Detection method for characteristic of edge and angle point of color image

Abstract

The invention relates to a method for detecting characteristics of edge and angular point of color pictures. The technical proposal of the method is characterized by comprising the steps that: calculating the spatial gradient of each color channel of the color pictures, projecting the spatial gradient onto the color space which is insensitive to the illuminance information of shadow, light and shade and spectrum; generating an unchanged-illuminance color tensor by using unchanged-illuminance differential coefficient and own Cartesian inner product; and carrying out characteristic decompositionto obtain the partial orientation corresponding to the maximum characteristic value; then a filtering kernel of a symmetrical orientation tensor is controlled and generated by the partial orientationinformation, convolution filtering based on the filtering kernel of the symmetrical orientation tensor of the unchanged-illuminance color tensor is carries out to obtain a new color tensor; the characteristic value decomposition of the new color tensor is carried out to obtain a characteristic value and a characteristic vector and derive a characteristic measurement; detecting the characteristicof the edge and angular point. The detection of the characteristic of the edge and angular point is carried out by utilizing the characteristic of the sensor, thereby providing the designed characteristic detecting apparatus with visual characteristic better conforming to the eyes of human beings and restraining stronger noises.

Description

technical field [0001] The invention relates to a color image edge and corner feature detection method, which is used in the field of image processing. Background technique [0002] Edges and corners are low-level visual features that are widely used in various fields of image processing and computer vision, such as matching segmentation, tracking and recognition. In the low-level feature detection of traditional color images (such as RGB, HIS and other three-color images), a common technique is to convert the color image into a brightness image, and then apply a differential-based method such as Canny operator or Harris for feature detection. Although this type of method has achieved good results, for color images, the conversion to brightness images means the loss of information, such as illuminance information and the difference between pixels or features with the same brightness but different colors. With the development of microprocessors, systems directed at color ima...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the correlation between color channels is ignored, and the edges between adjacent regions with the same brightness but different colors cannot be detected.

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