Patent Application
20190012777
The present disclosure generally relates to systems and techniques for automated visual inspection of components.
The components of high-temperature mechanical systems, such as, for example, gas turbine engines, operate in severe environments. For example, the high-pressure turbine blades and vanes exposed to hot gases in commercial aeronautical engines typically experience surface temperatures of about 1000° C., with short-term peaks as high as 1100° C.
Components of high-temperature mechanical systems may include a superalloy substrate, a ceramic substrate, or a ceramic matrix composite (CMC) substrate. In many examples, the substrates may be coated with one or more coatings to modify properties of the surface of the substrate. For example, superalloy substrates may be coated with a thermal barrier coating to reduce heat transfer from the external environment to the superalloy substrate. Ceramic or CMC substrates may be coated with an environmental barrier coating to reduce exposure of the ceramic or CMC substrate to environmental species, such as oxygen or water vapor. Additionally, certain components may include other functional coatings, such as abradable coatings for forming seals between moving parts, abrasive coatings to provide toughness to moving components that may contact abradable coatings, or the like.
In some examples, the disclosure describes an apparatus for measuring a feature of a tested component. The apparatus may include a lighting device configured output light to illuminate at least a portion of the tested component, an imaging device, and a computing device. The computing device may be configured to receive, from the imaging device, a first image of the portion of the tested component in a first state; segment the first image to isolate a first target area of the image from background areas of the first image; and measure a plurality of first lengths of at least one portion of the first target area. The computing device also may be configured to receive, from the imaging device, a second image of the portion of the tested component in a second, different state; segment the second image to isolate a second target area of the second image from background areas of the second image; and measure a plurality of second lengths of at least one portion of the second target area, wherein a respective first length of the plurality of first lengths corresponds to a respective second length of the plurality of second lengths. The computing device also may be configured to compare each respective first length of the plurality of first lengths to the corresponding second length of the plurality of second lengths.
In some examples, the disclosure describes a method for measuring a feature of a tested component. The method may include controlling, by a computing device, a lighting device to illuminate at least a portion of a tested component. The method may also include controlling, by the computing device, an imaging device to acquire a first image of the portion of the tested component in a first state. The method may also include segmenting, by the computing device, the first image to isolate a first target area of the image from background areas of the first image. The method may also include measuring, by the computing device, a plurality of first lengths of at least one portion of the first target area. The method may also include controlling, by the computing device, the imaging device to acquire a second image of the portion of the tested component in a second, different state. The method may also include segmenting, by the computing device, the second image to isolate a second target area of the second image from background areas of the second image. The method may also include measuring, by the computing device, a plurality of second lengths of at least one portion of the second target area, wherein a respective first length of the plurality of first lengths corresponds to a respective second length of the plurality of second lengths. The method may also include comparing, by the computing device, each respective first length of the plurality of first lengths to the corresponding second length of the plurality of second lengths.
The details of one or more examples are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.
The disclosure describes example systems and techniques for measuring a feature of a tested component using a lighting device configured output light to illuminate at least a portion of the tested component, an imaging device, and a computing device. The computing device may receive, from the imaging device, a first image of the portion of the tested component in a first state. The computing device may also segment the first image to isolate a first target area of the image from background areas of the first image. The first target area may be an area whose one or more dimensions are to be measured (e.g., lengths of line segments traversing any portion of first target area may be measured). The computing device may measure a plurality of first lengths of at least a portion of the first target area. The computing device may receive, from the imaging device, a second image of the portion of the tested component in a second state. The computing device may segment the second image to isolate a second target area of the second image from background areas of the second image. The second target area may be an area whose one or more dimensions are to be measured. The computing device may measure a plurality of second lengths of at least a portion of the second target area. A location at which each of the plurality of first lengths is measured may correspond to a location at which a respective second length of the plurality of second lengths is measured. The computing device may compare a respective first length to a corresponding second length. By comparing the respective first lengths to corresponding second lengths, the computing device may determine whether a difference between each respective first length and the corresponding second length is within a predetermined tolerance, whether the first target area substantially corresponds to the second target area, whether the second target area is within or out of a predetermined tolerance, or the like.
