Enhanced analysis for image-based serpentine belt wear evaluation

Systems and methods are provided for the improvement of an image of a device under test, such as a belt. The image of device under test is made more optimal by determining if the object is rotated away from a preferred axis of the image frame. If so, the image is rotated an opposing angle such that the object is parallel to the preferred axis of the image frame. The rotated image is then made available for analysis of the object. Rib width analysis is performed along the entire length of the detected rib by either de-rotating the image or not.

FIELD OF THE DISCLOSURE

The present disclosure is generally directed toward measuring belt wear, and more specifically using images to identify belt wear characteristics and predict belt life.

BACKGROUND

Serpentine drive belts are becoming increasingly durable due to the use of Ethylene Propylene Diene Monomer (EPDM) materials. As a result, a historically reliable indicator of belt wear, cracking, occurs less frequently although belts continue to wear over time. One problem that exists due to the use of these advanced materials is that pre-failure wear detection is increasingly difficult to quantify. In other words, serpentine drive belts made of EPDM materials are commonly only diagnosed as excessively worn after a complete failure of the belt.

Recent advances to deal with the above-identified problem require a physical tool that is contacted with a belt being measured. Examples of such tools are described in U.S. Pat. No. 7,946,047 and U.S. Patent Publication No. 2010/0307221 both to Smith et al., each of which are hereby incorporated herein by reference in their entirety. These solutions rely on physical contact between the measurement tool and the belt being measured.

It would be useful to develop a belt measurement solution that does not rely on physical contact between a tool and the belt being measured, and which can quickly and effectively identify belt wear. Further benefits would be realized if such a system reduced the burden of image processing required of an operator of such a system.

SUMMARY

One technique for non-contact measuring of belt wear is described in applicants' co-pending application, application Ser. No. 13/226,266, filed on Sep. 6, 2011, and entitled, MEASUREMENT OF BELT WEAR THROUGH EDGE DETECTION OF A RASTER IMAGE, which is hereby incorporated herein by reference in its entirety for all that it teaches.

A method for determining the orientation of a serpentine belt depicted in a digital photograph, for the purpose of correcting for rotation prior to analyzing the degree of rib wear. By performing digital filtering manipulations of the photograph's gamma, luminance, contrast, hue, color channels and other information, the software will identify parallel, high aspect-ratio, quadrilateral areas of the digital data which will be deemed to represent the longitudinal axes of the belt ribs. The results of this analysis will be used to define the orientation of the belt image within the photograph's field, and establish the perpendicular axis for use in the subsequent analyses.

Additionally, a method for compensating for uneven lighting in a digital photograph of a serpentine belt, for the purpose of accurately identifying the orientation and/or number of belt ribs, prior to analyzing the degree of rib wear. By performing digital filtering manipulations of the photograph's gamma, luminance, contrast, hue, color channels and other information, the software will normalize the contrast levels in various regions of the photograph to prevent differences in edge sharpness from causing the software to incorrectly interpret the data.

Sequence of Operation:

This invention solves a prior art issue of defining edges of belt and analysis of a skewed or not parallel rib profile for improved user interface by processing image range of pixels (resolution) before start of analysis where this step provides an image size that is consistent for all image imputes from any type of smartphone or focal length of the image capture

Step 1 of algorithm: Reduce resolution of image, for example by as much as 1/10thand measure the angles of the belt rib, additionally crop the belt edges as defined

Step 2 of algorithm: Return to full resolution of the image and define rib edges as described below.

To find the orientationMask the belt, crop by removing high contrast areas, additionally analyze the threshold of variations of pixel sizes (neighborhoods) or singularly analyze the thresholds of pixel neighborhoods

Using Adaptive Threshold Open CV Library or Other Library with Equivalent Functionality:

Validate proper size of pixel neighborhood to define the number of ribsProgression of analysis with selection of different pixel areas such as 5, 10, 100 or additional size pixel neighborhoodsAn example of one process or cycle of analysis to determine if black or white—look in the near neighborhood of 35 pixels for adaptive threshold analysis and analyze for fit within a tolerance to a polygon, preferably a polygon with 4, 5, or 6 verticesContinue cycle of analysis of the range of greys to determine black or white image regions of contours where one image has several contoursProcess contours through polygon fit—in pixel regions

Process an area filter of regions greater than 1/50thimage area pixels square or a value of similar size to eliminate non-rib spurious regions and additionally a process method to make the polygon error (tolerance) based on the image pixel size.

