Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Stereo matching method based on self-adaptive weight and local entropy

An adaptive weight and stereo matching technology, applied in image data processing, instruments, calculations, etc., can solve problems such as low precision and high computational complexity

Active Publication Date: 2018-01-12
KUNMING UNIV OF SCI & TECH
View PDF6 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is to propose a stereo matching method based on adaptive weights and local entropy to solve the problems of low precision and high computational complexity of the current traditional local matching algorithm

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
  • Stereo matching method based on self-adaptive weight and local entropy
  • Stereo matching method based on self-adaptive weight and local entropy
  • Stereo matching method based on self-adaptive weight and local entropy

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0058] Embodiment 1: as figure 1 As shown, a stereo matching method based on adaptive weights and local entropy, first redefines the calculation of window weights according to gray similarity and spatial proximity under the framework of adaptive weight theory; then introduces in the cost function The data smoothing item and the image local entropy are used to determine its penalty coefficient; finally, the left-right consistency test and weighted median filter are used to refine the disparity for the initial disparity map obtained by cost aggregation to obtain a higher-precision disparity map;

[0059] Specific steps are as follows:

[0060] Step 1: Calculation of data items: When determining the weight value in each matching window, according to the principle of similarity and proximity under Gestalt theory, use the gray similarity and spatial similarity of the image to construct the weight value Calculation;

[0061] Step 2: Calculation of smoothing items: Based on the ass...

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 relates to a stereo matching method based on a self-adaptive weight and local entropy, belonging to the technical field of binocular stereo vision. The method includes redefining the calculation of the window weight according to the gray similarity and the spatial similarity under a self-adaptive weight theory framework; introducing a data smoothing item into the cost function, and determining the penalty coefficient of the data smoothing item by applying the image local entropy; and performing parallax refinement by applying left-right consistency check and weighted median filtering on the initial parallax map obtained by cost aggregation to obtain a high-precision parallax image. The accurate parallax map can be obtained, and the parallax accuracy is improved.

Description

technical field [0001] The invention relates to a stereo matching method based on self-adaptive weight and local entropy, which belongs to the technical field of binocular stereo vision. Background technique [0002] The essence of stereo matching is to search for the positions of points in different views after imaging in the space, and thus obtain the corresponding disparity images. Generally speaking, stereo matching algorithms can be divided into local matching algorithms and global matching algorithms. In general, the local algorithm has the advantage of fast algorithm speed, but the matching accuracy is not high in occlusion, no texture and repeated texture areas; the global algorithm has the advantage of high matching accuracy, but the disadvantage is that not only the calculation speed is much faster than most local matching Algorithms, and there are often many parameters whose values ​​are difficult to determine. For the local matching algorithm, the calculation a...

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 Applications(China)
IPC IPC(8): G06T7/33
Inventor 李文国陈田
Owner KUNMING UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
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