Brightness and contrast correction and method for video extensometer system

Abstract

This disclosure describes systems and methods for correcting edge position errors associated with the brightness level of an associated back-screen in a video extensometer system. In some examples, to correct for edge position errors, a processing system is configured to execute an edge detection algorithm to measure and / or calculate a difference between an observed edge position and a reference edge position, associated with an amount of error, and calculate a correction term to account for the error. The correction term can be added to the result of the edge detection algorithm in the case of a white-to-black transition and subtracted from the result in the case of a black-to-white transition to correct for the error.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is a non-provisional patent application that claims priority to U.S. Provisional Patent Application No. 62 / 866,391, filed on June 25, 2019, entitled “Brightness And Contrast Correction For Video Extensometer Systems And Methods,” the contents of which are incorporated herein by reference in their entirety. Background Art

[0003] Camera-based vision systems have been implemented as part of materials testing systems to measure sample strain. These systems collect one or more images of the sample under test, which are synchronized with other test signals of interest (e.g., sample load, machine actuator / crosshead displacement, etc.). As the test progresses, the images of the test sample can be analyzed to locate and track specific features of the sample. Changes in the position of such features (such as the width of the sample) allow calculation of local sample deformation and, in turn, sample strain.

[0004] Conventional systems use a backlit screen and / or one or more light sources to direct light onto multiple surfaces and / or sides of a test specimen. However, edge position errors associated with the backlit screen's brightness level can lead to distorted readings and inaccurate measurements. Therefore, a system is needed to correct for these errors. Summary of the Invention

[0005] Disclosed herein are systems and methods for correcting edge position errors associated with the brightness level of an associated backscreen in a video extensometer system. In disclosed examples, one or more image processing algorithms can be executed to measure the width of a test sample by identifying the transition edges of the sample, as the transition edges of the sample appear as dark outlines in front of an illuminated backscreen. In some examples, to correct for edge position errors, the processing system is configured to execute an edge detection algorithm to measure and / or calculate a difference between an observed edge position and a reference edge position associated with an amount of error, and calculate a correction term to account for the error. The correction term can be applied to one or more results of the algorithm to correct for the error. In some examples, the correction term can be added to the result of the edge detection algorithm in the case of a white-to-black transition and subtracted to correct for the error in the case of a black-to-white transition.

[0006] These and other features and advantages of the present invention will become apparent from the following detailed description when taken in conjunction with the appended claims.

[0007] In a disclosed example, a system for correcting brightness distortion of a test sample includes a test system for fixing the test sample. A screen provides illumination to display the outline of the test sample, whether active or passive. An imaging device (such as a camera) is arranged opposite the screen relative to the test sample and is configured to capture an image of the test sample. A processing system is configured to receive an image of the test sample from the imaging device, measure one or more features at one or more locations along an edge of the test sample, compare the one or more features with a reference feature, determine a correction term based on the comparison result, and apply the correction term to the one or more feature measurements to provide a corrected measurement value.

[0008] In some examples, a correction term is added to the result of the edge detection algorithm in the case of a white-to-black transition. In some examples, a correction term is subtracted to correct for errors in the case of a black-to-white transition. In some examples, the correction term is in units of one of millimeters, inches, or pixels. In some examples, the one or more features include one or more of the edge location or width of the test sample.

[0009] In some examples, the processor is located on a remote computing platform that is in communication with one or more of the testing system or the imaging device.

[0010] In other disclosed examples, a method for correcting brightness distortion of a test sample includes placing the test sample between an illuminated screen and an imaging device. A processing system accesses a list of correction terms, wherein the correction term is a function of one or more characteristics including brightness and focal length. The processing system determines a correction term from the list of correction terms based on one of a predetermined focal length or a calculated focal length of the imaging device. The imaging device images a profile of the test sample against the illuminated screen. The processing system calculates one or more characteristic measurements based on the imaging and applies the correction term to the one or more characteristic measurements of the test sample to provide a corrected measurement.

[0011] In some examples, the one or more features include one or more of an edge location or a width of the test sample. In some examples, the correction term is in units of one of millimeters, inches, or pixels. In some examples, the method corrects for distortion based on contrast in the captured image or focal length of the imaging system. In some examples, the method includes modeling values associated with one or more of brightness, contrast, or focal length to determine distortion associated with brightness, contrast, or focal length in the captured image; and outputting the correction term based on the distortion relative to the one or more features. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The benefits and advantages of the present invention will become more apparent to those skilled in the relevant art after reading the following detailed description and the accompanying drawings, in which:

[0013] Figure 1is a block diagram of an example extensometer system according to aspects of the present disclosure.

[0014] Figure 2 According to aspects of the present disclosure, Figure 1 Example test specimen measured in an extensometer system.

[0015] Figure 3 According to aspects of the present disclosure Figure 1 Block diagram of an alternative view of an example extensometer system.

[0016] Figure 4 Shown are captured images of test samples according to aspects of the present disclosure.

[0017] Figure 5 Example images showing a sample subjected to the optical influence of a shadowed background.

[0018] Figure 6 An example graph relating perceived sample width to brightness is shown, according to aspects of the present disclosure.

[0019] Figure 7 is a lookup table according to aspects of the present disclosure.

[0020] Figure 8 yes Figure 1 A block diagram of an example implementation of an extensometer system.

[0021] Figure 9 A flow chart representing example machine-readable instructions for correcting brightness distortion of a test specimen in an extensometer system is shown, according to aspects of the present disclosure.

