A WKPCA homogeneity-corrected NCRF-suppressed salient contour extraction method for low-light images

Abstract

The invention discloses a method for extracting salient contours of low-light images suppressed by WKPCA homogeneity correction nCRF. First, a WKPCA algorithm is proposed, which performs feature vector angle matching (FAM) weighting on each feature vector in the high-dimensional feature space, weakens or eliminates the interference of pathological or abnormal feature data in the CRF region, and extracts the principal components of the CRF more accurately; here Based on this, a concept and calculation method of homogeneity is defined, and the environment-center homogeneity is calculated through the projection of nCRF eigenvectors on the central principal component; finally, based on the homogeneity, each suppression amount in nCRF is corrected, so that the homogeneity The amount of mutual inhibition in the homogeneous area is large, the amount of inhibition in the heterogeneous area is small or no mutual inhibition, and at the same time, the self-inhibition of the contour elements is weakened as much as possible, so as to improve the accuracy of the inhibition. Therefore, the model proposed by the present invention can detect environment-center differences more comprehensively, reduce noise interference, suppress texture details more accurately, and improve contour response strength and integrity.

Description

technical field [0001] The invention belongs to a method for extracting salient contours of low-light images in complex scenes based on visual modeling, in particular to a method for extracting salient contours of low-light images suppressed by WKPCA homogeneity correction nCRF. Background technique [0002] Contour extraction plays an important role in understanding and analyzing night vision images. At present, most of the applications of night vision target detection and recognition are aimed at natural scenes, so low-light images contain a large number of natural textures (such as trees and grass), and the results of traditional edge detection operators retain a large number of non-contour edges. Composition (canny operator). Moreover, the low-light image itself has strong noise interference. How to remove these local uninteresting edges generated by texture and noise and maintain the integrity of the contour is the main problem for night vision image contour detection....

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method for extracting salient contours of low-light images suppressed by WKPCA homogeneity correction nCRF, which can solve the deficiency of single azimuth weighted suppression, combined with the mechanism of biological vision, aiming at multi-dimensional feature differences of low-light images, from complex scenes Effectively suppress noise and texture in low-light images and extract salient contours

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