Belt orientation image is solved by Cartesian coordinates of longest polygon edges from the primary angles of detected polygons

The above invention presents a belt profile image that is cropped, rotated and presented to algorithms of prior art: Measurement of Belt Wear Through Edge Detection of a Raster ImageStep One Screenshot—image to identify edges of belt [SeeFIG. 1]Step Two Screenshot—image of belt ribs [SeeFIG. 2]Code sampling of invention

One method for accomplishing this utilizes a series of manipulations that will sequentially increase the contrast between adjacent areas of low contrast in poorly-lit areas of the photograph until they are similar in contrast to the well-lit areas of the photograph. These manipulations should be able to exploit as little as one data point of difference in one or all of the data channels in the digital photograph by altering variables such as the radius from the target pixel of the area of adjacent data to be used in the analysis, the degree of added contrast applied, and the threshold of difference that will determine whether the transformation will be applied to the data. This process is similar to a process used in digital photography and printing known as un-sharp masking.

Additionally, a method for determining the number of ribs and/or valleys present in the belt depicted in the digital photograph, for the purpose of informing the analysis software, prior to analyzing the degree of rib wear. Utilizing data representing the parallel quadrilateral areas of the photograph, in conjunction with the data representing the marked rib top a comparison will be made to determine whether these two datasets return a consistent value representing the number of ribs contained in the belt represented in the photograph. If these two data sets do not agree, the marks applied by the user will be used to determine the number of ribs on the belt. Collection and analysis of these two data sets will provide a method for determining the accuracy of the methodology, and to allow further refinements to the software.

Collectively, these improvements will obviate the need for the user to:

1. Orient the photographic capture device in any particular manner

2. Zoom, rotate, center or otherwise manipulate the photo after capture

3. Manually enter the number of belt ribs prior to analysis

4. Eliminate the need to mark the belt

The quality of the acquired image of an object under test may be a factor in the ability or accuracy of a non-contact analysis tool to analyze an object under test, such as a belt. Many image defects can be negated in whole or in part. The human eye can be utilized to identify many image defects associated with an image of a belt. However, in accord with the embodiments and claims herein, a machine-based image correction provides a remedy to many belt-image defects and may also improve the speed and accuracy of the image and analysis thereof.

Advantages in non-contact analysis, such as the measurement of belt wear by analysis of a belt image, may be realized by implementing the embodiments described herein. One advantage is realized by providing a belt measurement application, incorporating automatic correction for certain image-capture defects, such as rotation of the belt relative to the image-capture frame of reference. With the belt image rotated or de-rotated, such that the belt image is made to have a particular orientation to a predefined axis of the imaging frame, the speed and accuracy of the analysis is improved over the image. Therefore, in one embodiment, a machine-based rotation of an image of a belt is provided. Additional embodiments illustrate the ability to further improve the image by performing operations such as cropping, edge detection and/or belt rib detection.

The term “computer-readable medium” as used herein refers to any tangible storage that participates in providing instructions to a processor for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, NVRAM, or magnetic or optical disks. Volatile media includes dynamic memory, such as main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, magneto-optical medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, a solid state medium like a memory card, any other memory chip or cartridge, or any other medium from which a computer can read. When the computer-readable media is configured as a database, it is to be understood that the database may be any type of database, such as relational, hierarchical, object-oriented, and/or the like. Accordingly, the disclosure is considered to include a tangible storage medium and prior art-recognized equivalents and successor media, in which the software implementations of the present disclosure are stored.

The terms “identify”, “determine”, “calculate”, “compute,” and variations thereof, as used herein, are used interchangeably and include any type of methodology, process, mathematical operation or technique.

The term “module” as used herein refers to any known or later developed hardware, software, firmware, artificial intelligence, or combination of hardware and software that is capable of performing the functionality associated with that element. Also, while the disclosure is described in terms of exemplary embodiments, it should be appreciated that individual aspects of the disclosure can be separately claimed.

DETAILED DESCRIPTION

One desired utility of the embodiments described herein are directed towards processing an image of a belt, and more specifically to an image of a portion of a belt, for use by a belt analysis module, application or engine. However, those of ordinary skill in the art will appreciate that, in addition to a belt, other objects under test may benefit from the teachings herein, including, but not limited to, gears, pulleys, idlers, shafts, bearings, blades and support members.

Referring now toFIG. 1, is a first belt image100. Outer edges102and104are identified.

ReferencingFIG. 1Bis first belt image100after adaptive threshold algorithm.FIG. 1Billustrates a second image206with binary and reversed values. First and second belt edges102B and104B are illustrated and form the border between the belt image110and background108.

