histogram 102) may be configured to represent the minimum expected image brightness of the environment of interest. For example, the training objects or scenes may be arranged so that the imaging device 32 will be exposed to the darkest specular reflections or overall lighting that can reasonably be expected during runtime operation of the system 20.

[0074] Once acquired, the light image or dark image training images may be analyzed to facilitate determining an appropriate operating system response 104 (i.e., a system response of the imaging device 32 during runtime operation) of the imaging device 32. This may be useful, for example, to ensure that the possible dynamic range of the imaging device 32 with respect to the light image training images, as well as the appropriate resolution of relevant features in the dark image training images, is optimally (e.g., substantially) fully utilized.

[0075] If an appropriate balance between these (or other) factors is achieved, the system response used to acquire the training images can then be set as the operating system response. Conversely, if an appropriate balance is not achieved, the operating system response can be determined after the system response used for training has been adjusted, including by computational adjustment (e.g., based on a lookup table or prior training or runtime work), or by acquiring and analyzing one or more additional dark image or light image training images using the updated system response.

[0076] In some implementations, light image training images may be acquired with a different system response than dark image training images. The operating system response is determined based on comparative analysis of the training images (or system responses) or based on iterative acquisition of training images with convergent system responses.

[0077] In some implementations, iterative training operations are possible. For example, as described above, some implementations may include iteratively acquiring training images with different system responses in order to identify appropriately optimized operating system responses.

[0078] In some implementations, the operating system response can be determined so as to provide an appropriate relationship between a statistical measure of bright pixels of the bright image training image and a maximum intensity acquired by the operating image provided by the bit depth. For example, the operating system response 104 can sometimes be determined as a system response that ensures that the average intensity value of bright pixels in the bright image training image (e.g., as shown in the histogram 100) is substantially equal to a predetermined fraction (e.g., approximately 77%) of the maximum brightness possible for the first training image (and imaging systems in general). In this way, for example, the operating system response 104 can be selected to ensure that the maximum expected brightness during runtime operation can be recorded while substantially fully utilizing the first bit depth and without oversaturation. In this regard, operation as described above can also ensure that darker acquired images are also appropriately analyzed (e.g., by appropriately scaled mapping of the acquired images to lower bit depths).

[0079] In some implementations, the operating system response can be determined so as to provide an appropriate resolution of features represented in the associated dark image training images (e.g., as shown in histogram 102). For example, if the system response used to optimize the imaging bit depth of the bright image training images (e.g., as shown in histogram 100) results in an unacceptably low resolution of the dark image training images, the operating system response 104 can be adjusted accordingly. In this regard, for example, in order to obtain an appropriate resolution for the darkest desired image, the optimized operating system response can sometimes be determined so as to produce some oversaturation for the brightest desired image.

[0080] In some implementations, training can be performed prior to runtime operations. In some implementations, training can be performed as part of runtime operations. For example, in some implementations, imaging system 28 (see Figure 1 ) can record candidates for bright image and dark image training images during runtime operation. When appropriate, imaging system 28 can update the operating system response of imaging device 32. For example, imaging system 28 can be configured to analyze multiple bright image or dark image training images similar to the above method to appropriately balance image saturation, image resolution, or other relevant factors.

[0081] In some embodiments, two imaging devices may be used, with the first imaging device being configured to acquire an initial image and the initial image being used to guide the system response of the second imaging device selected to acquire an image for symbol evaluation. Figure 11 ) is configured to capture a test image of the object 24 before the imaging device 32 captures an image for evaluating the symbol 22. The brightness (or other aspects) of the test image can then be analyzed to determine an appropriate system response for image acquisition by the imaging device 32, including based on the operating bit depth of the imaging device 32 or other relevant considerations. For example, the brightness of the test image can be analyzed in the context of the system response and other known characteristics of the imaging device 110 and the known characteristics of the imaging device 32 to determine an appropriate system response for the imaging device 32 to capture an appropriate image. In this way, for example, acquiring a test image by the imaging device 110 can help support the use of the imaging device 32 to acquire a first image exhibiting appropriate characteristics. In some implementations, such an image acquired by the imaging device 32 can then be mapped to a derived image based on an appropriately determined saturation threshold, as described above.

[0082] Consistent with the above discussion, some embodiments of the present invention may include computer-implemented methods, including methods performed by software or hardware modules of symbol evaluation or other (e.g., general machine vision) systems. In this regard, for example, a method that may include one or more of the operations discussed above or below may be performed by a module of system 20 (see Figure 1 ) or implemented by modules of other systems.