The components of high-temperature mechanical systems, such as, for example, gas turbine engines, may include a superalloy substrate, a ceramic substrate, a CMC substrate, or the like, having one or more coatings. For example, gas turbine engine components may include at least one of a bond coat, a calcia-magnisia-aluminosilicate (CMAS)-resistance layer, an environmental barrier coating (EBC), a thermal barrier coating (TBC), an abradable coating, an abrasive coating, or the like. Each of the one or more coatings may have unique mechanical properties, chemical properties, or both to contribute to the performance of an article. For example, an abrasive coating may be confined to a portion of a turbine blade fin tip to facilitate formation of a channel in an abradable coating of a shroud ring of the gas turbine engine during an engine break-in period. Formation of a channel in the abradable coating of the shroud ring may enhance engine efficiency by reducing air flow past the tips of the turbine blade.
Application of a coating to a substrate, e.g., a turbine blade tip, may include a number of processing steps, for example, masking, grit blasting, applying a bond coat, and/or applying a top coat to a portion of the substrate. The mechanical integrity of the top coat may be affected by, for example, the spatial relationship (e.g., the area and the position, relative to one another) of the masked portion of the substrate, the grit blasted portion of the substrate, the bond coated portion of the substrate, and/or the top coated portion of the substrate. For example, application of the bond coat to a portion of a substrate that is not grit blasted may affect the adhesion of the bond coat (and, thereby, adhesion of the abrasive coating) to the substrate. As another example, application of the top coat to a portion of the substrate that does not include a bond coat may affect the adhesion of the top coat to the substrate. Therefore, before or after any step of a coating process, it may be useful to determine the spatial relationship of any one of the masked, grit blasted, bond coated, or top coated portions of the substrate.
The systems and techniques of the disclosure may enable automated visual inspection of a portion of a substrate (e.g., a turbine blade tip) to determine the spatial relationship of a masked, grit blasted, bond coated, or top coated portions of the substrate. For example, the systems and techniques of the disclosure may measure a plurality of first lengths of at least one portion of a first target area of a tested component in a first state (e.g., before or after masking, grit blasting, or bond coating). The systems and technique of the disclosure also may measure a plurality of second lengths of at least one portion of a second target area of the tested component in a second state (e.g., before or after a subsequent step of a coating process). Respective first lengths of the plurality of first lengths may correspond to respective second lengths of the plurality of second lengths. The systems and techniques of this disclosure may compare a respective first length to a corresponding respective second length. The systems and techniques may utilize the comparison of the respective first length and the corresponding respective second length to determine whether a difference between the respective first length and the respective second length is within a predetermined tolerance, whether the first target area substantially corresponds to the second target area, whether the second target area is within a predetermined tolerance, or the like.
For example, a respective first length of the plurality of first lengths may correspond to a length of a portion of the target area of the tested component after grit blasting, and a respective second length of the plurality of second lengths may correspond to a length of a portion of the target area of the tested component after application of a bond coat. Comparison of the respective first and the respective second lengths may enable determination of the distance the edge of the bond coat area from the edge of the grit blasted area. In this way, the disclosure describes systems and techniques to quantify the spatial relationship of the masked, grit blasted, and/or coated portions of a substrate more quickly and accurately than other systems and techniques.
Mount 18 may be configured to receive and detachably secure tested component 12, e.g., relative to imaging device 20 and lighting device 22. For example, mount 18 may be shaped to receive a root section (e.g., fir tree section) of a turbine blade. Mount 18 may further include a clamp (e.g., spring clamp, bolt clamp, vise, or the like) or another fastener configured to detachably secure tested component 12 on stage 16.
Imaging device 20 may be configured to acquire a digital image of at least a portion 24 of tested component 12. For example, imaging device 20 may include a fixed or variable focal length lens, a fixed or variable aperture, a shutter, a shutter release, an image sensor (e.g., a charge-coupled device, a complementary metal-oxide semiconductor, or the like), or the like. Imaging device 20 may include fewer or additional components.
Lighting device 22 may be configured to output light to illuminate at least a portion 24 of tested component 12. Lighting device 22 may include any suitable light source, such as, e.g., one or more of LED lamps, incandescent bulbs, fluorescent lamps, halogen lamps, metal halide lamps, sulfur lamps, high- or low-pressure sodium lamps electrodeless lamps, or the like. In some examples, lighting device 22 may include an LED strip. For example, lighting device 22 may include an LED strip that may span a portion of enclosure 14 to illuminate tested component 12 from a plurality of angles.
