 |
| Image for representative purpose only. |
Find Out About The Processing Of Images Obtained From Machine Vision For Automatic Inspection
Here we continue with the fourth part of our blog on mechatronics. Those who have missed our third blog can read it from Here. It will help to connect with the fourth part of the blog discussing about the processing of images obtained from machine vision for the purpose of automatic inspection. Let us explore the blog to find out in more details.
Image Processing
After an image is acquired, it is processed.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 vales to create and communicate "pass/fail" results. Machine vision image processing methods include:
Stitching/Registration: Combining of adjacent 2D or 3D images.
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.
Pixel Counting: Counts the number of light or dark pixels
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.
Edge detection: finding object edges.
Color Analysis: Identify parts, products and items using color, assess quality from color, and isolate features using color.
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.
Neural Net / Deep Learning / Machine Learning Processing: weighted and self-training multi-variable decision making.
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.
Optical Character Recognition: Automated reading of text such as serial numbers.
Gauging/Metrology: Measurement of object dimensions (e.g. in pixels, inches or millimeters)
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.
Output
A common output from automatic inspection systems is pass/fail decisions. These decisions may in turn trigger mechanisms that reject failed items or sound an alarm. Other common outputs include object position and orientation information for robot guidance systems.Additionally, output types include numerical measurement data, data read from codes and characters, counts and classification of objects, displays of the process or results, stored images, alarms from automated space monitoring MV systems, and process control signals. This also includes user interfaces, interfaces for the integration of multi-component systems and automated data interchange.
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.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.
Market
The global Machine Vision market is expected to reach USD 15.46 billion by the end of 2022 with 8.18% CAGR during the forecast period 2017-2022. Machine Vision Market is growing with positive growth in all regions. Increasing application areas year on year and advancement in technology and integration is driving the market on a global scale. Asia Pacific is dominating the global market with more than 30% of market share followed by Europe standing as the second biggest market due to heavy demand from the automotive and healthcare industry. North America stands as the third biggest market.
No comments:
Post a Comment