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

A Robotic Scene Recognition Method Based on Scale Invariant Feature Extreme Learning Machine

A scale-invariant feature, extreme learning machine technology, applied in the field of image recognition, can solve problems such as inability to recognize and match images, large original dimensions of images, etc., and achieve the effect of solving the training speed is too slow

Active Publication Date: 2018-06-08
WUHAN INSTITUTE OF TECHNOLOGY
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

After the scene image is obtained by electronic equipment, we cannot directly identify and match the image due to the large original dimension of the image and the existence of redundant information

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
  • A Robotic Scene Recognition Method Based on Scale Invariant Feature Extreme Learning Machine
  • A Robotic Scene Recognition Method Based on Scale Invariant Feature Extreme Learning Machine
  • A Robotic Scene Recognition Method Based on Scale Invariant Feature Extreme Learning Machine

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0055]In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0056] A robot scene recognition method based on a scale-invariant feature extreme learning machine, comprising the following steps:

[0057] Step 1, in the embodiment, the images are used to randomly select 500 pictures from the ImageCLEF library. 450 of them are used as training samples and 50 are used as testing samples. We first enhance and normalize the image to obtain a standardized image with the same size and the same gray value.

[0058] Step 2: Scale-invariant feature conversion is performed on the training images, and the scale-invariant features of all training images are combined to form a new matrix set. The...

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 discloses a scale-invariant feature extreme learning machine-based robot scene recognition method. The method comprises the following steps: firstly, expressing a scene of a robot visual image by using a scale-invariant feature; secondly, realizing a robot scene image expression codebook by using a K mean clustering algorithm; finally, establishing mapping relation between the robot visual scene image and a scene label by using the extreme learning machine algorithm. According to the method, the complexity of parameter estimation and optimization of a conventional neural network is reduced, the training time is further shortened and the recognition rate of the robot scene image is improved by using the advantages of the extreme learning machine.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a robot scene recognition method based on a scale-invariant feature extreme learning machine. Background technique [0002] Vision is the most intuitive and effective way for humans to observe and understand the world. Robot vision simulates the function of human vision to perceive and recognize the outside world from one or more images. As an important part of the field of robot vision, the recognition of scenes in images has been the focus of many researchers. In recent years, robot scene recognition technology has been widely used in criminal investigation systems of public security departments, medical image processing, 3D modeling, industrial inspection and other fields. [0003] The robot scene recognition application technology is to give an input scene image and recognize its registered scene category information. After the scene image is obtained by electroni...

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): G06F17/10G06K9/00G06K9/62
Inventor 卢涛杨威张彦铎李晓林万永静余军鲁统伟闵锋周华兵朱锐李迅魏运运黄爽段艳会张玉敏
Owner WUHAN INSTITUTE OF TECHNOLOGY
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