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

Image segmentation method based on non-subsampled contourlet and multiphase cv model

A non-subsampling contour and image segmentation technology, applied in the field of image processing, can solve problems such as high computational complexity, achieve strong robustness, good practicability, and improve segmentation accuracy

Inactive Publication Date: 2015-11-18
LIAONING NORMAL UNIVERSITY
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, since only the spatial features of the image are used, the CV model also contains a lot of irrelevant information, resulting in high computational complexity

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
  • Image segmentation method based on non-subsampled contourlet and multiphase cv model
  • Image segmentation method based on non-subsampled contourlet and multiphase cv model
  • Image segmentation method based on non-subsampled contourlet and multiphase cv model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] Embodiments of the present invention include the following steps:

[0032] agreement: I Refers to the image to be segmented; R representative image I The entire region of , and a simple image segmentation satisfies R=R 1 ∪R 2 ; The number of decomposition layers of NSCT transform is n ; The parameters to be estimated in the Gaussian mixture model of the NSCT transformation coefficient ; m=1,2 are the two states of the size of the coefficient; is the prior probability of the mixed model and satisfies ; and are the mean and variance in the training process of the two states, respectively; c is the evolution curve of the active contour model; and is a positive weighting coefficient; For a piecewise constant function:

[0033] ,

[0034] in and Representation and Contour Curves c The relevant constant values ​​generally take the average gray value inside and outside the contour curve;

[0035] Such as figure 1 Shown:

[0036] a.Initial se...

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 an image segmentation method based on non-subsmapled contourlet and multi-phase chan-vese (CV) models. The method comprises the steps of firstly, performing multi-resolution representation on images to be segmented through non-subsmapled contourlet conversion; secondly, establishing probability models of multi-resolution coefficients; and finally, integrating the multi-resolution coefficients through active contour models based on domains to segment images. Experiment results indicate that images can be well segmented, the global property of images can be guaranteed, and detail information of the images can be segmented.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to a non-subsampled contourlet transform (NSCT)-based active contour model image segmentation method that can not only ensure the globality of the segmented image, but also segment out the detailed information of the image. Background technique [0002] Image segmentation is the basis of computer vision, and higher-level image analysis and understanding can be made possible through segmentation technology. Image segmentation technology has a very wide range of applications, such as remote sensing satellite image processing, traffic monitoring, military, agriculture and so on. In the past two decades, multi-resolution analysis methods and variational methods are more popular image segmentation methods. The image segmentation technology for multi-resolution analysis first transforms the input image (specific transformations include wavelet transform, contourlet transform, and ...

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 王相海方玲玲宋传鸣倪培根王金玲
Owner LIAONING NORMAL UNIVERSITY
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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com