FIG. 2is an enhanced contrast image200of first belt image100. In the embodiment illustrated, 6 ribs (202A-202F) are identified. Further discussion of rib identification follows below. Referring again to the illustrated embodiment, ribs202A,202B,202C,202D and202F form dark regions that, within a tolerance, form polygons with 4 vertices. Rib202E, forms another vertex due to the truncation of image200, and thus, forms a polygon with 5 vertices. In another embodiment (not illustrated) a rib may form a polygon with 6 vertices, such as when a single rib200is imaged from one corner to a diagonally opposite corner and is therefore truncated by four sides of rectangular image, such as image200.

FIG. 3illustrates identified belt300with a number of ribs302identified (for clarity, not all ribs are identified in the figure). Ribs302are identified being along the entire axis of belt300. In another embodiment, identification of ribs302occurs along a portion of the axis, such as an area substantially co-located with marks304.

Referring now toFIG. 4, a measurement system400will be described in accordance with embodiments of the present disclosure. The measurement system400may comprise one or more components for analyzing an object under test402for classifying the object under test402as either good (e.g., not requiring a replacement) or bad (e.g., requiring a replacement). Other determinations may be made for the object under test402without departing from the scope of the present disclosure; for instance, the object under test402may be identified as failing (e.g., soon requiring a replacement) or abnormal (e.g., not following an expected wear pattern and, therefore, requiring further investigation and/or replacement).

In some embodiments, the measurement system400comprises an image-capture device404, an image processor406, an analysis module408and a user interface410for use by user412.

As a non-limiting example, the object under test402may comprise a belt, specifically a serpentine belt made of EPDM materials. The belt may either be located in an operational position (e.g., mounted on a vehicle or other device which employs the belt) or it may be in a non-operational position (e.g., removed from a vehicle or other device which employs the belt). The image-capture device404may be capable of capturing one or more still images. Alternatively, or in addition, the image-capture device404may be capable of capturing video images (e.g., a sequenced number of image frames which may or may not be synchronized with an audio input). The image(s) captured by the image-capture device404may comprise color (e.g., a pixel image where each pixel comprises a Red, Green, and Blue (RGB) pixel value), greyscale (e.g., a pixel image where each pixel comprises a greyscale pixel value between 0 and a predetermined number such as 255), black-and-white (e.g., a pixel image where each pixel comprises a binary value corresponding to either a black or white), infrared (e.g., a pixel image where each pixel comprises an infrared pixel value), ultraviolet (e.g., a pixel image where each pixel comprises an ultraviolet value), or any other known type of image. A non-limiting example of the image-capture device404is a camera (still or video) that is either a stand-alone device or is incorporated into a user device such as a smart phone.

Image processor406determines if any automatic corrections are necessary to improve the accuracy of the image acquired by image-capture device404of the object under test402. Upon determining automatic corrections are to be applied, such corrections are applied by image processor406. If automatic corrections are not applied, the image is made available to the analysis module408without automatic corrections. If automatic corrections are applied, then the image is made available to the analysis module408following application of the automatic corrections.

Analysis module408then analyzes the image of the object under test402and reports the results of the analysis to user412via user interface410.

Image processor406may determine that an image is beyond correction, such as may occur with an image that is under or over exposed, and may further notify the user that the image needs to be re-acquired. Notification of an unusable image may be via user interface410or another user interface.

In one embodiment, the image processing functionality performed by image processor406is performed upon the image being made available by image-capture device404. An image is made available upon one component providing the image into shared memory, accessible memory, or delivering the image via a communications link or the like. In some embodiments, a signal is sent from one component to a second component to notify the second component of the availability of the image or the termination of processing by the first component.

While the embodiments provided herein are primarily directed towards the acquisition and alteration of a single image, additional images may be created without departing from the scope of the present invention. Embodiments whereby an image is transferred from a first module to a second may be performed by copy operations whereby both the first and second module both maintain a copy of the image. Similarly, embodiments whereby the image is altered may be performed on a copy of the image and the original or preceding image remains unaltered. Furthermore, alterations may be applied to a copy of an image, change file or a logical image layer such that the alterations may be discarded and the original image left in, or returned to, an unaltered state. Processing continues with the application of the alterations to the image or with a copy of the image containing the alterations.