[0022] The accompanying drawings are not necessarily drawn to scale. Where appropriate, similar or identical reference numerals are used to refer to similar or identical parts. DETAILED DESCRIPTION

[0023] This disclosure describes systems and methods for correcting edge position errors associated with the brightness level of an associated back-screen in a video extensometer system. In some examples, to correct for edge position errors, a processing system is configured to execute an edge detection algorithm to measure and / or calculate a difference between an observed edge position and a reference edge position, associated with an amount of error, and calculate a correction term to account for the error. The correction term can be applied to one or more results of the algorithm to correct for the error. In some examples, the correction term can be added to the results of the edge detection algorithm in the case of a white-to-black transition and subtracted from the results of the edge detection algorithm in the case of a black-to-white transition to correct for the error.

[0024] As disclosed herein, a video extensometer system is configured to perform optical width measurements of a test sample. In some examples, the edge of a substantially opaque test sample is measured based on a brightness contrast level between the test sample and a back screen. For example, the test sample is secured within a testing machine and positioned in front of an illuminated (e.g., actively or passively illuminated) back screen. An imaging device is positioned to view a camera-facing surface of the test sample that is proximate to the focal plane of the imaging device optics (see, e.g., FIG. 1 ). Figure 3 With this arrangement, the test sample is viewed by the imaging device and imaged as a dark silhouette shape because it is in front of a brightly illuminated back screen (see e.g. Figure 4 ).

[0025] For example, when placed between an illuminated backscreen and an imaging device, the test sample's sharply focused dark outline is distinct, and when imaged in front of the illuminated backscreen, the shape and features of the edges are well defined. In some examples, the test sample is made of a material with greater transparency. Such a translucent test sample can absorb some light from the light source, sufficient to provide a measurable contrast level between the test sample and the backscreen.

[0026] However, performing high-precision measurements can be difficult because the observed position of the test sample edge depends on the brightness of the backscreen and the focal length of the imaging device.

[0027] As disclosed herein, to correct for edge position errors, a processing system is configured to execute an edge detection algorithm to measure and / or calculate a difference between an observed edge position and a reference edge position, associated with an amount of error, and calculate a correction term to account for the error. For example, the correction term can be applied to one or more results of the algorithm to correct for the error. In some examples, the correction term can be added to the results of the edge detection algorithm in the case of a white-to-black transition, and subtracted from the result in the case of a black-to-white transition to correct for the error.

[0028] As described herein, material testing systems (including material testing systems that apply tension, compression, and / or torsion) include one or more components that generate displacement and / or load to apply and / or measure stress on a test sample. In some examples, a video extensometer system is used for sample strain testing, which can include one or more of the following steps: collecting high-resolution images; providing the images to an image processor; analyzing the images to identify one or more sample features corresponding to displacement or strain values; and generating an output corresponding to the features. In disclosed examples, identified features (such as width) from one or more collected images are compared to one or more sources (such as a threshold list) or images previously collected (i.e., before testing). In some examples, the values of the identified features can be applied to one or more algorithms to generate outputs corresponding to displacement or strain values associated with the test sample.

[0029] Video processing with extensometers may include an external machine vision imaging device connected to a processing system or computing platform and / or video processing hardware and using software and / or hardware to convert the data from the camera into electrical signals or having a software interface that is compatible with the materials testing system.

[0030] As disclosed herein, a camera-based image capture (e.g., vision or video) system is implemented in a materials testing system to measure strain on a test specimen. Such a system collects multiple images of the specimen under test (i.e., during the test process), with the images synchronized with other test signals of interest (such as specimen load, machine actuator and / or crosshead displacement, etc.). The specimen images are analyzed by an algorithm (e.g., in real time and / or post-test) to locate and track specific specimen features as the test proceeds. For example, changes in the position, size, shape, etc., of such features allow calculation of test specimen deformation, which in turn leads to analysis and calculation of specimen strain.

[0031] Characteristics such as the width of the sample can be captured via an imaging device, and the captured image sent to a processing system. Image analysis can be performed by the extensometer system (e.g., via the processing system) to determine a first or initial position and / or location of the sample width to track changes in width while the test is ongoing.

[0032] The image processing algorithm then determines the edges of the test specimen and calculates the width of the test specimen and tracks the change in the width of the test specimen compared to the initial width at the start of the test (ie, the lateral strain).

[0033] The processing system is configured to execute an edge detection algorithm to measure and / or calculate a difference between an observed edge position (from a captured image) and a reference edge position associated with an amount of error, and calculate a correction term to account for the error. The correction term can be applied to one or more results of the algorithm to correct for the error. In some examples, the correction term can be added to the result of the edge detection algorithm in the case of a white-to-black transition and subtracted from the result of the edge detection algorithm in the case of a black-to-white transition to correct for the error.

[0034] As described in this article, video extensometers for measuring the width of test specimens require controlled background lighting conditions. This is achieved by incorporating a backlighting system, either active (from a separate source) or passive (using reflected light). In the case of video extensometers that will be used to measure specimen width (based on strain at the transverse specimen edge), there is currently a limitation where the brightness level of the background lighting can cause errors in edge detection of dark specimen contours.

[0035] Additionally, control of the relative brightness level of the backscreen is achieved by adjusting absolute and / or relative component positions, angular orientations of the camera, light source, test sample, and / or backscreen, and / or adjusting power levels or other lighting characteristics.

[0036] In a disclosed example, a system for correcting brightness, contrast, or focus distortion of a test sample includes: a testing system for securing the test sample; a screen that provides illumination to outline the test sample; an imaging device disposed opposite the screen from the test sample and configured to capture an image of the test sample; and a processing system. The processing system receives the image of the test sample from the imaging device; measures one or more features at one or more locations along an edge of the test sample during testing; and compares the one or more features to a reference feature.