Computing device 30 may include, for example, a desktop computer, a laptop computer, a tablet computer, a workstation, a server, a mainframe, a cloud computing system, or the like. Computing device 30 is configured to control operation of system 10 including, for example, stage 16, mount 18, imaging device 20, and lighting device 22. Computing device may be communicatively coupled to at least one of stage 16, mount 18, imaging device 20, or lighting device 22 using respective communication connections. In some examples, the communication connection may include network links, such as Ethernet or other network connections. Such connection may be wireless and/or wired connections. In other examples, the communications connections may include other types of device connections, such as, USB, IEEE 1394, or the like. For example, computing device 30 may be communicatively coupled to imaging device 20 via wired or wireless imaging device connection 26 and/or lighting device 22 via wired or wireless lighting device connection 28.
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Computing device 30 may be configured to control operation of at least one of stage 16, mount 18, imaging device 20, or lighting device 22 to position tested component 12 relative to imaging device 20, lighting device 22, or both. For example, one or both of stage 16 and mount 18 may be translatable, rotatable, or both along at least one axis to position tested component 12 relative to imaging device 20. Similarly, imaging device 20 may be translatable, rotatable, or both along at least one axis to position tested component 12 relative to one or both of stage 16 and mount 18. Computing device 30 may control any one or more of stage 16, mount 18, or imaging device 20 to translate and/or rotate along at least one axis to position tested component 12 relative to imaging device 20. Positioning tested component 12 relative to imaging device 20 may include positioning at least portion 24 of tested component 12 to be imaged using imaging device 24. In some examples, computing device 30 may record an initial position of any one or more of stage 16, mount 18, or imaging device 20. In this way, computing device 30 may enable repeatable imaging of a plurality of tested components.
Computing device 30 also may be configured to control operation of lighting device 22. For example, computing device 30 may be configured to control a power delivered to one or more light sources within lighting device 22 to control intensity of light output by lighting device 22. Further, computing device 30 may be configured to control lighting device 22 to output light of a selected wavelength (e.g., one or more wavelength ranges). For example, lighting device 22 may include one or more LED packages. An LED package may include one or more of individual red, green, and blue (RGB) LEDs, and a controller to selectively control power, or a percentage of power, to one or more of the individual RGB LEDs. Computing device 30 may control an LED package to output light of a selected wavelength or wavelength range. In this way, computing device 30 may be configured to control lighting device 22 to output light to illuminate at least portion 24 of tested component 12 with a selected intensity and wavelength range of light. In some examples, the intensity or wavelength range of light illuminating portion 24 may affect the contrast of one or more portions of an image of portion 24 acquired by imaging device 20. In this way, lighting device 24 may selectively control the contrast of one or more portions of an image acquired by imaging device 20.
Computing device 30 may be configured to control imaging device 20 to acquire images of tested component 12. For example, computing device 30 may control imaging device 20 to capture a plurality of images of at least portion 24 of tested component 12 and may receive data representative of the plurality of images from imaging device 20. Computing device 30 may compare at least one feature of at least two of the plurality of images.
In some examples, each of the plurality of images may correspond to a respective state of the tested component 12. Each state may be a different stage of a manufacturing process by which tested component 12 is formed. For example, a first state may be after casting, forging, additive manufacturing, or the like to form a substrate of tested component 12; a second stage may be after grit blasting of the substrate; a third state may be after masking of the substrate; a fourth state may be after forming a bond coating on a selected area of the substrate; and a fifth state may be after forming a top coating on a selected area of the substrate. In some examples, each of the plurality of images may correspond to four states of the tested component 12, including after masking, after grit blasting, after bond coating, and after top coating. Other states are possible, depending on the manufacturing process used to form tested component 12.
For example, computing device 30 may receive, from imaging device 22, data representative of a first image of portion 24 of tested component 12 in a first state. Similarly, computing device 30 may receive, from imaging device 22, data representative of a second image of portion 24 of tested component 12 in a second state. In some examples, computing device 30 may receive, from imaging device 22, data representative of a standard component image of a portion of a standard component (e.g., a portion 24 of tested component 12 that is representative of a plurality of tested components). In some examples, computing device 30 may be configured to retrieve a stored image, e.g., a first image, second image, standard image, or the like. In this way, computing device 30 may receive a plurality of images, each image corresponding to one or more states of a plurality of tested components.
In some examples, computing device 30 may be configured to condition an image. For example, to condition an image, computing device 30 may remove artifacts from the image, resize a portion of the image, deform a portion of the image, transform a portion of the image, adjust a wavelength of light emitted from lighting device, adjust a color of a portion of the image, adjust a contrast of a portion of the image, or the like. As another example, to condition an image, computing device 30 may determine that an image should be reacquired; adjust at least one of a focal length of the imaging device, a position of one or more of stage 16, mount 18, imaging device 20, and lighting device 22, or an output light wavelength range or intensity of lighting device 22; and reacquire the image. In some examples, image conditioning may result in an image that computing device 30 can more easily segment. In this way, computing device 30 may improve the speed and/or accuracy of subsequent image analysis by conditioning the image (e.g., image segmentation, feature measurement, or the like).