Image capture device404, image processor406, analysis module408and user interface410are illustrated herein as discrete components. Measurement system400may be embodied in various other configurations. In one embodiment, every component of the measurement system400may be included in a user device such as a cellular phone, smart phone, Personal Computer (PC), laptop, netbook, tablet, or the like or access a common user interface, such as user interface410. In such an embodiment, a connectable communication link is provided between components, such as wired, wireless or optical or magnetic removable media interface. In other embodiments at least two of the image capture device404, image processor406, analysis module408and user interface410are co-located within the same form factor or processing device, such as an application specific integrated circuit (ASIC), processing card (e.g., PCI, PCIe), general purpose integrated device or computing platform. It can be appreciated that a communication bus, via, circuit, PCB trace or other communications medium may be employed for communication within physically integrated components.

FIG. 5is a flowchart500illustrating a user's experience with the measurement system, such as measurement system400, in accordance with embodiments of the present disclosure. User412performs step502whereby the belt evaluation application is initiated.

In one embodiment, the completion of initiation step502automatically initiates (e.g., powers-up or otherwise makes available) the electronic components of system400(one or more of user interface410, analysis module408, image processor406, and image-capture device404). In embodiments whereby certain electronic components of system400are not initiated concurrently, or nearly so, with step502may be initiated as a precursor to their use. In other embodiments, step502resets the application and in yet another embodiment, step502is simply accessing the application.

Processing continues with the user being notified, such as by user interface410, that the application is ready to acquire an image of the object under test402, such as a belt. The user performs step504and acquires the image and is automatically presented with the results in step506. In other embodiments, one or more additional messages may be presented to the user, such as, error messages, instructions to re-acquire the image by performing step504again, informational messages, tutorials, samples, progress bars, options to save and/or print the analysis results or similar information which may improve the user's experience.

While there is no functional requirement to present intermediate steps, such as those performed by image processor406and/or analysis module408, the results or progress of the any intermediate steps may be presented to the user412as an option. The option may be selected at the time of development of the application or a configuration choice determined by user412.

With reference now toFIG. 6a flowchart600illustrating one embodiment of method steps for processing an image is provided. Flowchart600may be executed on one or more electronic devices, such as the measurement system of the embodiments ofFIG. 4. Step602acquires an initial image of the object under test, such as a belt. Step606identifies the edges of the belt. Step604finds the belt image. Step608determines the angle of the belt relative to the frame of the image. Step610determines if de-rotation is needed, if yes, processing continues to step612. If no, processing proceeds to step614. Step612de-rotates the image, whereby the image is rotated or counter rotated as the case may be, the negative value of the angle determined in step608. Step614provides the image to the analysis module for analyzing the belt image. Additional steps, not shown, may include reporting or storing the results of the analysis for use by a user, such as user412, to review and take appropriate action (e.g., replace a defective belt or schedule a future re-evaluation of the belt).

De-rotation step612may include the application of a rotation algorithm to a copy of the image or the original image as acquired in step602. De-rotation step612may embody the generation of de-rotation information (e.g., points, matrix, equation, or code) usable by analysis module408. In such an embodiment, analysis module408would read the original image with the application of the de-rotation information, such that the analysis is provided on the original image as if it had been de-rotated.

In another embodiment, the image is cropped (automatically or manually). Portions of the image that fall outside of the identified edges of the belt image may be considered extraneous and discarded. Imaging certain objects under test, such as a belt, typically excludes the entirety of the belt from any one frame as the belt image runs the length of one axis, such as the preferred axis, and terminates at the two opposite edges of the frame. Embodiments for the analysis of objects under test that do not terminate at the edge of the frame (e.g., a portion of a cut belt) may be cropped or otherwise processed, such that the termination of the object image becomes a frame edge.

Step606identifies the edges of the belt in the image. An edge can be embodied as an array of pixels forming a line. However, slight variations of the arrangement of pixels, whereby the pixels form a curve, a number of line segments, or other less than ideal line may still be considered a line if such an irregularity is determined to be within the expected value of belt edge pixels. In other embodiments, step606identifies indicia of the position of the belt, which may be an edge, marking, rib or other attribute of the belt operable to indicate the belts rotational position to the frame.

Step606may embody additional processing, such as determining a number of candidate edge lines and confirming or denying their position as an edge line. More specifically, if step606expects two edges, as would be expected with a belt, but only one line is identified as an edge candidate, the image may be reprocessed and step606repeated. Reprocessing may include enhancing or de-enhancing the image and is described in more detail with respect toFIG. 7. Alternatively, a signal may be created to indicate to a user that the image is unusable and re-acquisition step602requires repeating.

