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

An image texture classification method based on jump subdivision local pattern

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

Active Publication Date: 2019-01-25
HENAN UNIV OF SCI & TECH
View PDF8 Cites 6 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 local pattern
  • An image texture classification method based on jump subdivision local pattern
  • An image texture classification method based on jump subdivision local pattern

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] A method for classifying image textures based on jump subdivision local patterns, comprising the following steps:

[0046] Step 1: Jumping local differential counting features (JLDCP), including second-order differential counting features (SDCP) and diagonal differential counting features (DDCP):

[0047] Calculation of second-order differential counting features (SDCP): It mainly extracts features in the spatial range of the local area, and realizes the differential relationship between the neighboring pixels of the central pixel in the local area. In theory, enough differential operations can extract the unstable information in the image, but the more times the better, the differential is the refined extraction of information, and some features will be lost, so it should be used appropriately in actual use. First, first specify the position of a neighbor pixel, then perform a difference operation with the previous pixel point, then perform a difference operation betwe...

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 jump subdivision local pattern includes:firstly, skipping local difference counting feature (JLDCP) information is extracted from the second-order difference counting feature and diagonal difference counting feature, then the subdivision complete local binary feature (RCLBP) is extracted from the symbol information and size information of the subdivision complete local binary feature, finally, the texture descriptors (JRLP) of skipping and subdivision local patterns are obtained by connecting the skipping local difference counting feature (JLDCP)and the subdivision complete local binary feature (RCLBP). The invention has the beneficial effects that the invention has robustness to image noise, rotation, scale and illumination change and the like.

Description

technical field [0001] The invention relates to the technical fields of computer vision and pattern recognition, in particular to an image texture classification method based on jumping and subdividing local patterns. Background technique [0002] Texture is a ubiquitous and indescribable visual feature, which is the repetition of basic units according to certain rules. People have the following consensus on its attributes, periodicity, directionality, regionality and scale. People generally divide textures into three categories, natural textures, artificial textures, and mixed textures. The texture classification process can generally be briefly summarized as follows: First, all texture images must be read in. Secondly, according to the construction method of the texture feature, the feature of each texture image is constructed. Then divide the picture features into training set and test set, and finally use a classifier to classify the pictures in the test set according...

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): G06K9/46G06K9/62G06K9/40
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