[0083] As an example, Figure 6 As shown, embodiments of the present invention may include a method 200 for evaluating a symbol on an object. In some implementations, the method 200 may be performed at least in part using an imaging device and a processor device (such as the imaging device 32 and the processor device (see FIG. 20 ) of the system 20. Figure 1 )) to achieve it.

[0084] In other operations, method 200 includes 202, using an imaging device to obtain a first image of an object, including a symbol. In some cases, the first image obtained in 202 may exhibit a specific bit depth 204, which may sometimes support the generation of a lower bit depth derived image (as described above).

[0085] After acquiring the first image in 202, the method further includes generating, using a processor device, a second image derived from the first image at 210. In the illustrated implementation, for example, generating the derived second image at 210 includes determining a saturation threshold for the second image at 212, and mapping pixels of the acquired image to the derived image at 214, including mapping pixels of the acquired image that exceed the saturation threshold determined in 212 to a maximum value at 216. In some implementations, as described above, the second image generated at 210 may exhibit a bit depth that is less than bit depth 204.

[0086] In some implementations and as described above, it is useful to determine the saturation threshold in 212 based on a target relationship 218 between the saturation threshold and image brightness. For example, in some implementations, method 200 includes 212, i.e., determining the saturation threshold so that a predetermined ratio (e.g., approximately 77 / 100) is achieved between the brightness of the second image generated in 210 and the saturation threshold, including after the above-mentioned 214, 216, i.e., mapping pixels.

[0087] In the illustrated implementation, once the properly derived image is generated at 210, method 200 further includes evaluating the symbol in the second image at 220. For example, an associated processor device may evaluate the symbol at 220 to verify that the symbol has been properly printed or marked, or for other reasons.

[0088] In some implementations, for method 200 and others, it may be useful to determine a saturation threshold and map pixels of a particular region of interest of an image. In some cases, such a region of interest may include only a portion of the image of interest. For example, for image evaluation primarily for evaluating symbols in 220, it may be useful to identify a region of interest of the image that contains the symbol of interest but does not include certain other portions of the image. Then, a saturation threshold may be determined in 212, and pixels of only (or primarily) the region of interest of the image may be mapped accordingly in 214, rather than the entire image. In some cases, an image derived based on the determined saturation threshold and the corresponding pixel map may include only (or primarily) pixels from the region of interest in the acquired image.

[0089] In some implementations, the derived image can be presented to the user for various additional purposes. For example, the second image generated in 210 based on the appropriately determined saturation threshold in 212 and the corresponding pixel map in 214 can be more visually useful to the user than the initial image, which may be substantially oversaturated, or may have meaningful pixels concentrated over a relatively small (and, e.g., dark) range. In this regard, the second image generated in 210 can provide a useful visual display to the user, such as allowing the user to better understand the content and value of the relatively dark (or other) initial image, and be useful for evaluation of the symbol in 220 or other purposes.

[0090] In some implementations, determining the saturation threshold at 212 may include a potentially iterative process. Figure 7 In the illustrated implementation, for example, determining the saturation threshold in 212 includes first identifying an initial saturation threshold in 230. As described above, the initial saturation threshold in 230 can be identified in a variety of ways, including based on identifying a lower or upper limit of a preset range of saturation thresholds, a midpoint or other intermediate point in the range, or in other ways.

[0091] After the initial saturation threshold is identified at 230, the pixels of the first image acquired at 202 may be mapped to an initial (derived) second image at 232 (see also Figure 6 A brightness measurement of the initial second image may then be determined at 234. For example, the average bright pixel brightness of the initial second image may be determined at 234 using one or more of a variety of known methods, such as those taught in AIM DPM.

[0092] To determine whether the initial second image exhibits appropriate characteristics, a relationship between the brightness measurement and the initial saturation threshold may then be determined at 236. For example, processor device 30 may determine at 236 a ratio of the brightness measurement determined at 234 to the saturation threshold determined at 230.

[0093] If the relationship determined in 236 substantially corresponds to a target predetermined relationship, such as a predetermined ratio (e.g., approximately 77 / 100), then the initial second image may be set as the final second image in 238 and the signs in the derived image may be appropriately evaluated in 220 (see Figure 6 ). If, on the other hand, the relationship determined in 236 does not substantially correspond to the predetermined relationship, the process may be repeated. Figure 7 The processing shown in , and identifying a subsequent saturation threshold at 230, mapping pixels to a subsequent (derived) second image at 232 based on the subsequent saturation threshold, evaluating the relationship between the resulting image brightness and the saturation threshold, and so on.