In the event more than two edge candidates are proved, where the additional candidate edges are likely ribs of the belt, the outermost edges candidates may be identified as the edges without the need for reprocessing of the image. If desired, the image may be reprocessed, such as by increasing of the contrast or increasing the resolution and step606repeated with the reprocessed image. A more detailed description of some of the embodiments of step606is provided with respect toFIG. 7.

Once the edges have been identified, step608determines the angle of at least one edge to the image frame. The edges, as identified in step606, may form an angle with the preferred axis of the frame of the image. Various embodiments are contemplated for the determination of the angle of the belt relative to the image frame in step608. Each of the edge lines are, as discussed with respect to step606, perfect lines or imperfect lines but within an acceptable range of curvature or completeness. It may be the case that each of the two edge lines are not parallel to each other due to out-of-plane image acquisition in step602. In one embodiment the angle of the belt is determined by the average slope or angle of the two edge line angles. Alternatively, a single edge line may be selected as indicating the angle of the belt. If two or more lines are to be the determinate of the indicia of the angle of the belt, the angle of the belt may be determined by an arithmetic function, such as the mean, mode, or average of the two or more lines. In another alternative, the angle of the belt is determined by one or more of a number of interior lines, such as belt rib lines, and optionally include one or both edge lines.

For many items under test, such as a belt, imaged indicia of the angle of the belt is readily determined by determining the edge lines and optionally a number of rib lines parallel to the edges. Other indicia of the angle of the belt are also contemplated. In another embodiment, step606identifies a feature of the belt indicative of orientation and step608determines the angle of the belt relative to the frame by utilizing the indicia of orientation. In one embodiment, a non-structural feature is added to the belt, such as a chalk mark, filament, printing or other demarcation. In another embodiment, the feature is structural, such as ribs or teeth. If the imaged feature is known to be non-parallel to the edge of the belt, step608considers the known angle of the feature when determining the angle of the belt relative to the image frame. To illustrate the embodiment, a belt with teeth, whereby peaks and valleys of the teeth are at a 90 degree angle to the belt are considered. In this embodiment, step606identifies a number of teeth and step608determines the angle of the belt as being 90 degrees from the angle delineating the teeth.

The frame of an acquired image known to be the perimeter of the image, or relevant portion of an image, as represented in human or computer readable form. In common imaging systems known in the art, a charged coupled device (CCD), or similar imaging array, is utilized to capture images. These imaging arrays comprise an array of light sensitive pixels commonly arranged in a rectangular array format. Individual pixels may be sensitive to a single color, such as red, blue and green, black and white, or grayscale. For purposes herein, we need not consider a first single-color pixel as a different pixel from those pixels capturing a different color of the same image. As is known with rectangles, rectangular imaging arrays have a long and short dimension or axis. The more ideal image of a belt to be analyzed is an image whereby the belt runs the length of the longest axis of the frame and is within the frame with respect to the width of the belt, such that both edges are captured, and parallel with the longest axis of the frame of the image.

It will be generally preferred to utilize the longest axis of the frame as the preferred axis. However, in other embodiment, the angle of the frame is determined with respect to a preferred orientation of the frame which may, or may not, coincide with the long dimension of the array. In embodiments employing an image capture device1204with a square imaging array, the more ideal image of the belt may be parallel to either of the perpendicular axis of the frame. One axis, such as the axis closest to parallel with the image of the belt, may be selected. However, analysis module1208may require or otherwise prefer a particular orientation (e.g., vertically) and the preferred axis selected in accord with such a requirement or preference. Similarly, image capture device1204with a circular or irregular frame may have a preferred axis selected solely in accord with the requirements or preference of the analysis module1208or in accord with an axis otherwise previously determined.

With regard toFIG. 7, flowchart700is provided illustrating one embodiment of sub-steps comprising edge detection step608. Pixel neighborhoods are examined in step702. Step704determines if an edge is indicated for the candidate pixel. In one simplified embodiment, a candidate pixel is considered within a neighborhood of 8 adjacent pixels, that is to say, a 3×3 pixel array with the candidate pixel in the center. In one example, step704would consider the pixel to be an edge candidate pixel upon determining all six of the pixels in the top two rows, which includes the candidate pixel, had one common attribute that was not shared from the three pixels in the bottom row. Should the neighboring pixels be less readily delineated, such as all pixel are identical or nearly identical to the candidate pixel or the neighborhood has no readily identified attribute to delineate an edge, the pixels may be considered non-edge pixels in step706.

