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- W3023150830 abstract "This chapter explains the segmentation that decomposes an image into constituent objects or regions. For complex inspection tasks, such as surface inspection, segmentation is the most complicated part of Automated Visual Inspection (AVI). Many different techniques are available for segmentation. Most common techniques are the edge detection, thresholding, and region growing. The basic assumption of edge detection is that regions can be separated because of the high differential of gray levels at the edge of each region. A well known edge detection method is that by Sobel, which can be implemented by using eight 3 x 3 convolution filters. Several other edge detectors have been developed, which include Prewitt edge detector and Roberts detector. The simplest and fastest of segmentation methods is thresholding, which is based on the idea that different objects or regions in the image have significantly different gray levels. Thresholding may be global or local and it may also be fixed or adaptive. There have been numerous applications of thresholding in AVI, such as inspection of mushrooms, lace, wood, and print on plastic containers. Region growing is a bottom up method of segmentation. The idea of region growing is to take a seed pixel, grow it and thus, create quasi-homogenous regions. There are many other methods of segmentation that are also employed. They are split and merge, window–based subdivision, template matching, horizontal and vertical profiling, and AI based segmentation. The postprocessing of segmented images can be done by morphology, the Hough transform and other techniques based on artificial intelligence." @default.
- W3023150830 created "2020-05-13" @default.
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- W3023150830 date "2003-01-01" @default.
- W3023150830 modified "2023-09-27" @default.
- W3023150830 title "Segmentation" @default.
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- W3023150830 doi "https://doi.org/10.1016/b978-012554157-2/50003-1" @default.
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