Method and device for extracting scale-invariant features and method and device for recognizing objects

A technology of scale-invariant features and extraction methods, applied in the field of image processing, can solve problems such as complex scenes and occlusions that are difficult to solve, and achieve the effect of facilitating target recognition and/or tracking

Inactive Publication Date: 2015-05-20
RICOH KK
View PDF5 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These features are based on two-dimensional image domain information, it is difficult to solve some complex scenes, such as occlusion

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 and device for extracting scale-invariant features and method and device for recognizing objects
  • Method and device for extracting scale-invariant features and method and device for recognizing objects
  • Method and device for extracting scale-invariant features and method and device for recognizing objects

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] In order to enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0030] Before further detailed introduction, in order to facilitate understanding, first introduce the core idea of ​​the present invention. The inventors found that, in object recognition or tracking, it is more convenient to use, for example, depth information and time domain information in pre-processing or post-processing stages, such as using depth information to preliminarily remove noise and the like. However, such depth information or temporal information is not suitable for direct application in machine learning such as classifiers. For example, in a classifier, it is usually necessary to train a classifier based on a sample represented by a feature vector, and then when a new image to be classified arrives, the feature vector in the image...

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 provides a method and a device for extracting scale-invariant features of images in video streams and a method and a device for recognizing objects on the basis of the method for extracting the features. The images comprise gray images and corresponding parallax images, correspond to moments t1 in the aspect of time, and include precedent images and posterior images in the video streams in the aspect of time. The method for extracting the features can include positioning critical points in the images; generating description regions around the critical points, and describing each description region around the corresponding critical point in four dimensions x, y, z and t; generating descriptors for each critical point on the basis of the description region of the critical point. Values of each description region in the range of each of the corresponding dimensions x, y, z and t are not equal to zero. The descriptors are used as the scale-invariant features of the critical points. The methods and the devices have the advantages that information of time domains, information of depth domains and information of image planes are closely combined with one another, so that the four-dimensional scale-invariant features can be extracted, and the methods and the devices are suitable to be applied to machine learning.

Description

technical field [0001] The present invention generally relates to image processing, and in particular, to feature extraction methods, object recognition methods and corresponding devices. Background technique [0002] Target recognition in real scenes has high requirements on local features, and requires features not to be affected by interference from nearby objects and partial occlusion. A major difficulty in object recognition is the selection of image features. [0003] The features used for object recognition are usually affected by the following factors: scale changes, image rotation, image blur, image compression, and brightness changes. [0004] There are many existing local features, including GLOH (Gradient Location Orientation Histogram, gradient position orientation histogram), SIFT (Scale Invariant Feature Transform, scale invariant feature transformation), direction controllable filter, etc. These features are based on two-dimensional image domain information...

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): G06K9/46G06T7/00
CPCG06V10/462
Inventor 贺娜刘媛师忠超王刚鲁耀杰
Owner RICOH KK
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap