Contour detection method based on non-classical receptive field and linear nonlinear modulation

Abstract

The present invention provides a contour detection method based on a non-classical receptive field and linear nonlinear modulation. The method comprises: A, inputting an image to be detected through gray processing; B, performing Gabor filtering of the image to be detected to obtain the Gabor energy value of each direction of each pixel point; C, constructing X-cell and Y-cell simulation models in a retina ganglion cell; D, calculating and obtaining stimulating response of each central neuron through the non-classical receptive field corresponding to the X cell; E, calculating and obtaining the stimulating response of each central neuron through the non-classical receptive field corresponding to the Y cell; F, respectively calculating the stimulating response of central neurons of the X cell and the Y cell through the combined modulation of the classical receptive field and the non-classical receptive field, and performing addition of the stimulating response of central neurons of the X cell and the Y cell to take as a corresponding contour value; and G performing processing of the contour values of each pixel point to obtain a final contour value. The contour detection method based on the non-classical receptive field and the linear nonlinear modulation overcomes the defect that the contour recognition rate is low in the prior art, and has good simulation effect and high contour recognition rate.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a contour detection method based on nonclassical receptive field and linear nonlinear modulation. Background technique [0002] The contour defines the shape of the target, and the contour is one of the important tasks in target recognition, and obtaining the target contour from the cluttered scene is an important and quite difficult task, mainly because there are usually a large number of textured background edges around the contour, so This work mainly needs to exclude nonsensical edges due to textured regions while preserving object contours. The key to improving the detection rate is to optimize the integration of local information into consistent global features based on context. The human visual system has the ability to quickly and effectively extract contour features from complex scenes, which effectively promotes the development of contour detection algorithms inspired b...

AI Technical Summary

Problems solved by technology

[0004] The present invention aims to provide a contour detection method based on non-classical receptive field and linear nonlinear modulation, which overcomes the defects of poor simulation effect and low contour recognition rate in the prior art, and has the characteristics of good simulation effect and high contour recognition rate

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