In some examples, computing device 30 also may segment an image to isolate a target area of the image from background areas of the image. For example, computing device 30 may isolate a target area (e.g., portion 24) of an image from the background (e.g., other non-target areas of tested component 12, enclosure 14, or the like) of the image. In other examples, computing device 30 may segment an image to isolate a plurality of target areas of the image from background areas of the image.
In some examples, computing device 30 may use active contouring, edge detection, or the like to segment the image. In some examples, active contouring may find the boundaries of an object in an image. In some examples, edge detection may include one or more mathematical algorithms that may identify points in an image where the brightness, contrast, or the like changes. Active contouring or edge detection may include identifying, by computing device 30, a first search region. The first search region may include a single pixel or a plurality of pixels in the image. For example, the first search region may be based on at least one of user input, a predetermined portion of an initial target area (e.g., portion 24), or the like. For example, computing device 30 may segment a standard component image to isolate a standard target area of the standard component image from background areas of the standard component image to determine a first search region based on a predetermined portion of the standard target area. Next, computing device 30 may identify a second, adjacent search region that is a predetermined distance in one or more predetermined directions from the first search region. The second search region may include a single pixel or a plurality of pixels in the image. Computing device 30 may then determine whether a difference in contrast between the first search region and the second search region is greater than predetermined threshold (e.g., whether the first search region and second search region include a high contrast area). Computing device 30 may repeat identifying subsequent search regions and determining whether a difference in contrast between a preceding search region and a subsequent search region is greater than predetermined threshold to identify a plurality of high contrast areas.
In some examples, the plurality of high contrast areas may define a boundary of the target area of an image acquired by imaging device 22. For example, a first plurality of high contrast areas may define a first boundary of a first target area of portion 24 of tested component 12 in a first state. Similarly, a second plurality of high contrast areas may define a second boundary of a second target area of tested component 12 in a second state. In some examples, computing device 30 may be configured to segment a plurality of images, each image corresponding to a respective tested component of a plurality of tested components or to a respective state of a tested component. For example, computing device 30 may segment a first image of portion 24 of tested component 12 in a first state and segment a second image of portion 24 of tested component 12 in a second state. In this way, computing device 30 may segment a plurality of images to improve the speed and/or accuracy of subsequent image analysis (e.g., measurement of a plurality of lengths of the target area of an image).
Computing device 30 also may measure a plurality of lengths of at least one portion of a target area. For example, computing device 30 may be configured to identify a respective first position of a plurality of first positions on a first side of a boundary of the target area. Computing device 30 may determine the first side of the boundary of the target area. For example, computing device 30 may determine the first side of the boundary based on at least one of a dimension, a coordinate position, or an orientation of at least a portion of a target area. In some examples, computing device 30 may determine at least one straight line to approximate at least one side of the boundary of the target area. For example, computing device may determine a linear regression based on at least a portion of the first plurality of high contrast areas that defines the first side of the boundary. Computing device 30 may determine a plurality of line segments extending at a predetermined angle from the at least one straight line. For example, computing device 30 may determine a plurality of line segments extending substantially perpendicular to a linear regression line that approximates the first side of the boundary. Computing device 30 may determine a first respective first position at an intersection of the first side of the boundary and a first line extending at a predetermined angle from a first position on the straight line. In some examples, computing device 30 may determine a second respective first position at an intersection of the boundary line and a second line extending at a predetermined angle from a second position on the straight line. In other examples, computing device 30 may determine a plurality of line segments extending from the first side of the boundary based on the orientation of the pixels of the image. For example, the image may include a plurality of pixel columns (or rows). A first respective pixel column of the plurality of pixel columns (e.g., one or more pixels in width) may intersect the first side of the boundary at a first respective first position of a plurality of first positions. A second respective pixel column of the plurality of pixel columns may intersect the first side of the boundary at a second respective first position of a plurality of first positions. In this way, computing device 30 may determine a plurality of first positions on a first side of a boundary of the target area.