[0094] In this regard, for example, as also discussed above, an iterative process may be employed. For example, because the remapping of pixels based on the saturation threshold identified in 230 may affect the resulting average brightness (and other brightness measurements) of bright pixels, a series of operations may be performed to identify successive saturation thresholds in 230, map pixels accordingly in 232, determine the resulting individual brightness measurements in 234, and then evaluate whether the relationship determined in 236 is appropriate or whether another saturation threshold should be identified in 230.

[0095] As another example, Figure 8 As shown, embodiments of the present invention may include a method 300 for evaluating a symbol on an object. In some implementations, the method 300 may be performed at least in part using an imaging device and a processor device (such as the imaging device 32 and the processor device (see FIG. 20 ) of the system 20. Figure 1 )) to achieve it.

[0096] In other operations, method 300 includes 302, that is, obtaining a first image of an object, including a symbol, using an imaging device. Specifically, the image can be obtained at a first bit depth 304 (e.g., a bit depth of 12 bits or greater) in 302. To facilitate further analysis, method 300 also includes 310, that is, generating a second image from the first image using a processor device, wherein the second image includes a second bit depth 312 (e.g., a bit depth of 8 bits or less) that is less than the first bit depth 304. In some aspects, this may include 330, determining a mapping of pixels of the first image to pixels of the second image, and implementing the mapping determined in 330. If appropriate, the second image can then be evaluated using the processor device in 320, including determining at least one quality attribute of the symbol.

[0097] As described above, in some embodiments, the second image may be generated in 310 based on determining a mapping of the first image to the second bit depth in 330. In some embodiments, the mapping may be determined in 330 based on determining an average value (or other brightness measurement) of bright pixels of the first image or a saturation threshold in 332. For example, as described above, pixels exceeding the determined saturation threshold may be set to a predetermined value (e.g., a maximum value), and pixels below the saturation threshold may be mapped to a lower bit depth.

[0098] In some embodiments, the saturation threshold may be determined based on a statistical analysis of the first image at 332. For example, the processing device may be configured to determine a dynamic range of the first image at 334, divide the first image into a set of bright pixels and a set of dark pixels at 336, and then determine an average value (e.g., a mean value) of the set of bright pixels at 338. The saturation threshold may then be determined at 332 based on the mean value determined at 338, such as by multiplying the mean value determined at 338 by a predetermined multiple (e.g., approximately 100 / 77).

[0099] In some implementations, one or both of methods 200, 300 may include or be implemented in conjunction with a training method, such as Fig. 9 400. In some implementations, the operations of method 400 may be performed before the operations of any of methods 200 and 300. In some implementations, the operations of method 400 may be performed simultaneously with the operations of any of methods 200 and 300 or after the operations of any of methods 200 and 300. In some implementations, similar to methods 200 and 300, method 400 may at least partially utilize an imaging device and a processor device (such as imaging device 32 and processor device (see system 20)). Figure 1 )) to achieve it.

[0100] exist Fig. 9In the illustrated embodiment, method 400 includes 402, that is, determining a bright image system response of an associated imaging device. Specifically, for example, the bright image system response can be determined so as to appropriately configure the imaging device to use the first bit depth 304 (see Figure 8 ) captures bright image training images that represent the maximum expected brightness during runtime operation.

[0101] In some implementations, the bright image system response can be determined at 402 based on a target statistical (e.g., average intensity) value 404 of the bright pixels of the bright image training image and a target saturation threshold 406. For example, the target saturation threshold 406 can be set to be substantially equal to the bright image training image (or Figure 8 The maximum intensity value of the bit depth of the first image acquired at 302 of FIG. 40 is set. Similarly, the target statistical (e.g., average intensity) value 404 for bright pixels can be set to exhibit an appropriate relationship to the maximum intensity value (e.g., approximately 77% thereof). Then, the bright image system response can be determined at 402 as a system response that can generally ensure that the actual statistical (e.g., average intensity) value of the bright pixels in the bright image training image is substantially equal to the target statistical (e.g., average intensity) value 404.

