Image partition method based on neighbor-hood PCA (Principal Component Analysis)-Laplace

Abstract

The invention discloses an image partition method based on neighbor-hood PCA (Principal Component Analysis)-Laplace. The method comprises the steps of carrying out PCT on an original image to obtain a feature vector of each pixel; extracting the main ingredients of an image to effectively restrain noise; carrying out edge detection on the image by a Laplace operator so as to realize image segmentation. Compared with a traditional Sobel operator partitioning algorithm and an LOG operator partitioning algorithm, the method is used for carrying out the PCA on the image pixels to estimate parameter values in a de-noising process in a way of being independent of an empirical value, so as to effectively reduce the interference on the image by the noise, and simplify the computation complexity. Experimental results show that the method can improve the segmentation effect of the image and is strongly excellent in accuracy and robustness.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image segmentation method based on neighborhood principal component analysis-Laplace. Background technique [0002] Image segmentation technology has penetrated into all aspects of life. For example, in biological and medical image analysis, cells, tissues and organs need to be separated from the image to measure their shape and cross-sectional images, so as to perform quantitative and qualitative analysis and evaluation of diseased tissues. and forecast. With the development of visual information capture equipment and technologies such as video cameras, cameras, infrared rays, and various sensors, the acquisition of visual information has become a simple problem, but how to obtain the interesting part from a large number of visual information has become an important research direction. Especially when the acquired image contains noise, how to obtain more accurate de...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide an image segmentation method based on Neighborhood Principal Component Analysis-Laplace for the defects in the prior art. This method solves the problem of edge extraction in the existing similar segmentation algorithms based on edge extraction For noise-sensitive problems, use neighborhood principal component analysis to denoise the image, and then use the Laplacian operator to extract the edge of the image, thereby realizing image segmentation and improving the quality of image segmentation

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