Shape matching and target recognition method based on PCA-SC algorithm

Abstract

The invention discloses a shape matching and target recognition method based on a PCA-SC algorithm. The method comprises the steps of carrying out preprocessing on a target image, filtering part of noises in the target image, extracting the edge of the target image, extracting information of boundary contour points, working out the rectangular coordinate parameters of the contour points, converting the contour points from rectangular coordinates into polar coordinates, obtaining a corresponding logarithmic polar histogram of each point to forming a local feature descriptor, forming a covariance matrix, extracting a corresponding feature vector of a larger characteristic value of the matrix, adopting a linear transformation method to drop the matrix from high dimension to low dimension, forming a new characteristic matrix, wherein the new characteristic matrix is used for the shape matching and the target recognition, calculating matching degree, and obtaining a matching degree value between the target image and each template image. According to the shape matching and target recognition method based on the PCA-SC algorithm, characteristic extracting and effective representation for the image can be achieved, scale invariance, rotation invariance and translation invariance are achieved, accuracy rate and efficiency are improved, and interference of the noise is effectively restrained.

Description

technical field [0001] The invention relates to the technical field of shape matching, in particular to a shape matching and object recognition method based on PCA-SC algorithm. Background technique [0002] Machine vision cognition has always been a research hotspot. Object shape feature description is the main research object of machine vision and has been widely used in engineering, such as wide baseline matching, target category recognition, image and video retrieval, specific target recognition, robot Navigation, scene classification, texture recognition and data mining and many other fields. [0003] According to the source of the feature, the shape description method is divided into two categories: the method based on the contour and the method based on the transformation domain. The former features all come from the target contour, such as Moravec, Harris corner feature, contour perimeter, compactness, eccentricity, Hausdroff The latter uses the feature information ...

AI Technical Summary

Problems solved by technology

[0005] The Fourier shape descriptor is simple and efficient, but its ability to capture local features is not strong, and it is more sensitive to noise interference;

[0006] Due to the multi-resolution analysis characteristics of wavelet transform, the multi-scale wavelet shape descriptor can accurately obtain the overall and local features of the image, and the matching accuracy is high. However, the algorithm is not only complex, but also takes a long time to match, and is also sensitive to image translation and rotation. ;

[0007] The Shape Context (SC) algorithm is based on the concept of statistical information, has high matching recognition accuracy, has a strong ability to capture local features, and has translation invariance and rotation invariance, and overcomes Fourier shape descriptors and wavelet shapes. Some shortcomings of the descriptor, but the algorithm has high computational complexity and the ability to suppress noise is not strong

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