Certain error detection operations may also be incorporated. In one embodiment, the number of edge pixels may be outside of an expected range. To illustrate one embodiment by way of example; a captured image of a belt is expected to have two sets of edge pixels corresponding the edge of the belt. A perfect line captured by an imaging array and running parallel to the preferred axis and terminating at the boundary of the frame, would include a number of pixels equivalent to the length of the preferred axis of the frame multiplied by the width of the line. Images of real world objects, even substantially linear ones such as a belt, are unlikely to form lines with such an exact dimension, however, a range can be expected. In one implementation, the number of edge pixels candidates equals zero and may trigger an error condition or steps to enhance the image.

Once a candidate edge pixel has been identified, step708determines if a number of the candidate edge pixels form a polygon region. An image with a significant number of edge candidate pixels that do not form a polygon region, may form another geometry or a more random pattern. This may be an indication of a poor quality image. In other cases, a certain number of edge candidate pixels that do not form polygon region may simply indicate other features (“noise”) and be excluded from further consideration as an edge candidate. As described with regard to edge pixels, if the number of expected edge lines falls outside of an expected range, processing may continue with step712or an error condition may be generated.

Step712determines if the number of lines formed are less than a target number of lines. In one embodiment, edges of a belt are being detected and, therefore, two lines are the expected number of target lines. In another embodiment, a number of belt ribs are expected and, therefore, two lines and the number of rib lines determine the expected number of target lines.

Step712determines if the number of edge lines are below the target number of lines. In one embodiment, the user is notified of an error condition. In another embodiment, processing continues to step712whereby the image is enhanced to bring out more detail. Enhancement step714may include decreasing contrast, increasing resolution, or other image enhancing technique. Processing may then resume at step704with the enhanced image.

Step716determines if the number of edge lines are above the target number of lines. In some embodiments, additional lines are not a determent to further processing and, in such embodiments, step716may be omitted and processing continues directly to step720. In embodiments where too many target lines are detected and correction is required, step718may de-enhance the image to reduce the detail and, preferably, result in fewer lines. De-enhancement step718may include increasing contrast, decreasing resolution or other image de-enhancing technique. Processing may then resume at step704with the de-enhanced image.

In certain embodiments, steps718and714are combined into an image alteration or enhancement step. A parameter, such as the increased or decreased image attribute value is selected and applied to either reveal more detail or diminish detail. Techniques for image alteration include, but are not limited to, changing the resolution, contrast, brightness, gamma, sharpness, or one or more color values.

Step720marks the location of the edges. Various embodiments of marking are contemplated herein. In one embodiment, the image is marked with the addition of a line, such as a line with a color known to the analysis module1208, a display or other module, as being associated with the location of an edge. In another embodiment, the image is encoded with the location of the edge lines in a format decodable by analysis module1208. Such encoding may be placed in the image metadata or in one or more pixels. In yet another embodiment, the location of the edges is associated with an image and the edge locations transmitted or otherwise provided to analysis module1208.

Flowchart700may be implemented to detect a number of ribs on a belt, whereby step704determines the edge of a number of ribs and step708determines if the rib edges form a line. The detection of the edge of a rib may be performed by detecting the top of a rib, the valley between ribs, the apex of a triangular or curved rib or rib valley or other visual cue delineating a rib. It should be appreciated that various steps illustrated in flowcharts600and700may be omitted or reordered without departing from the invention described herein. In one embodiment of a modification to flowchart700, step704identifies candidate edge pixels and processing continues directly to step720to marks the candidate edge pixels as edges.

FIG. 8is an embodiment of a first belt image800captured by image capture device1204. Image portion900is further described with respect toFIG. 9.

FIG. 9illustrates a binary image portion900of the first belt image800. Binary image portion900illustrates a portion of first belt image800after the application of image alteration processes, such as contrast enhancement. Image portion900, illustrates a number of pixels902. Pixels902are illustrated to indicate which value associated with a binary attribute, such as black and white, is associated with ones of pixels902. Other values (e.g., luminosity, color threshold) may also be used such as when black pixels904represent pixels with a red value above a threshold and white pixels906represent pixels with a red value below a threshold.

As a simplified example of the embodiment, binary image portion900has black pixels904of belt portions of the image and while pixels906are extraneous (e.g., background) portions of the image. Images may include artifacts not representing the desired image. Here, white pixels906include black pixel artifacts910and black pixels904include while pixel artifacts908. The embodiments provided herein allow the artifacts to be excluded from edge detection processing.

