Image segmentation method based on PCA reconstruction error level set

Abstract

The invention belongs to the image processing technology field and discloses an image segmentation method based on a PCA reconstruction error level set. A to-be-segmented image is inputted; image characteristics are extracted; an evolution curve is initialized; base vectors of inner and outer regions of the image are obtained utilizing the PCA; each pixel of the image is reconstructed according tothe basis vectors; a reconstruction error of each pixel of the image is calculated; the reconstruction errors of the pixels are accumulated, and a data-driven energy item is constructed; a novel energy function is minimized, and curve evolution is driven to obtain the segmentation result. The method is advantaged in that the used PCA technology assumed image information satisfies Gaussian distribution with respect to image segmentation of a segmented constant model level set, a non-homogeneous image can be well segmented, and noise robustness is further realized; Gaussian model calculation ismore time-consuming compared with image segmentation of a Gaussian model level set, the operation speed of the used PCA technology is faster.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image segmentation method based on PCA reconstruction error level set. Background technique [0002] In the past half century, image segmentation has attracted people's attention and maintained a persistent research heat. So far, thousands of segmentation methods based on different theories have been proposed. Among them, the method based on active contour model is a kind of segmentation method which is very popular in current research. Fundamentally speaking, the active contour model method can be divided into two types: parametric type and geometric type. Parametric type refers to a curve or surface that directly expresses deformation in a parametric form; geometric type is to embed a low-dimensional curve into a higher one-dimensional surface. In , the surface is called a level set function, and the original curve is its zero level set, and is described by geometr...

AI Technical Summary

Problems solved by technology

However, the parameters of the Gaussian model are time-consuming to solve, resulting in a slow speed of this type of method

[0006] In summary, the problems in the prior art are: 1) The segmentation of inhomogeneous images is not accurate enough, and it is not robust enough to noise, for example, the image segmentation based on the level set of the piecewise constant model; 2) The operation speed is slow, for example, the image segmentation based on the level set of the Gaussian model

[0007] The difficulty and significance of solving the above technical problems: The difficulty in solving the above problems is not only to overcome the shortcomings of inaccurate segmentation of non-homogeneous images and not robust to noise, but also to overcome the shortcomings of slow running speed.

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