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

Stereoscopic-image visual significance extraction method based on frequency domain sparse representation

A stereoscopic image and sparse representation technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve problems such as staying

Active Publication Date: 2018-08-31
深圳牧野微电子技术有限公司
View PDF4 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the current visual saliency extraction method for stereo images still stays on the visual saliency extraction method for planar 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
  • Stereoscopic-image visual significance extraction method based on frequency domain sparse representation
  • Stereoscopic-image visual significance extraction method based on frequency domain sparse representation
  • Stereoscopic-image visual significance extraction method based on frequency domain sparse representation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0030] A method for visually salient extraction of stereoscopic images based on frequency-domain sparse representation proposed by the present invention, its overall realization block diagram is as follows figure 1 As shown, it includes the following steps:

[0031] ① For any test stereo image S test , the S test The left-viewpoint image of is transformed into the Lab color space, and the size is transformed to 200×200 pixel size, and the transformed image is denoted as {L Lab200 (x 1 ,y 1 )}; then put {L Lab200 (x 1 ,y 1 )}'s L-channel image, a-channel image, and b-channel image are correspondingly denoted as {L Lab200,L (x 1 ,y 1 )}, {L Lab200,a (x 1 ,y 1 )}, {L Lab200,b (x 1 ,y 1 )}; among them, S test The width is W, S test The height is H, 1≤x 1 ≤200, 1≤y 1 ≤200, L Lab200 (x 1 ,y 1 ) means {L Lab200 (x 1 ,y 1 )} ...

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 stereoscopic-image visual significance extraction method based on frequency domain sparse representation. The method is characterized by acquiring the small-sized three-channel image of the left-viewpoint image of a testing stereoscopic image and a small-sized normalized parallax image; carrying out partitioning on the three-channel image and the normalized parallax imageand acquiring matrixes corresponding to partitioning images; using the four matrixes to form a quaternion matrix, acquiring a transformed quaternion matrix after two-dimensional quaternion Fourier transform, and according to the low frequency components of a real portion and three imaginary portions, acquiring a low frequency component image; using a sparse expression dictionary to extract the sparse weight matrix of the low frequency component image; according to the sparse weight matrix and a center preference image, acquiring two center and periphery significance images; carrying out fuzzyprocessing on the two center and periphery significance images and then fusing and acquiring a fusion image; using the center preference image to reinforce the center and periphery of the fusion image; and acquiring a visual significance image after center and periphery reinforcement image size conversion. The method has advantages of high extraction stability and good extraction accuracy.

Description

technical field [0001] The invention relates to an image signal processing method, in particular to a method for extracting stereoscopic image visual salience based on frequency domain sparse representation. Background technique [0002] After people receive natural images, because the human brain needs to treat different levels of information resources differently, when processing natural image information, people will classify different information to show the characteristics of selection. When people watch an image or a video clip, their attention is not evenly distributed to each area of ​​the image, but the semantic information part that is more interesting is prioritized. Calculating salient regions of images is an important research content in the field of computer vision and content-based video detection. With the rapid development of stereoscopic image projection and acquisition equipment, visual saliency detection of stereoscopic images has become a very important...

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
IPC IPC(8): G06K9/46
CPCG06V10/464G06V10/513G06V10/40
Inventor 周武杰蔡星宇张爽爽顾鹏笠潘婷郑飘飘吕思嘉袁建中陈昱臻胡慧敏金国英王建芬王新华孙丽慧吴洁雯
Owner 深圳牧野微电子技术有限公司
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