In proposed method, an adaptive structure firstly detects the edges of motion objects.
The application of deep learning to pattern recognition reflects that human firstly takes as input the pixels of an object projected onto the retina and then detects the edges of the object, recognizes the parts of it, and finally forms the high-level abstract concept of the object [70], as shown in Fig. 7a.
The functions of photoreceptors, horizontal cells, and bipolar cells in the human retina are transformed to a simple equivalent circuit for detecting the edge of an input image.
The images were pre-processed to remove noise and were subjected to sub-pixel edge location to detect the edge of the workpiece using an algorithm coded in MATLAB.
This is achieved by proposed edge detection method which involves two steps: (1) a 2D-direction filter is designed accordingly to enhance the contrast between the background and the scratches then (2) a modified adaptive threshold method is employed to automatically detect the edge of the scratches on a filtered image.
Researchers have tried using machine vision before, for instance by having cameras detect the edge of a piece of fabric to work out where to stitch.The Atlanta team, however, have greatly increased accuracy by using high-speed photography to capture up to 1,000 frames per second.
The robots can also detect the edge of the spill and work their way inward.
The cameras that scan the surroundings can now sometimes detect the edge of the road, not just painted lines.
Therefore, the registration of the halftone cell locations can be done by detecting the edge of all the outer grids.
By fusing the three detail sub-bands to form a high-frequency image and by Laplace operator to detect the edge of the high-frequency image, the image contour can be extracted more accurately by level set, and the computation time is reduced.