[0037] In some examples, a correction term is added to the result of the edge detection algorithm in the case of a white-to-black transition. In some examples, a correction term is subtracted in the case of a black-to-white transition to correct for errors. In some examples, the correction term is in one of millimeters, inches, or pixels.

[0038] In some examples, the one or more characteristics include one or more of an edge location or width of the test sample. In some examples, the edge location is referenced to pixel coordinates and corrected based on a contrast direction, contrast level, or brightness and / or focus level of the test sample relative to the screen.

[0039] In some examples, the processor is located on a remote computing platform that communicates with one or more testing systems or imaging devices.

[0040] In an example, the processor is integrated with one of the imaging device or the testing system.In some examples, the processor is further configured to determine a correction term based on the comparison; and apply the correction term to the one or more feature measurements to provide a corrected measurement value.

[0041] In some disclosed examples, a method for correcting brightness, contrast, or focus distortion of a test sample is provided. The method includes positioning the test sample between an illuminated screen and an imaging device; imaging, via the imaging device, a profile of the test sample against the illuminated screen; calculating, via a processing system, one or more characteristic measurements based on the imaging; accessing, via the processing system, a list of correction terms, wherein the correction term is a function of one or more characteristics including brightness, contrast, and focus; and determining, via the processing system, a correction term from the list of correction terms based on one of brightness, a predetermined focus, or a calculated focus of the imaging device.

[0042] In some examples, the method includes applying, via the processing system, a correction term to one or more characteristic measurements of the test sample to provide corrected measurements.

[0043] In some examples, the one or more features include one or more of an edge location or a width of the test sample. In an example, the correction term is in units of one of millimeters, inches, or pixels. In an example, the method includes correcting for distortion based on contrast in the captured image or a focal length of the imaging system.

[0044] In some examples, the method includes modeling values associated with one or more of brightness, contrast, or focus to determine distortion associated with brightness, contrast, or focus in the captured image; and outputting a correction term based on the distortion relative to the one or more features.

[0045] In some examples, the imaging device is configured to capture polarized or infrared light reflected from a screen or a test sample, with the screen reflecting the light to form a dark outline of the test sample for edge analysis.

[0046] In some disclosed examples, a system for correcting brightness distortion of a test sample is provided. The system includes a processing system configured to receive an image of the test sample from an imaging device during a test process, wherein the imaging device is disposed opposite a reflective screen relative to the test sample; measure one or more features at one or more locations along an edge of the test sample during the test process; determine distortion of the test sample edge in the image associated with brightness, contrast, or focus; and determine a correction term based on the distortion.

[0047] In some examples, the processing system outputs a correction term based on the distortion relative to the one or more features. In an example, the processing system is further configured to apply the correction term to correct the distortion of the one or more features based on one or more of brightness, contrast, or focus in the image.

[0048] In some examples, one or more light sources direct light onto a surface of the test sample and a reflective surface of the screen, wherein the test sample is disposed between the one or more light sources and the screen.

[0049] Referring now to the accompanying drawings, Figure 1 1 is an example extensometer system 10 for measuring changes in one or more characteristics of a test specimen 16 undergoing mechanical property testing. The example extensometer system 10 can be connected to, for example, a testing system 33 capable of performing mechanical testing on the test specimen 16. The extensometer system 10 can measure and / or calculate changes in the test specimen 16 undergoing, for example, a compressive strength test, a tensile strength test, a shear strength test, a bending strength test, a flexural strength test, a tear strength test, a peel strength test (e.g., adhesive strength), a torsional strength test, and / or any other compressive and / or tensile test. Additionally or alternatively, the material extensometer system 10 can perform dynamic testing.

[0050] According to the disclosed examples, the extensometer system 10 may include a testing system 33 for manipulating and testing the test specimen 16, and / or a computing device or processing system 32 communicatively coupled to the testing system 33, the light source, and / or the imaging device, such as Figure 8 The testing system 33 applies a load to the test specimen 16 and measures a mechanical property of the test, such as the displacement of the test specimen 16 and / or the force applied to the test specimen 16 .

[0051] The extensometer system 10 includes a remote and / or integrated light source 14 (eg, an LED array) to illuminate a test specimen 16 and / or a reflective back screen 18. The extensometer system 10 includes a processing system 32 (see also Figure 8 ) and a camera or imaging device 12. In some examples, the light source 14 and the imaging device 12 are configured to transmit and receive infrared (IR) wavelengths; however, other wavelengths are also suitable. In some examples, one or both of the light source 14 or the imaging device 12 include one or more filters (e.g., polarization filters), one or more lenses. In some examples, a calibration routine (e.g., a two-dimensional calibration routine) is performed to identify one or more features of the test sample 16, and one or more markers 20 (including marker patterns) are also used.

[0052] In some examples, backscreen 18 is configured to reflect light from light source 14 back toward imaging device 12. For example, the surface of backscreen 18 can be configured to have properties that enhance reflection and / or direct reflected light toward the imaging device. The properties can include the shape of backscreen 18 (e.g., a parabolic configuration), and / or treatments to increase reflection (e.g., application of cube corner reflectors, reflective materials, etc.). Additionally or alternatively, a filter 30 can be arranged and / or applied to the surface to increase reflection and / or direct the amount of reflected light in a desired direction and / or wavelength. In some examples, filter 30 is configured as a collimating filter to direct as much reflected light as possible toward imaging device 12 and away from other nearby components.