Determination of a pixel neighborhood, as described with respect toFIG. 3, allow artifact pixels908and910to be excluded as edge candidate pixels. Pixel912is of one attribute (e.g., white) and 3×3 pixel neighborhood914contains pixel which are all of a common attribute and therefore can be excluded as an edge candidate pixel. Pixel920is unique in 3×3 grid of pixel neighbors522and may also be excluded as an edge candidate.

Pixel916, with five contiguous neighboring black pixels and three contiguous white pixels may be considered an edge candidate. Pixel918is illustrated with four neighboring white and four neighboring black pixels, and may also be considered an edge candidate. More complex examples illustrating the embodiments whereby a pixel is determined to be, or not be, and edge pixel candidate are also considered. One or more iteration whereby the threshold of a pixel attribute is changed or the size or configuration of the pixel neighborhood is modified may also be used to determine edge pixels. Once the edge pixels are determined, their location is made available for further processing.

FIG. 10illustrates an embodiment of an application of an edge demarcation line1002. In one embodiment, image portion1000is modified or a modified copy of image portion900. Edge pixels1002have been identified and enhanced (represented in the figure by crosshatching). Enhancement may be embodied by the application of a specific color, luminosity or other identifiable pixel attribute. Edge pixels1002are enhanced to facilitate identification of the belt edge by a human or computer user of image portion1000. In other embodiments, the edge locations are recorded in a form and location usable by analysis module1208. Enhancement to edge pixels1002may be applied to a modified image, such as binary image portion900representing a processed version of first belt image800or a native image, such as first belt image1600.

FIG. 11illustrates an embodiment of an enhanced image1100of first belt image800. Enhanced image1100reveals, first and second edges1106and1108and a number of dark areas1100B and1104B and a number of light areas1100A and1104A, corresponding to a number of ribs. For clarity, additional ribs beyond1100and1104have not been identified. In one embodiment, the boundary of dark areas1100B and1104B with white areas1100A and1104A are rib lines and are determined to be indicia of the angle of belt1100within the frame ofFIG. 11. In another embodiment, at least one of edge1106and1108are indicia of the angle of belt1100to the frame ofFIG. 11. The indicia being determined by a process, such as that illustrated byFIG. 7.

FIG. 12illustrates a portion of the first belt image1200, such as a segment of image1100. Rib lines1206and1208, edge lines1202and1204, and preferred axis1212are illustrated in accordance with embodiments of the present disclosure. Edge lines1202and1204and rib lines1206and1208are determined, such as by execution of the steps of flowcharts1300,600, and/or700, which may be performed by imaging system1200.

As discussed in more detail, with respect toFIG. 6, an image may have a preferred axis. The preferred axis may correspond to the longest axis of a rectangular imaging array, an axis associated with a preferred orientation of the image by image analysis module1208or other preferred axis by which an advantage may be obtained. Rib lines1206and1208are interior to edge lines1202and1204. For clarity additional rib lines are not illustrated in the figure.

In the illustrated embodiment, preferred axis1212is at angle θ (theta) to edge line1202. Due to out-of-plane imaging lines, such as edge lines1202and1204and rib lines1206and1208may not be parallel. In such embodiments, theta may be the angle formed by the preferred axis1212and any one or more of edge line1204, rib lines1206and1208, additional rib lines (not illustrated), or the average, mean, mode, best-fit or other function operable to produce an indication of the orientation of the portion of the first belt image1200from two or more potential indicators.

FIG. 13can be precluded or included in the analysis whereFIG. 13illustrates a processed belt image1300in accordance with embodiments of the present disclosure. In one embodiment, edge line1202was selected as the determining line and image1300rotated into a position such that the angle theta formed by preferred axis1212and edge line1202is zero. As discussed with respect to other embodiments, alternate lines or mathematical operations of more than one line may be used to determine the orientation of belt relative to preferred axis1212and, after processing, becoming parallel to preferred axis1212

In additional embodiments, creating processed belt image1300facilitates measuring of features of processed belt image1300. An additional factor may be required to be known to convert distance on an image (e.g., distance between two or more pixels as measured in pixels) to distances associated with the object under test1202(e.g., width of a belt, missing portions due to wear or damage). The additional factor may include the known width or other dimension of the belt or belt feature, the acquisition of first belt image800occurring with imaging device1204being a known distance from object under test1202, known imaging properties of image-capture device1204(e.g., a narrow and known plane of focus), or imaging of an object not under with a known dimension at substantially the same distance from image-capture device1204as object under test1202. With the benefit of knowing belt dimensions, belt analysis module1208may utilize such information to determine the condition of the belt or other analysis operation.

