Color fringe edge sub-pixel detection method based on color component intersection point

Abstract

The invention relates to a color fringe edge sub-pixel detection method based on a color component intersection point. The traditional color fringe edge sub-pixel detection algorithm is easy to influence by factors of environment, a detected object and the like to generate large detection error. The method comprises the following steps of: A, converting an RGB (Red, Green and Blue) color model into 111213 normalization color model by carrying out smooth de-noising on a color fringe image; and B, fitting gray level curves of various components in the color fringe image, and solving the intersection point of the components to realize the fringe sub-pixel edge detection. The method is suitable for color fringe edge in an optical three-dimensional measurement field based on a color structure.

Description

Technical field: [0001] The invention relates to a color fringe edge sub-pixel detection method based on color component intersections. Background technique: [0002] In recent years, with the reduction of the cost of hardware equipment for processing color images, color images have been more and more applied to engineering, and color image processing has also been extensively studied. Color edge detection is one of the classic research topics in color image processing. At present, the color image edge detection algorithm can be roughly divided into three categories, one is to apply the gray edge detection method to each component of the color image, and then synthesize the final detection result by a certain method, which is the most commonly used method; The color image is converted into an intensity image for edge detection. Studies have shown that 10% of the color edge information cannot be displayed in its intensity image, so this method will lose color information; th...

AI Technical Summary

Problems solved by technology

At present, the color image edge detection algorithm can be roughly divided into three categories, one is to apply the gray edge detection method to each component of the color image, and then synthesize the final detection result by a certain method, which is the most commonly used method; The color image is converted into an intensity image for edge detection. Studies have shown that 10% of the color edge information cannot be displayed in its intensity image, so this method will lose color information; there is another type of vector method, which is to make the color of the pixel into a three-dimensional vector Gradient detection of vectors, this method is computationally complex, the theory is not complete, and has not been widely used

[0003] In view of the fact that the color images acquired by hardware devices are generally stored as RGB color models, the intersection points of R, G, and B components can be used for color edge detection. Larger detection errors due to the influence of factors

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