[0053] In the disclosed example, the computing device 32 can be used to configure the test system 33, control the test system 33, and / or receive measurement data (e.g., transducer measurements, such as force and displacement) and / or test results (e.g., peak force, fracture displacement, etc.) from the test system 33 for processing, display, reporting, and / or any other desired purpose. The extensometer system 10 connects to the test system 33 and software using standard interfaces, including Ethernet, analog, encoder, or SPI. This allows the device to be plugged into and used with existing systems without the need for specialized integration software or hardware. The extensometer system 10 provides real-time axial and transverse encoder information or analog information to the material testing machine 33. The real-time video extensometer 10 and the material testing machine 190 exchange real-time test data, including tension / strain data, with an external computer 32, which can be configured via wired and / or wireless communication channels. The extensometer system 10 provides measurement and / or calculation of tension / strain data captured from the test specimen 16 undergoing testing in the material testing machine 33, and in turn provides stress and tension / strain data to the processor 32.

[0054] As disclosed herein, the captured images are input from the imaging device to a processor 32, wherein one or more algorithms and / or lookup tables are employed to calculate multiaxial stretch / strain values (i.e., the change or percentage change in the distance between targets, as calculated by monitoring the images of the markers 20 affixed to the test sample 16). Following calculation, the data may be stored in memory or output to a network and / or one or more display devices, I / O devices, etc. (see also FIG. Figure 8 ).

[0055] Figure 2 is used in Figure 1 1. For example, one or more markers are applied to a surface 28 facing the light source 14 and the imaging device 12. The fixture segment 26 is configured to be arranged within a fixture of a testing system 33 (see also FIG. Figure 8 ), and applies a force to the test sample 16. For example, a cross member loader applies a force to the sample 16 being tested, while the fixture grasps the test sample 16 or otherwise couples the test sample to the testing system 33. A force applicator, such as a motor, causes the crosshead to move relative to the frame to apply the force to the test sample 16, as indicated by the double arrow 34. The force 34 pulling the fixture segments 26 apart from each other may stretch the test sample 16, causing the marker to move from the first position 20A to the second position 20B. Additionally or alternatively, the marker may change shape or size, which may also be measured by the processing system 32 in view of the captured image. The force 34 may also cause an edge of the test sample to move from a first position 22A to a second position 22B. For example, in a first or initial position, the edge has a width 24A that is reduced to a width 24B when the force 34 is applied.

[0056] Based on the captured images, the processing system 33 is configured to implement stretch / strain during the measurement process. For example, to detect stretch / strain on the test sample 16, the processing system 33 monitors the images provided by the imaging device 12. When the processing system 33 identifies a change in the relative position between two or more markers and / or the edge of the test sample 16 (e.g., compared to the initial position when the crosshead began to move), the processing system 33 measures the change to calculate the amount of stretch and / or strain on the test sample 16. As disclosed herein, the markers are configured to reflect light from a light source to the camera, while the backscreen reflects the light to form a dark outline for edge analysis.

[0057] As disclosed herein, the video extensometer system 10 is configured to perform optical width measurements of an opaque test sample 16. The imaging device 12 is arranged to view a surface 28 of the test sample 16 that faces the imaging device 12 and is near the focal plane of the imaging device optics (see, e.g., FIG. Figure 3 With this arrangement, the test sample 16 is viewed and imaged by the imaging device 12 as a dark silhouette shape because it is located in front of the brightly illuminated back screen 18.

[0058] For example, when positioned between an illuminated backscreen 18 and imaging device 12, the sharply focused dark outline of test sample 16 is distinct, and the shape and features of edge 22 are well defined when imaged in front of illuminated backscreen 18. However, performing high-precision measurements can be difficult because the observed position of edge 22 of test sample 16 depends on the brightness of backscreen 18, as well as the focal length of imaging device 12.

[0059] As disclosed herein, to correct for edge position errors, the processing system 32 is configured to execute an edge detection algorithm to measure and / or calculate a difference between an observed edge position and a reference edge position associated with an amount of error, and to calculate a correction term to account for the error. For example, the correction term is applied to one or more results of the algorithm to correct for the error. In some examples, the correction term may be added to the results of the edge detection algorithm in the case of a white-to-black transition, and subtracted from the result in the case of a black-to-white transition to correct for the error.

[0060] Figure 3 The arrangement of the video extensometer system 10 is shown to measure one or both of axial strain (based on changes in the marker 20 and / or changes in the marker pattern on the front surface 28 of the test specimen 16) and transverse strain (calculated from changes in the width of the specimen 16). The components of the video extensometer system 10 are shown in FIG. Figure 3B, which illustrates the approximate position of each component relative to the other components. As shown, the components include an imaging device 12 (e.g., a camera) configured to capture one or more images of a test specimen 16 during a physical test (e.g., periodically, continuously, and / or based on one or more thresholds associated with time, force, or other suitable test characteristics).

[0061] One or more light sources 14 emit light 36 to illuminate the surface 28 of the test sample 16 and the screen 18, which is arranged to face the back surface of the test sample 16 opposite the light sources 14. In some examples, the light sources 14 are arranged to direct light off-axis (e.g., at a Figure 3 , and are angled to illuminate the front surface 28 and / or back screen 18 of the test specimen 16.

