Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method for dividing level set image based on characteristics of neighborhood probability density function

A technology of probability density function and image segmentation, which is applied in the field of image processing, can solve the problems of inability to accurately segment natural texture images, and cannot effectively deal with the variability of texture scale and texture direction of natural image texture primitives, and achieve the effect of accurate segmentation

Inactive Publication Date: 2011-04-06
XIDIAN UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, for real natural texture images, the biggest deficiency of the existing level set image segmentation methods is that they cannot effectively deal with the variability of texture primitives in natural image textures in terms of texture scale and texture direction, resulting in the inability to accurately segment natural texture images.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for dividing level set image based on characteristics of neighborhood probability density function
  • Method for dividing level set image based on characteristics of neighborhood probability density function
  • Method for dividing level set image based on characteristics of neighborhood probability density function

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0027] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0028] Step 1. Initialize the level set function φ 0 .

[0029] Select any area R in the input image I as the initial target area, and the area I-R outside the initial target area is the initial background area, and the initial level set function φ 0 :

[0030] φ 0 ( x , y ) = - c , ( x , y ) ∈ R c , else

[0031] Among them, c is a normal number, and the level set function φ 0 (x, ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a method for dividing a level set image based on characteristics of a neighborhood probability density function, which mainly solves the problem existing in the prior method that the variability of texture elements of a natural image in texture scale and texture direction cannot be effectively processed. The method comprises the following steps of: initializing a level setfunction; setting an initial value and a maximum value for the iteration number of the level set function; working out a characteristic set of the neighborhood probability density function; working out characteristics of a subset probability density function; working out a similarity vector; according to an evolution equation of the level set, updating the level set function; judging whether to enter a cycle, if so, working out the characteristics of the subset probability density function, the similarity vector and the level set function again; otherwise stopping the cycle, and using a zero level set of the level set function as a segmentation border of an input image. Because the texture elements of the natural image are fit by a multi-scale sliding window, the variability of the textureelements in texture scale and texture direction can be better processed, so that the method can be applied to the segmentation of the natural texture image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to image segmentation, in particular to a level set image segmentation method based on neighborhood probability density function features. The method can be used in the technical field of image segmentation. Background technique [0002] Image segmentation is the basis of image understanding and pattern recognition. It is widely used in medicine, military, meteorology, climate, environment and other fields. It is a hot and difficult point in current research. So far, there are many kinds of image segmentation methods, which can be generally divided into two categories: traditional image segmentation methods and image segmentation methods combined with specific theoretical tools. Traditional segmentation methods usually include three categories: boundary detection-based, region-based and a combination of the two. Theoretical tools often used in segmentation met...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00
Inventor 王爽焦李成符升高钟桦侯彪田小林缑水平朱虎明苏开亮
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products