Goal-oriented automatic high-precision edge extraction method

Abstract

The invention discloses a goal-oriented automatic high-precision edge extraction method. The goal-oriented automatic high-precision edge extraction method comprises the stage of model training and the stage of edge extraction. The stage of model training comprises the following steps of (A1) training of a cascade classifier based on HAAR features, (A2) training based on a Canny operator and an ASM and (A3) training of an active contour model. The stage of edge extraction comprises the following steps that (B1) non-target components of an image to be processed are rapidly eliminated through a cascade structure; (B2) the initial position of a target edge is found by means of combination of the Canny operator and the ASM; (B3) the initial position is calibrated through the active contour model; (B4) samples which do not meet the edge extraction requirement are used as training samples of a database and used for feedback regulation of a whole system.

Description

technical field The invention relates to image detection technology, in particular to a target-oriented automatic high-precision edge extraction method. Background technique Edge is the most important feature parameter for people to describe and recognize objects in images (such as faces, hands, various objects, etc.). The extraction of digital image edge information (edgeextraction), that is, edge detection (edgedetection) is of great significance in image segmentation and overall target operation (target selection, overall copy, cutting, etc.). Most of the current edge information extraction methods use edge extraction operators (such as Sobel operator, Kirsch operator, Prewitt operator, Roberts operator, Canny operator, etc.). In the edge information extraction method based on edge extraction operators, specific operators can only detect edge information in a specific direction, and are sensitive to noise, especially in complex backgrounds, where the edge extraction ac...

AI Technical Summary

Problems solved by technology

The technical problem to be solved by the present invention is: to make up for the deficiencies of the above-mentioned prior art, first adopt the method based on the cascade detection to determine the approximate position of the target, reduce the interference of the background to the edge information of the target; then use the classic edge extraction operator and the ASM model [4] The method of fusion initially locates the edge of the image to solve the problem that the active contour model is sensitive to the initial position; then uses the active contour model to extract edge information to solve the problem that the traditional edge extraction operator does not use the prior information of the target; finally, the In the process of edge extraction, samples that do not meet expectations are sent back to the model training subsystem, and continuous learning makes the whole system more and more intelligent, which solves the problem of lack of intelligence in traditional methods

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