[0062] As shown, a passive (i.e., no active illumination source) back screen 18 is disposed behind the test specimen 16 and is designed to have reflective properties and is sized to present a uniformly bright background to the video extensometer imaging device 12. Figure 3 As shown in FIG, light 36 incident on backscreen 18 is reflected back as light 40 directed toward imaging device 12. In some examples, an actively illuminated backscreen is used, the brightness level of which can be adjusted by processing system 32. As shown, imaging device 12 and test sample 16 are arranged at a focal distance 39, which can be static, predetermined, and / or varied during the testing process. The light from backscreen 18 creates a darkened outline of test sample 16, allowing imaging device 12 to capture images of edge 22 and its changes during the testing process.

[0063] The test sample 16 is positioned between the imaging device 12 and the back screen 18. The test sample 16 has suitable markers 20 on a forward-facing surface 28 of the test sample 16. Analysis of one or more images associated with the video extensometer system 10 is accomplished via the processing system 32 to execute a recognition algorithm that allows for continuous tracking and measurement of both the test sample 16 markers 20 and the test sample edge 22 during the testing process.

[0064] Figure 4 Shown is a captured image of the test sample 16. As shown, the sharply focused dark outline of the test sample 16 is sharp, and when imaged in front of the illuminated backscreen 18, the shape and features of the edge 22 are well defined. Figure 4 The brightness of the background in FIG is shown as being substantially uniformly distributed. However, for a background of varying brightness, a brighter illuminated background makes the edge appear closer to the dark area relative to a lower brightness illuminated area in the background. Figure 5As shown in the figure, a rectangular sample is placed on a background where the brightness varies from the center outward. Therefore, as shown in the figure, the brighter parts of the background make the sample appear thinner. This phenomenon persists even when the test system is carefully calibrated. The resulting edge position error is therefore defined as the difference between the observed edge position and the reference edge position, measured in pixels from the white-to-black transition.

[0065] To achieve highly accurate measurements, correction terms can be calculated and applied to correct for errors between the observed position of the test sample edge and the reference edge position, which depends on the brightness of the back screen and the focal length of the imaging device.

[0066] Figure 6 A graph is shown in Figure 1 that relates observed changes in the width of a test sample to the width measured at a reference brightness. For example, errors associated with the width measurement may correspond to one or both of the brightness level of the backscreen and the sensitivity level of the edge detection algorithm relative to the brightness of the backscreen. Such errors may result in an aggregate composition error, such as when the width is imaged and recorded multiple times within a given time period during a test procedure, during which the brightness of the backscreen changes. In some examples, the test sample is controlled to move in an axial direction (e.g., the test system moves the test sample vertically) within a region where the backscreen is illuminated at different levels during the axial movement.

[0067] As disclosed herein, to correct for edge position errors, the processing system 32 is configured to execute an edge detection algorithm to measure and / or calculate a difference between an observed edge position and a reference edge position associated with an amount of error, and to calculate a correction term to account for the error. For example, the correction term is applied to one or more results of the algorithm to correct for the error. In some examples, the correction term may be added to the results of the edge detection algorithm in the case of a white-to-black transition, and subtracted from the result in the case of a black-to-white transition to correct for the error.

[0068] In some examples, the results of the edge detection algorithm may correspond to one or more corrective actions, such as commands controlling adjustments to, for example, backscreen brightness level, focus of an imaging device, or position of one or more components.

[0069] When correcting for brightness errors, the extensometer system 10 can calibrate the imaging device 12 so that the width of the test sample can be determined as the width of the shadow cast by the test sample 16 on the sensor (e.g., photodiode, etc.) of the imaging device 12. An edge is defined as the area in the image where the dark outline ends and the light background begins. An edge detection algorithm can be implemented to determine the location of the test sample edge with sub-pixel accuracy.

[0070] Based on the determined positions and / or characteristics of one or more edges of the test sample, the processing system 32 executes an edge detection algorithm to measure and / or calculate the difference between the observed edge position (e.g., the edge position captured by the imaging device 12) and the reference edge position (e.g., the edge position expected from the test sample measurement) associated with an error amount, and calculates a correction term to account for the error. For example, to correct for edge position error, a correction term can be applied to one or more results of the algorithm to correct the error. In some examples, the correction term can be added to the width determined from the edge detection algorithm in the case of a white-to-black transition, and subtracted from the correction term in the case of a black-to-white transition to correct the error. For example, the edge position referenced in pixel coordinates is corrected based on contrast direction, contrast level, or brightness and / or focus level. In some examples, the measurement and / or position of one or more edges is provided in pixel coordinates, as captured by the imaging device 12. Additionally or alternatively, the measurement and / or position of one or more edges is provided in other standard coordinate systems / units (such as meters). In such examples, a calibration process can be implemented to determine the absolute and / or relative position and / or size of the test sample within the test system prior to measurement. Additionally, width measurements may be employed to determine error, such as when one or more relevant parameters (eg, contrast, focus, etc.) are available and captured for two edges of a test specimen (eg, to perform a comparison of edge characteristics).

[0071] When performing highly accurate measurements, the observed position of the edge depends on the brightness of the background, as in Figure 5 As shown, and the focal length of the camera. However, for a known focal length and a known background brightness, the error associated with the background brightness is well defined. Therefore, a list of values containing the edge position error for configurations of relevant focal lengths and background brightness can be generated. Figure 7 In the example above, a list can store values as a two-dimensional lookup table.

[0072] For example, for a given brightness score and / or a given focus score (e.g., in standard or relative units, including algorithmically generated integers), the list can include an adjusted correction term (e.g., addition, subtraction, multiplication, etc.). The correction term can be generated as a function of one or more characteristics (including brightness and focus, as well as other values related to image distortion). In some examples, the correction term can be calculated as the edge position error multiplied by negative one (-1).