[0102] And in Fig. 9 In the illustrated embodiment, method 400 includes determining a dark image system response of an imaging device at 410. Specifically, for example, the dark image system response may be determined so that the imaging device captures a dark image training image representing a minimum desired brightness during runtime operation at an appropriate resolution. For example, a target resolution 412 may be determined for properly analyzing symbols at runtime, and the dark image system response may be determined at 410 to ensure that the target resolution 412 is generally achieved.

[0103] Continuing, method 400 includes 420 determining, based on one or more of the light image or dark image system responses, a method for acquiring a first (larger bit depth) runtime image (eg, Figure 8 The operating system of the imaging device responds to the image acquired by 302. For example, the processor device 30 (see Figure 1 ) may be configured to determine, at 420, an operating system response corresponding to the bright image system response or the dark image system response determined at 402, 410 based on an appropriate balance of a target resolution 412 for darker images and a target saturation threshold 406 for brighter images or a statistical value 404 for bright pixels.

[0104] Although the present invention has been described with reference to exemplary embodiments, it will be appreciated by those skilled in the art that various changes may be made and elements substituted with equivalents without departing from the scope of the present invention. In addition, those skilled in the art will appreciate the many modifications that may be used to adapt a particular device, situation, or material to the teachings of the present invention without departing from the essential scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed as the best mode for carrying out the present invention, but the present invention will include all embodiments that fall within the scope of the appended claims.

Claims

1. A system for evaluating a symbol on an object, characterized in that Including imaging systems, The imaging system comprises: Imaging equipment; and a processor device in communication with said imaging device, The imaging device is configured to acquire a first image of the object, including the symbol, The processor device is configured to generate a second image derived from the first image based on: determining a saturation threshold of the second image; and mapping pixels of the first image to the second image, including mapping pixels of the first image having values ​​exceeding the saturation threshold to a maximum value of the second image such that a brightness measurement of the second image exhibits a predetermined target relationship to the saturation threshold of the second image, The saturation threshold of the second image is determined based on the predetermined target relationship between the saturation threshold of the second image and a brightness measurement of the first image or the second image.

2. The system according to claim 1, characterized in that The brightness measurement of the mapped pixel is determined based on: dividing the mapped pixels into bright pixels and dark pixels; and Determine the average value of bright pixels.

3. The system according to claim 1, characterized in that The first image has a first bit depth, and the second image has a second bit depth that is less than the first bit depth.

4. The system according to claim 1, characterized in that The saturation threshold of the second image is determined based on the predetermined target relationship between the saturation threshold of the second image and a brightness measurement of the second image, and determining the saturation threshold of the second image and mapping pixels of the first image includes: Identify the initial saturation threshold; First, mapping pixels of the first image to an initial second image includes mapping pixels of the first image having values ​​exceeding the initial saturation threshold to an initial maximum value; determining an initial brightness measurement of the initial second image and an initial relationship between the initial brightness measurement and the initial saturation threshold; and If the initial relationship deviates substantially from the predetermined target relationship, then: identifying subsequent saturation thresholds; subsequently mapping pixels of the first image to a subsequent second image, comprising mapping pixels of the first image having values ​​exceeding the subsequent saturation threshold to a subsequent maximum value; and A subsequent brightness measurement of the subsequent second image and a subsequent relationship between the subsequent brightness measurement and the subsequent saturation threshold are determined.

5. The system according to claim 4, characterized in that The subsequent saturation threshold is determined based on a predetermined set of saturation thresholds.

6. The system according to claim 4, characterized in that The subsequent saturation threshold is determined based on the initial relationship between the initial brightness measurement and the initial saturation threshold.

7. The system according to claim 1, characterized in that The saturation threshold of the second image is determined based on the predetermined target relationship between the saturation threshold of the second image and a brightness measurement of the first image, and determining the saturation threshold of the second image includes: determining a brightness measurement of the pixels of the first image; and The saturation threshold is determined based on the brightness measurement of the pixel of the first image and the predetermined target relationship.

8. The system of claim 1, wherein: The predetermined target relationship specifies a predetermined ratio between the saturation threshold of the second image and the brightness measurement of the mapped pixel.

9. The system according to claim 1, characterized in that The processor device and the imaging device are further configured to perform a training operation, the training operation comprising: obtaining a first training image representing a maximum desired image brightness; acquiring a second training image representing a minimum desired image brightness; and A system response of the imaging device is determined for acquiring the first image based on one or more of the first training image and the second training image.