Computing device 30 then may identify a respective second position of a plurality of second positions on a second opposing side of the boundary of the target area. In some examples, computing device 30 may determine the respective second position at an intersection of the second opposing side of the boundary and the first line extending at a predetermined angle from a first position on the straight line. In other examples, the plurality of second positions on a second opposing side of the boundary of the target area may be determined in substantially the same manner as described above with respect to the plurality of first positions, except that the straight line may be fit to the second opposing boundary. Computing device 30 then may assign, by a predetermined process, a respective first position to a corresponding second position. In other examples, a respective pixel column of the plurality of pixel columns may interest the second side of the boundary at a corresponding second position of the plurality of second positions. In this way, computing device 30 may determine each respective first position of a plurality of first position and a corresponding second position of the plurality of second positions.
In some examples, a plurality of vectors extending from each respective first position to a corresponding second position may be substantially parallel. In other examples, a plurality of vectors extending from each respective first position to a corresponding second position may not be substantially parallel, e.g., one or more vectors may converge or diverge.
Once computing device 30 has identified a plurality of first positions and a plurality of corresponding second positions, computing device 30 may determine a plurality of lengths between the respective first positions of the plurality of first positions and the respective corresponding second positions of the plurality of second positions. In some examples, each of the plurality of lengths may include chains of image pixels (e.g., columns or rows of pixels that may be one or more pixels in width) extending from a respective first position to a corresponding second position. For example, computing device 30 may identify respective first positions of a plurality of first positions on a first side of a first boundary of a first target area of tested component 12 in a first state, identify respective second positions of a plurality of second positions on a second opposing side of the first boundary of the first target area of portion 24 of tested component 12 in a first state, and determine a plurality of first lengths (e.g., respective lengths of a plurality of pixel chains) between the respective first positions and the corresponding respective second positions. In some examples, computing device 30 may convert each of the plurality of lengths from a number of pixels in a pixel chain to a unit of length (e.g., millimeters) based on a predetermined pixel-to-length ratio.
Computing device 30 may repeat the active contouring, edge detection, or the like for a second target area of tested component 12 in a second state. For example, computing device 30 may identify respective third position of a plurality of third positions and corresponding fourth positions of a plurality of fourth positions on a boundary of the second target area. Once computing device 30 has identified a plurality of third positions and a plurality of corresponding fourth positions, computing device 30 may determine a plurality of lengths between the respective third positions and corresponding fourth positions. For example, computing device 30 may identify respective third positions of a plurality of third positions on a first side of a second boundary of a second target area of tested component 12 in a second state, identify respective fourth positions of a plurality of fourth positions on a second opposing side of the second boundary of the second target area of portion 24 of tested component 12 in a second state, and determine a plurality of second lengths (e.g., respective lengths of a plurality of pixel chains) between the respective third positions and the corresponding respective fourth positions.
In some examples, the respective first positions may substantially correspond to the respective third positions, and the respective second positions may substantially correspond to the respective fourth positions. For example, a first vector extending from a respective first position to a corresponding second position may substantially overlap, or otherwise substantially correspond to, a second vector extending from a respective third position to a corresponding fourth position. As such, the locations at which respective first lengths were determined by computing device 30 may correspond to locations at which respective second lengths were determined by computing device 30. In this way, computing device 30 may determine respective first lengths of a plurality of first lengths of at least one portion of a first target area and corresponding second lengths of a plurality of second lengths of at least one portion of a second target area.
Computing device 30 also may compare respective first lengths to corresponding second lengths. For example, computing device 30 may determine a respective difference between each respective first length and corresponding second length. In other examples, computing device 30 may compare statistics based on respective first lengths, corresponding second lengths, or both. For example, computing device 30 may compare the respective average of first lengths and second length, the variance of first lengths and second length, the relative positions of first lengths and second lengths, or the like. In other examples, computing device 30 may use machine learning to estimate a quality of a state of tested component 12 based on respective lengths to corresponding second lengths or statistics derived from respective lengths to corresponding second lengths. In this way, computing device 30 may determine a plurality of target area dimension differences.
Computing device 30 may be configured to analyze the plurality of lengths and/or the plurality of target area dimension differences. For example, computing device 30 may determine whether one or more of the plurality of target area dimension differences is within a predetermined tolerance (e.g., less than a predetermined value). In some examples, in response to determining that one or more of the plurality of lengths is outside a predetermined tolerance, computing device 30 may determine that the second state (e.g., after grit blasting, masking, or a coating step) is out of tolerance. In some examples, computing device 30 may count a number of lengths that is out of tolerance, compare the number of out-of-tolerance lengths to a threshold value, and determine whether the second state is within tolerance based in the comparison. For example, in response to a number of out-of-tolerance lengths being greater than the threshold value, computing device 30 may determine that the second state is out of tolerance. Computing device 30 may be configured to output an indication of whether the second state is within or out of tolerance, e.g., via a user interface device, such as a screen.
