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

Method for extracting and describing image characteristics with turnover invariance

An image feature extraction and image technology, which is applied in the field of image processing, can solve problems such as the failure of SIFT feature extraction methods, and achieve the effects of increasing robustness, speed, and expanding application fields

Inactive Publication Date: 2010-03-03
TIANJIN UNIV
View PDF0 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] The purpose of the present invention is to provide a kind of problem that can solve the invalidation of SIFT feature extraction method caused by the phenomenon of mirror flipping, and keep all the advantages and performances of SIFT at the same time

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
  • Method for extracting and describing image characteristics with turnover invariance
  • Method for extracting and describing image characteristics with turnover invariance
  • Method for extracting and describing image characteristics with turnover invariance

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] First, the scale of the input image is changed. This change is done through Gaussian convolution. In a series of images of different scales, the extreme point of the gray value of the pixel is found for each pixel. However, not all extreme points meet the criteria as feature points. Since the feature points need to have certain prominence and robustness, by setting appropriate thresholds for Difference of Gaussian and Hessian matrix , so as to filter the candidate points with low contrast and edge response of feature points respectively. The extreme points left after these two steps of screening are the desired feature points. For these extreme points, after fitting them with a three-dimensional quadratic curve, their precise coordinates and scale information are obtained. The coordinates, scale and other information of the feature points are preserved to provide usable information for the subsequent matching stage.

[0032] The above part can be considered as a featur...

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 belongs to the technical field of image processing and relates to a method for extracting and describing image characteristics with turnover invariance. The method comprises the following steps of: (1) carrying out Gaussian kernel convolution processing on the input image; (2) carrying out Gaussian processing on the image continuously and measuring the extreme point; (3) screening feature points; (4) precisely positioning the position of the feature point; (5) determining the direction parameter of each feature point; (6) summing the gradient magnitudes taking the principal directions as the boundary line at two sides respectively; and (7) dividing auxiliary regions, organizing pixel units and carrying out coding and normalized operation, thus generating the describing data on the image. The method increases the haleness of feature extraction and description method on the mirror surface imaging problem and enlarges the application field of computer vision.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to an image feature extraction method. Background technique [0002] Today's computer technology is developing rapidly, and the application fields of computer vision and image retrieval are becoming more and more extensive, which also highlights their importance. Popular 3D reconstruction, object recognition, camera calibration, robot binocular navigation, etc. are all based on computer vision, so reasonably and effectively solving the problems existing in computer vision or improving imperfections can give the computer industry even A huge boost from the scientific community. Computer vision is based on the idea that the computer can simulate human (mammalian) vision to achieve a certain degree of intelligence. Together with image retrieval, it is necessary to extract and analyze the features of the image, so the definition and extraction scheme of image features has a pivo...

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): G06T7/00G06K9/46
Inventor 操晓春郭晓杰孙济州孙美君
Owner TIANJIN 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