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

An Image Texture Classification Method Based on Jump Subdivision Partial Mode

An image texture and local pattern technology, applied in the field of computer vision and pattern recognition, can solve the problem of limited application scope of image texture method, and achieve the effect of fast calculation speed, easy implementation and small feature dimension.

Active Publication Date: 2022-01-28
HENAN UNIV OF SCI & TECH
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide an image texture classification method based on jump subdivision local mode, which solves the problem that the application range of existing image texture methods is relatively limited

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
  • An Image Texture Classification Method Based on Jump Subdivision Partial Mode
  • An Image Texture Classification Method Based on Jump Subdivision Partial Mode
  • An Image Texture Classification Method Based on Jump Subdivision Partial Mode

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] An image texture classification method based on jumping sub-partial mode, including the following steps:

[0046] Step 1: Jumping Local Differential Counting Features (JLDCP), including second-order differential count features (SDCP), and diagonal dimension counting features (DDCP):

[0047] Calculate the second order differential count (SDCP): It is mainly to extract features within the spatial range of the local area, and is achieved by the differential relationship between the neighboring pixel points of the central pixels within the local area. In theory, enough differential operations can extract instability information in the image, but the more the number is, the better, the difference is the refining extraction of the information, and some features will be lost, so it is appropriate to actually use. First, first specify a position of a neighbor pixel point, then do a difference with the previous pixel point, then the designated neighbor pixel point is different from...

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

An image texture classification method based on jumping subdivision local patterns, first extracts jumping local difference counting features (JLDCP) information through second-order difference counting features and diagonal difference counting features, and then subdivides the complete local binary features Extract the subdivided complete local binary feature (RCLBP) from the sign information and size information of Texture Descriptor for Differentiated Local Patterns (JRLP). Beneficial effects of the present invention: the present invention is robust to image noise, rotation, scale and illumination changes and the like.

Description

Technical field [0001] The present invention relates to the field of computer vision and pattern recognition, and is specifically a method of image texture classification based on jumping sub-block mode. Background technique [0002] Texture is a visual feature that is difficult to describe, and is repeated in accordance with the basic unit of a certain rule. People have the following consensus, periodic, directional, regional and scale. People generally divide textures into three major categories, natural textures, artificial textures, mixed textures. Texture classification process is generally simple summary: first to read all texture pictures. Second, according to the construction method of texture characteristics, the characteristics of each texture image are constructed. The picture feature is then divided into training sets and test sets, and finally utilize the classifier, and classify the pictures in the test set according to the classification, such as the nearest neighb...

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): G06V10/44G06V10/50G06V10/30G06K9/62G06V10/764
CPCG06V10/30G06V10/467G06V10/50G06V10/44G06F18/24147
Inventor 董永生王田玉杨春蕾梁灵飞郑林涛谢国森刘中华王琳宋斌
Owner HENAN UNIV OF SCI & TECH
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