10. The system according to claim 1, wherein: The imaging device is a first imaging device, and the system further comprises: a second imaging device, wherein the second imaging device is configured to acquire a test image of the object before the first imaging device acquires the first image; and Wherein the first imaging device is configured to capture the first image using a system response determined based on a characteristic of the test image.

11. A system for evaluating a symbol on an object, the system comprising: Imaging equipment; as well as a processor device in communication with said imaging device, The imaging device is configured to acquire a first image of the object, including the symbol, at a first bit depth, and The processor device is configured to generate a second image derived from the first image based on: determining the saturation threshold of the second image based on a predetermined target relationship between the saturation threshold of the second image and a brightness measurement of at least one of the first image or the second image; as well as Pixels of the first image are mapped to the second image based on the saturation threshold of the second image.

12. The system according to claim 11, characterized in that The symbol is a DPM symbol and the object is a moving object.

13. The system according to claim 11, characterized in that The brightness measurement of at least one of the first image or the second image, respectively, is determined based on: dividing at least one of the first image or the second image into bright pixels and dark pixels; and The respective average values ​​of the bright pixels are determined.

14. The system according to claim 11, characterized in that The predetermined target relationship specifies a predetermined ratio between the saturation threshold of the second image and the brightness measurement of the second image.

15. The system of claim 11, wherein: The processor device and the imaging device are further configured to perform a training operation before acquiring the first image, the training operation comprising: determining a bright image system response for the imaging device to capture a first training image representing a maximum desired brightness at a target bit depth; determining a dark image system response for the imaging device to capture a second training image representing a minimum desired brightness at a target resolution; and An operating system response of the imaging device used to acquire the first image is determined based on one or more of the light image system response or the dark image system response.

16. A method for evaluating a symbol on an object, the method being implemented at least in part using an imaging device and a processor device, the method being characterized by comprising: acquiring, using the imaging device, a first image of the object, including the symbol, Generating, using the processor device, a second image derived from the first image in the following manner, the manner comprising: determining a saturation threshold for the second image, the saturation threshold for the second image being determined based on a target relationship between the saturation threshold for the second image and a brightness measurement of at least one of the first image or the second image; and Mapping pixels of the first image to the second image includes mapping pixels of the first image having values ​​exceeding the saturation threshold of the second image to maximum values ​​of the second image; and The second image is evaluated with the processor device to determine at least one attribute of the symbol.

17. The method according to claim 16, characterized in that The saturation threshold of the second image is iteratively determined based on identifying a plurality of potential saturation thresholds, and the saturation threshold of the second image is selected from the plurality of potential saturation thresholds according to an evaluation of image brightness respectively provided by each of the plurality of potential saturation thresholds.

18. The method according to claim 16, characterized in that iteratively determining the saturation threshold of the second image, Iteratively determining the saturation threshold of the second image and mapping pixels of the first image to the second image comprises: Identify the initial saturation threshold; first mapping pixels of the first image to an initial second image, comprising mapping pixels of the first image having values ​​exceeding the initial saturation threshold to an initial maximum value; and determining an initial brightness measurement of the initial second image and an initial relationship between the initial brightness measurement and the initial saturation threshold; If the initial relationship substantially corresponds to the target relationship, setting the initial second image as the second image, If the initial relationship deviates substantially from the target relationship, then: identifying subsequent saturation thresholds; subsequently mapping pixels of the first image to a subsequent second image, including mapping pixels of the first image having values ​​exceeding the subsequent saturation threshold to a subsequent maximum value; determining a subsequent brightness measurement of the subsequent second image and a subsequent relationship between the subsequent brightness measurement and the subsequent saturation threshold; and If the subsequent relation substantially corresponds to the target relation, setting the subsequent second image as the second image.

19. The method according to claim 16, characterized in that further comprising performing training operations for image acquisition and analysis, The training operation includes one or more of the following: determining a bright image system response for said imaging device to capture a first training image representing a maximum desired brightness; or determining a dark image system response for the imaging device to capture a second training image representing a minimum desired brightness at a target resolution; as well as An operating system response of the imaging device used to acquire the first image is determined based on one or more of the light image system response or the dark image system response.

20. The method of claim 19, wherein: The bright image system response is determined based on one or more of the following, namely: a target average value of bright pixels in the first training image, the target average value being a predetermined fraction of the maximum recorded brightness of the first training image; or A target saturation threshold corresponding to the maximum recorded brightness for the first bit depth.

Images