[0073] During the measurement and / or testing process, the processing system 32 is configured to generate edge detection brightness scores and / or focus scores. Based on the results, the associated correction terms can be determined by accessing a two-dimensional lookup table. Typically, linear interpolation (and / or extrapolation) will be used to determine correction terms not explicitly listed in the table. As disclosed herein, the correction terms are then added to the results of the edge detection algorithm in the case of a white-to-black transition and subtracted in the case of a black-to-white transition. Additionally or alternatively, the correction terms can be calculated in real time (e.g., during a measurement process, a test process, a calibration process, etc.) based on a model (e.g., an algorithm) and / or an analytical description of the process. Model parameters (e.g., a scaling factor) can be employed, which can be hard-coded and applied as needed. For example, values associated with focus, contrast, and / or brightness can be input into the model or can be hard-coded.

[0074] In some examples, one or both of the brightness and focus measurements may be known in advance. In this example, test sample 16 may remain in the same optical plane during the measurement and / or testing process. The associated focus scores may be predetermined and applied prior to measurement. Additionally or alternatively, the lookup table may be simplified to a one-dimensional lookup table. In other words, the one-dimensional lookup table contains correction terms for different brightness scores for predetermined focus scores. In some examples, a list or matrix of predetermined focus scores may be generated by the processing system, stored in memory, and accessed and applied to the corresponding testing process.

[0075] An example application that would benefit from the use of the disclosed systems and methods would be R-value measurement in materials testing where the test sample is moved relative to a background having non-uniform brightness (e.g., Figure 5 ) and width changes are being recorded.

[0076] Figure 8 yes Figure 1 A block diagram of an example extensometer system 10 is shown. Figure 1 As shown in FIG, the extensometer system 10 includes a test system 33 and a computing device 32. The example computing device 32 may be a general purpose computer, a laptop computer, a tablet computer, a mobile device, a server, an all-in-one computer, and / or any other type of computing device. Figure 8The computing device 32 includes a processor 202, which can be a general-purpose central processing unit (CPU). In some examples, the processor 202 can include one or more special-purpose processing units, such as an FPGA, a RISC processor with an ARM core, a graphics processing unit, a digital signal processor, and / or a system on a chip (SoC). The processor 202 executes machine-readable instructions 204, which can be stored locally at the processor (e.g., in an included cache or SoC), in a random access memory 206 (or other volatile memory), in a read-only memory 208 (or other non-volatile memory, such as flash memory), and / or in a mass storage device 210. An example mass storage device 210 can be a hard drive, a solid-state storage drive, a hybrid drive, a RAID array, and / or any other mass data storage device. A bus 212 enables communication between the processor 202, the RAM 206, the ROM 208, the mass storage device 210, the network interface 214, and / or the input / output interface 216.

[0077] An example network interface 214 includes hardware, firmware, and / or software to connect computing device 201 to a communication network 218, such as the Internet. For example, network interface 214 may include IEEE 202.X compliant wireless and / or wired communication hardware for sending and / or receiving communication data.

[0078] Figure 8 The example I / O interface 216 includes hardware, firmware, and / or software for connecting one or more input / output devices 220 to the processor 202 to provide input to and / or output from the processor 202. For example, the I / O interface 216 may include a graphics processing unit for interfacing with a display device, a universal serial bus port for interfacing with one or more USB-compatible devices, FireWire, a fieldbus, and / or any other type of interface. The example extensometer system 10 includes a display device 224 (e.g., an LCD screen) coupled to the I / O interface 216. Other example I / O devices 220 may include a keyboard, a keypad, a mouse, a trackball, a pointing device, a microphone, an audio speaker, a display device, an optical media drive, a multi-touch touch screen, a gesture recognition interface, a magnetic media drive, and / or any other type of input and / or output device.

[0079] Computing device 32 may access non-transitory machine-readable media 222 via I / O interface 216 and / or I / O device 220 . Figure 8Examples of machine-readable media 222 include optical discs (e.g., compact discs (CDs), digital versatile / video discs (DVDs), Blu-ray discs, etc.), magnetic media (e.g., floppy disks), portable storage media (e.g., portable flash drives, secure digital (SD) cards, etc.), and / or any other type of removable and / or mounted machine-readable media.

[0080] The extensometer system 10 further includes a testing system 33 coupled to the computing device 32. Figure 8 In some examples, test system 33 is coupled to a computing device via I / O interface 216, such as via a USB port, a Thunderbolt port, a FireWire (IEEE 1394) port, and / or any other type of serial or parallel data port. In some examples, test system 33 is coupled to network interface 214 and / or I / O interface 216 directly or via network 218 via a wired or wireless connection (e.g., Ethernet, Wi-Fi, etc.).

[0081] The testing system 33 includes a frame 228, a load cell 230, a displacement sensor 232, a cross-member loader 234, a material clamping device 236, and a control processor 238. The frame 228 provides rigid structural support for the other components of the testing system 33 that perform the test. The load cell 230 measures the force applied to the material under test by the cross-member loader 234 via the clamp 236. The cross-member loader 234 applies force to the material under test, while the material clamping device 236 (also known as a clamp) grasps or otherwise couples the material under test to the cross-member loader 234. The exemplary cross-member loader 234 includes a motor 242 (or other actuator) and a crosshead 244. As used herein, a "crosshead" refers to a material testing system that applies directional (axial) force and / or rotational force to a sample. A material testing system can have one or more crossheads, and the crosshead can be in any suitable position and / or orientation in the material testing system. A crosshead 244 couples the material gripping device 236 to the frame 228, and a motor 242 moves the crosshead relative to the frame to position the material gripping device 236 and / or apply force to the material being measured. Example actuators that can be used to provide force and / or motion to components of the extensometer system 10 include electric motors, pneumatic actuators, hydraulic actuators, piezoelectric actuators, relays, and / or switches.

