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

Image Segmentation Method Based on Geometric Block Interval Co-occurrence Features and Semantic Information

A technology of image segmentation and semantic information, applied in the field of image processing, can solve the problems of random changes in size and direction

Active Publication Date: 2017-01-18
XIDIAN UNIV
View PDF1 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the existing methods need to be based on a certain neighborhood window and direction when extracting texture features from images, while the size and direction of natural animal textures change randomly

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 Geometric Block Interval Co-occurrence Features and Semantic Information
  • Image Segmentation Method Based on Geometric Block Interval Co-occurrence Features and Semantic Information
  • Image Segmentation Method Based on Geometric Block Interval Co-occurrence Features and Semantic Information

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0063] The present invention will be further described below in conjunction with the accompanying drawings.

[0064] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0065] Step 1, get the initial sketch of the natural image.

[0066] Enter as figure 2 As shown in a natural image, use the Primalsketch model to obtain the line segments representing changes in the image to form the initial sketch of the natural image, which contains a set of line segments with a single pixel width {S i ,i=1,2,...,n}, such as image 3 As shown, n represents the total number of line segments, and the value is 1709.

[0067] Step 2, in the initial sketch, construct geometric blocks with line segments as units, proceed as follows:

[0068] 2a) The process of building geometric blocks: for any line segment S in the sketch l , by len l Composed of points, take the sketch line segment as the symmetry axis, the length along the line segment directi...

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 an image segmentation method based on geometric block spacing symbiotic characteristics and semantic information. The method comprises the steps that an initial sketch model is used for obtaining a sketch image, geometric blocks are constructed by taking line segments forming a sketch line as a unit, then the geometric blocks are mapped to the corresponding position of an original image, spacing symbiotic matrixes based on the geometric blocks are extracted, the spacing symbiotic matrix of each geometric block is taken as the characteristics of the corresponding line segments, the characteristics are utilized for dividing the sketch line into the speckle semantic category and the general boundary category, semantic information classified by the sketch line is respectively utilized for guiding the image super pixels obtained by the over-segmentation method to be combined, statistic is carried out on color mean values of the super pixels which are combined by being guided by the sketch line with the speckle semantic category, according to the symbiotic statistic relation between each super pixel and the super pixel in the neighbourhood in color, further combining is carried out, and the final segmenting result is obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image segmentation method based on geometric block interval co-occurrence features and semantic information, which can be used for image processing, recognition and detection, especially images containing zebras, tigers and other stripe targets. Background technique [0002] Image segmentation is the technology and process of dividing an image into multiple regions with similar characteristics. It is a key step from image processing to image understanding, and it is the most basic and key technology for further processing such as target recognition, data compression, and transmission. Therefore, High-quality segmentation methods are very important for natural image processing. In images containing targets such as zebras and tigers, the stripes on the zebras and tigers have the characteristics of two different colors appearing alternately, which makes it difficult to seg...

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 刘芳李玲玲郑莹焦李成郝红侠戚玉涛武杰段一平马晶晶尚荣华于昕
Owner XIDIAN UNIV
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