In some examples, rather than outputting an indication of whether the second state is out of or within tolerance, computing device 30 may be configured to output a graphical display of at least one of first lengths of the plurality of first lengths, second lengths of the plurality of second lengths, or difference between respective first lengths and respective second lengths. In other examples, computing device 30 may be configured to output a graphical display including one or more statistics based on any one or more of the first lengths, the second lengths, or the differences between respective first lengths and respective second lengths. For example, computing device 30 may statistically analyze at least one of the first lengths, the second lengths, or the differences. In some examples, computing device 30 may be configured to output a histogram indicating the absolute value of a difference between a respective first length and a corresponding second length. In other examples, computing device 30 may be configured to output other statistical analyses of at least one of the first lengths, the second lengths, the differences, or the predetermined tolerances. For example, computing device 30 may be configured to output fits to a given distribution, a measure of similarity between two distributions, analysis of variance, or the like. In some examples, computing device 30 may be configured to output a display of at least one of the first image, the first target area, the second image, or the second target area, and an indication of at least one of the first lengths, the second lengths, the differences, or the predetermined tolerances. In this way, computing device 30 may output a graphical display that may enable evaluation of the spatial relationship of the first state and the second state of tested component 12 (e.g., after casting, forging, or additive manufacturing; after grit blasting; after masking; after bond coating; after top coating; or any other stage of a manufacturing process) to determine if the second state meets a predetermine tolerance.
One or more processors 40 are configured to implement functionality and/or process instructions for execution within computing device 30. For example, processors 40 may be capable of processing instructions stored by image processing module 50. Examples of one or more processors 40 may include, any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or integrated logic circuitry.
Memory units 48 may be configured to store information within computing device 30 during operation. Memory units 48, in some examples, include a computer-readable storage medium or computer-readable storage device. In some examples, memory units 48 include a temporary memory, meaning that a primary purpose of memory units 48 is not long-term storage. Memory units 48, in some examples, include a volatile memory, meaning that memory units 48 does not maintain stored contents when power is not provided to memory units 48. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art. In some examples, memory units 48 are used to store program instructions for execution by processors 40. Memory units 48, in some examples, are used by software or applications running on computing device 30 to temporarily store information during program execution.
In some examples, memory units 48 may further include one or more memory units 48 configured for longer-term storage of information. In some examples, memory units 48 include non-volatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.
Computing device 30 further includes one or more communication units 44. Computing device 30 may utilize communication units 44 to communicate with external devices (e.g., stage 16, mount 18, imaging device 20, and/or lighting device 22) via one or more networks, such as one or more wired or wireless networks. Communication unit 44 may be a network interface card, such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information. Other examples of such network interfaces may include Wi-Fi radios or USB. In some examples, computing device 30 utilizes communication units 44 to wirelessly communicate with an external device such as a server.
Computing device 30 also includes one or more input devices 42. Input devices 42, in some examples, are configured to receive input from a user through tactile, audio, or video sources. Examples of input devices 42 include a mouse, a keyboard, a voice responsive system, video camera, microphone, touchscreen, or any other type of device for detecting a command from a user.
Computing device 30 may further include one or more output devices 46. Output devices 46, in some examples, are configured to provide output to a user using audio or video media. For example, output devices 46 may include a display, a sound card, a video graphics adapter card, or any other type of device for converting a signal into an appropriate form understandable to humans or machines.
Computing device 30 also may include image acquisition module 52, image conditioning module 54, initial position module 56, segmentation module 58, measurement module 60, and visualization module 62. Image acquisition module 52, image conditioning module 54, initial position module 56, segmentation module 58, measurement module 60, and visualization module 62 may be implemented in various ways. For example, one or more of image acquisition module 52, image conditioning module 54, initial position module 56, segmentation module 58, measurement module 60, and visualization module 62 may be implemented as an application executed by one or more processors 40. In other examples, one or more of image acquisition module 52, image conditioning module 54, initial position module 56, segmentation module 58, measurement module 60, and visualization module 62 may be implemented as part of a hardware unit of computing device 30 (e.g., as circuitry). Functions performed by one or more of image acquisition module 52, image conditioning module 54, initial position module 56, segmentation module 58, measurement module 60, and visualization module 62 are explained below with reference to the example flow diagrams illustrated in
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Various examples have been described. These and other examples are within the scope of the following claims.
