Object Segmentation Method Based on Prior Shape and Cyclic Shift

Abstract

The invention relates to a target segmentation method based on priori shape and cyclic shift. The method comprises the steps that a shape is defined through a probability method; the shape library of a priori target is established, and principal component analysis is used to reduce the dimension; a priori shape constraint term is established by fitting the priori shape distribution through kernel density estimation; the cyclic shift of the prior shape is used to regard target displacement and rotation transformation are cyclic convolution operations; and the operation speed is improved through fast Fourier transformation; a data constraint term is established by combining target deformation and an underlying gray feature; the data constraint term and the prior shape constraint term are linearly combined to establish a total energy function; and finally target segmentation is completed through energy minimization. According to the invention, the cyclic shift of the high-level priori shape is used to assist underlying target segmentation, which solves the problem of poor segmentation effect of the existing target segmentation method when the target shape is deformed.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to an object segmentation method based on prior shape and cyclic shift. Background technique [0002] Object segmentation refers to extracting objects of interest from image information, which is a basic problem in computer vision. It has been widely used in military guidance, robotics, medical diagnosis and intelligent monitoring and other fields. Early object segmentation algorithms mainly relied on the underlying image data information (gray value, texture and edge features, etc.), and some classic segmentation algorithms appeared, such as threshold segmentation algorithm, region segmentation algorithm and edge segmentation algorithm. In practical applications, due to interference factors such as noise, blocking, and background clutter, satisfactory results cannot be obtained only by relying on the underlying segmentation algorithm. It becomes important to utilize high-...

AI Technical Summary

Problems solved by technology

The internal alignment method is to normalize the target shape and prior shape by selecting a reference position and rotation direction. This type of method is very sensitive to the reference position and main direction. In the case of interference, it is easy to have incorrect alignment.

The branch and bound method is actually similar to the poor search method. The results of this type of method are more accurate, but the amount of calculation is huge.

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