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Machine Vision

The industrial camera is used to obtain the image and motion status of the measured object, and the required two-dimensional or three-dimensional coordinate information is solved by the image algorithm, the system establishes the coordinate mapping relationship, and provides it to the actuator, and the actuator performs the corresponding action.


The primary uses for machine vision are imaging-based automatic inspection and sorting and robot guidance.; in this section the former is abbreviated as "automatic inspection". The overall process includes planning the details of the requirements and project, and then creating a solution.[9][10] This section describes the technical process that occurs during the operation of the solution.

Image Processing

After an image is acquired, it is processed.[19] Central processing functions are generally done by a CPU, a GPU, a FPGA or a combination of these.[16] Deep learning training and inference impose higher processing performance requirements.[29] Multiple stages of processing are generally used in a sequence that ends up as a desired result. A typical sequence might start with tools such as filters which modify the image, followed by extraction of objects, then extraction (e.g. measurements, reading of codes) of data from those objects, followed by communicating that data, or comparing it against target values to create and communicate "pass/fail" results. Machine vision image processing methods include;

  • Stitching/Registration: Combining of adjacent 2D or 3D images.[citation needed]
  • Filtering (e.g. morphological filtering)[30]
  • Thresholding: Thresholding starts with setting or determining a gray value that will be useful for the following steps. The value is then used to separate portions of the image, and sometimes to transform each portion of the image to simply black and white based on whether it is below or above that grayscale value.[31]
  • Pixel counting: counts the number of light or dark pixels[citation needed]
  • Segmentation: Partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.[32][33]
  • Edge detection: finding object edges [34]
  • Color Analysis: Identify parts, products and items using color, assess quality from color, and isolate features using color.[6]
  • Blob detection and extraction: inspecting an image for discrete blobs of connected pixels (e.g. a black hole in a grey object) as image landmarks.[35]
  • Neural net / deep learning / machine learning processing: weighted and self-training multi-variable decision making [36] Circa 2019 there is a large expansion of this, using deep learning and machine learning to significantly expand machine vision capabilities.
  • Pattern recognition including template matching. Finding, matching, and/or counting specific patterns. This may include location of an object that may be rotated, partially hidden by another object, or varying in size.[37]
  • BarcodeData Matrix and "2D barcode" reading [38]
  • Optical character recognition: automated reading of text such as serial numbers [39]
  • Gauging/Metrology: measurement of object dimensions (e.g. in pixels, inches or millimeters[40]
  • Comparison against target values to determine a "pass or fail" or "go/no go" result. For example, with code or bar code verification, the read value is compared to the stored target value. For gauging, a measurement is compared against the proper value and tolerances. For verification of alpha-numberic codes, the OCR'd value is compared to the proper or target value. For inspection for blemishes, the measured size of the blemishes may be compared to the maximums allowed by quality standards.[38]

Imaging based robot guidance 
Machine vision commonly provides location and orientation information to a robot to allow the robot to properly grasp the product. This capability is also used to guide motion that is simpler than robots, such as a 1 or 2 axis motion controller.[6] The overall process includes planning the details of the requirements and project, and then creating a solution. This section describes the technical process that occurs during the operation of the solution. Many of the process steps are the same as with automatic inspection except with a focus on providing position and orientation information as the end result.[6]

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