FIG. 14illustrates belt image1400and crop buffer1412in accordance with embodiments of the present disclosure. In one embodiment, belt1402lies at a non-zero, non-perpendicular angle within belt image1400. Buffer1404captures an image of belt1402bounded by the edge of belt1402to the edge of frame of image1400. Operational conditions and environmental factors may prohibit buffer1404from capturing the true and complete edge of belt1402. As a result, processing rotated buffer1404may be subject to a higher error rate due to image information being omitted.

In another embodiment, crop buffer1412is bounded by the belt with an extended buffer of the width of belt1402. The amount of crop buffer1412extends beyond buffer1404may vary in accord with the degree of certainty for which the edge of belt1402may be accurately captured. For example, environmental factors (e.g., lighting, belt scarring, etc.), image properties (e.g., contrast, degree of belt rotation, etc.), and/or user selection may determine the extent of crop buffer1412beyond buffer1404. In one embodiment, crop buffer1412is approximately 10% larger than buffer1404.

In one embodiment, crop buffer1412may be shorter along the length of belt1402such that crop buffer1412may remain within the frame of belt image1400.

FIG. 15illustrates process flow1500in accordance with embodiments of the present disclosure. In one embodiment, process flow1500includes a number of operation steps1502,1504,1508,1510,1512, and1514. In other embodiments, more, fewer, or a reordered number of steps may be implemented such that a user may capture a belt image for analysis and view the results of the analysis.

In one embodiment, a user starts at start screen step1502and proceeds to operation selection step1504. Operation selection step1504may proceed to saved results step1506, help step1508, and select ribs step1510. Select ribs step1510may then proceed to image capture step1512and results step1514, whereby the user is presented with results of the analysis of a belt image. A user may be able to return to a previous process step.

In one embodiment, process flow1500is an application and starts with start screen step1502displaying initial information on the application. Operation selection step1504displays options for selection. One option is saved results step1506, whereby prior image captures (see step1512) and/or results (see step1514) may be retrieved for display. Help step1508provides instructions, tutorials, examples, or other assistance to a user operating an application using process flow1500.

Select ribs step1510displays an interactive presentation whereby the number of ribs for a belt to be analyzed is input but the user. The user may then proceed to image capture step1512whereby a prior image may be selected or an image captured via a built-in camera or a camera accessible to a device performing process flow1500or otherwise operable to capture an image of a subject belt.

With a belt image captured in image capture step1512, the belt may be analyzed according to at least some of the embodiments described herein, and presented in results step1514. In a further embodiment, image capture step1512, once an image has been selected or acquired, may display the progress of the analysis prior to presenting results step1514

FIGS. 16A-16Cillustrate process flow1600as presented to a user in accordance with embodiments of the present disclosure. In one embodiment, process flow1600is a visual presentation of a single device executing process flow1500and presenting a display in accord with steps of process flow1500. The device may be a cellular telephone application, personal data assistant (PDA), tablet computer, laptop computer, desktop computer with an attached camera, or other device operable to perform the steps of process flow1500.

In one embodiment, display1602is presented to a user in accord with step1502, display1604is presented to a user in accord with step1504, display1606is presented to a user in accord with step1506, display1608is presented to a user in accord with step1508, display1610is presented to a user in accord with step1510, and display1612is presented to a user in accord with step1512. In another embodiment, one of displays1614is presented to a user in accord with step1514. Display1612, may include a captured image, a live image and receive a user input to capture the live image (e.g., by touching image1616), or an option to retrieve an image. Display1612may also include progress bar1618, text, and/or other indicator as to the progress of the analysis of the image.

In one embodiment, the analysis may determine the belt is in one of three conditions (e.g., good, fair, bad; 1, 2, 3; etc.) and select one of displays1614A,1614B, and1614C for display to the user accordingly. In a first further embodiment, display1614A is presented to a user in accord with step1514upon the analysis indicating the subject belt is in good condition and may further indicate the belt may remain in service. In a second further embodiment, display1614B is presented to a user in accord with step1514upon the analysis indicating the subject belt is in fair condition and may further indicate the belt is nearing the end of its service life. In a third further embodiment, display1614C is presented to a user in accord with step1514upon the analysis indicating the subject belt is in poor condition and may further indicate the belt is in need of replacement.