[0082] While the example test system 33 uses a motor 242, such as a servo or direct drive linear motor, other systems may use different types of actuators. For example, hydraulic actuators, pneumatic actuators, and / or any other type of actuator may be used based on the requirements of the system.

[0083] Example fixtures 236 include platens, jaws, or other types of clamping devices, depending on the mechanical property being tested and / or the material being tested. The fixtures 236 can be manually configured, controlled via manual input, and / or automatically controlled by a control processor 238. The crosshead 244 and the fixtures 236 are operator-accessible components.

[0084] The extensometer system 10 may further include one or more control panels 250 including one or more mode switches 252. The mode switches 252 may include buttons, switches, and / or other input devices located on the operator control panel. For example, the mode switches 252 may include buttons that control the motor 242 to move (e.g., position) the crosshead 244 in small increments at a specific location on the frame 228, switches (e.g., foot switches) that control the grip actuator 246 to close or open the pneumatic grips 248, and / or any other input device that controls the operation of the test system 33.

[0085] An example control processor 238 communicates with the computing device 32 to, for example, receive test parameters from the computing device 32 and / or report measurements and / or other results to the computing device 32. For example, the control processor 238 may include one or more communication or I / O interfaces to enable communication with the computing device 32. The control processor 238 may control the cross-member loader 234 to increase or decrease the applied force, control the clamping device 236 to grasp or release the material being tested, and / or receive measurements from the displacement transducer 232, the load cell 230, and / or other transducers.

[0086] The example control processor 238 is configured to implement a stretch / strain measurement process while the test specimen 16 is being tested in the testing system 33. For example, to detect stretch / strain on the test specimen 16, the control processor 238 monitors images provided via the imaging device 12. When the control processor 238 identifies a change in the positioning and / or position of the edge 22 of the test specimen 16 (e.g., compared to an initial position at the start of movement of the crosshead 244), the control processor 238 measures the change to calculate the amount of stretch and / or strain on the test specimen 16. For example, the real-time video provided by the imaging device 12 captures the absolute position of the edge 22 and monitors their relative motion over the course of several images to calculate the stretch / strain in real time. Stress and strain data are exchanged between the real-time video extensometer 10, the testing system 33, and the processing system 32 and are typically organized and displayed via the display device 224.

[0087] Figure 9 Show that the representative can be Figure 1 and Figure 8A flowchart of example machine-readable instructions 300 for execution by a processing system 32 to correct brightness distortion of a test sample in an extensometer system is provided. At block 302, a test sample is positioned between an illuminated screen and an imaging device. At block 304, the imaging device 12 images a profile of the test sample against the illuminated screen. At block 306, the processing system 32 calculates one or more characteristic measurements based on the imaging. At block 308, the processing system 32 accesses a list of correction terms, where the correction term is a function of one or more characteristics including brightness and focal length. At block 310, the processing system 32 determines a correction term from the list of correction terms based on the brightness of the imaging device and a predetermined or calculated focal length. Furthermore, at block 312, the processing system 32 applies the correction term to the one or more characteristic measurements of the test sample to provide corrected measurements.

[0088] The present method and system can be implemented with hardware, software and / or a combination of hardware and software. The present method and / or system can be implemented in a centralized manner in at least one computing system, or in a distributed manner, with different elements distributed across several interconnected computing systems. Any type of computing system or other device suitable for executing the methods described herein is suitable. A typical combination of hardware and software can include a general-purpose computing system with a program or other code that controls the computing system when loaded and executed so that it executes the methods described herein. Another typical implementation can include a dedicated integrated circuit or chip. Some implementations can include a non-transitory machine-readable (e.g., computer-readable) medium (e.g., a flash drive, an optical disc, a magnetic storage disk, etc.) having one or more lines of machine-executable code stored thereon, so that the machine executes the process as described herein. As used herein, the term "non-transitory machine-readable medium" is defined to include all types of machine-readable storage media and does not include propagation signals.

[0089] As used herein, the terms "circuit" and "circuitry" refer to physical electronic components (i.e., hardware) and any software and / or firmware ("code") that can configure hardware, be executed by hardware, and / or otherwise be associated with hardware. For example, as used herein, a specific processor and memory can constitute a first "circuit" when executing the first or more lines of code, and can constitute a second "circuit" when executing the second or more lines of code. As used herein, "and / or" refers to any one or more items connected by "and / or" in a list. As an example, "x and / or y" refers to any element in a three-element set {(x), (y), (x, y)}. In other words, "x and / or y" refers to "one or both of x and y." As another example, "x, y and / or z" refers to any element in a seven-element set {(x), (y), (z), (x, y), (x, z), (y, z), (x, y, z)}. In other words, "x, y and / or z" refers to "one or more of x, y and z." As used herein, the term "exemplary" means serving as a non-limiting example, instance, or illustration. As utilized herein, the terms "such as" and "for example" list one or more non-limiting examples, instances, or illustrations. As used herein, whenever a circuit includes the hardware and code (if any) necessary to perform a function, the circuit is "operable" to perform that function, regardless of whether the functionality of that function is disabled or not enabled (e.g., by a user-configurable setting, a factory adjustment, etc.).

[0090] Although the present method and / or system has been described with reference to certain implementations, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the present method and / or system. In addition, many modifications may be made to adapt specific circumstances or materials to the teachings of the present disclosure without departing from the scope of the present disclosure. For example, the systems, frames and / or other components of the disclosed examples may be combined, divided, rearranged and / or modified in other ways. Therefore, the present method and / or system is not limited to the specific implementation disclosed. On the contrary, the present method and / or system will include all implementations that fall within the scope of the appended claims, both literally and under the doctrine of equivalents.

Claims

1. A system for correcting brightness, contrast, or focus distortion of a test sample, comprising: a test system for securing the test specimen; providing a screen illuminated to display an outline of the test specimen; an imaging device disposed opposite the screen relative to the test sample and configured to capture an image of the test sample; as well as Processing systems for: receiving the image of the test sample from the imaging device; During a testing process, measuring one or more features at one or more locations along an edge of the test sample based on the image from the imaging device; comparing the one or more features to a reference feature; determining a correction term based on the comparison, a given brightness score and a given focus score for the screen; as well as The correction term is applied to the one or more feature measurements to provide a corrected measurement.

2. The system according to claim 1, wherein: In the case of white to black transitions, the correction term is added to the result of the edge detection algorithm.

3. The system according to claim 1, wherein: In the case of a black to white transition, the correction term is subtracted to correct the corresponding error.

4. The system according to claim 1, wherein: The correction term is in one of millimeters, inches or pixels.

5. The system according to claim 1, wherein: The one or more characteristics include one or more of an edge location or a width of the test sample.

6. The system according to claim 5, wherein: The edge position is referenced to pixel coordinates and is corrected based on the contrast direction, contrast level or brightness level and / or focal length of the test sample relative to the screen.

7. The system according to claim 1, wherein: The processor is located on a remote computing platform in communication with one or more of the testing system or the imaging device.

8. The system according to claim 1, wherein: The processor is integrated with one of the imaging device or the testing system.

9. The system according to claim 1, wherein: The given brightness score of the screen is a brightness score relative to a brightness level of the test sample obtained from the image of the imaging device, and, The given focus score is a focus score of the test sample based on a focus level of the test sample relative to the screen obtained from the image of the imaging device.

10. A method for correcting brightness, contrast, or focus distortion of a test sample, the method comprising: arranging the test sample between an illuminated screen and an imaging device; imaging, via the imaging device, a profile of the test sample against the illuminated screen; calculating, via a processing system, one or more characteristic measurements based on the imaging; accessing, via the processing system, a list of correction terms, wherein the correction terms are functions of one or more characteristics including brightness, contrast, and focus; as well as A correction term is determined from the list of correction terms based on a known brightness level of the illuminated screen and one of a known focal length of the imaging device or a calculated focal length of the imaging device, via the processing system. 11 . The method of claim 10 , further comprising applying, via the processing system, the correction term to the one or more characteristic measurements of the test sample to provide corrected measurements.

12. The method according to claim 10, wherein: The one or more characteristics include one or more of an edge location or a width of the test sample.

13. The method according to claim 10, wherein: The correction term is in one of millimeters, inches or pixels.

14. The method of claim 10, further comprising correcting for distortion based on contrast in the captured image or a focal length of the imaging system.

15. The method according to claim 14, further comprising: modeling values associated with one or more of brightness, contrast, or focus to determine distortion associated with brightness, contrast, or focus in the captured image; and The correction term is output based on the distortion relative to the one or more features.

16. The method according to claim 10, wherein The imaging device is configured to capture polarized light or infrared light reflected from the screen or the test sample, wherein the screen reflects light to form a dark outline of the test sample for edge analysis.

17. The method of claim 10, wherein the method is a computer-implemented method.

18. A system for correcting brightness distortion of a test sample, comprising a processing system configured to: receiving an image from an imaging device of the test sample during a testing process, wherein The imaging device is arranged opposite to the reflective screen relative to the test sample; During the testing process, measuring one or more features at one or more locations along an edge of the test sample based on the image from the imaging device; determining distortion along the edge of the test sample in the image, the distortion associated with brightness, contrast, or focus; and A correction term is determined based on the distortion, a given brightness fraction of the reflective screen, and a given focus fraction.

19. The system according to claim 18, wherein: The processing system is further configured to output the correction term based on the distortion relative to the one or more features.

20. The system of claim 19, wherein: The processing system is further configured to apply the correction term to correct for distortion of the one or more features based on one or more of brightness, contrast, or focus in the image.

21. The system of claim 18, wherein: One or more light sources direct light onto a surface of the test sample and a reflective surface of the screen, wherein the test sample is disposed between the one or more light sources and the screen.

22. The system of claim 18, wherein: The given brightness score of the reflective screen is a brightness score relative to the brightness level of the test sample obtained from the image of the imaging device, and The given focus score is a focus score of the test sample based on a focus level of the test sample relative to the reflective screen obtained from an image of the imaging device.

23. A computer-implemented method for correcting brightness, contrast, or focus distortion of a test sample, the method comprising: arranging the test sample between an illuminated screen and an imaging device; imaging, via the imaging device, a profile of the test sample against the illuminated screen; calculating, via a processing system, one or more characteristic measurements based on the imaging; accessing, via the processing system, a list of correction terms, wherein the correction terms are functions of one or more characteristics including brightness, contrast, and focus; and determining, via the processing system, a correction term from the list of correction terms based on one of brightness, a predetermined focal length, or a calculated focal length of the imaging device, wherein the one or more characteristic measurements include one or more of edge position or width of the test sample, and The method further includes applying, via the processing system, the correction term to the one or more characteristic measurements of the test sample to provide corrected measurements.

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