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

K-means clustering-based target recognition method

A target recognition and K-means technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as low recognition rate and vulnerability to environmental influences

Active Publication Date: 2017-05-17
TIANJIN JINHANG COMP TECH RES INST
View PDF3 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide a target recognition method based on K-means clustering to improve the recognition rate of sea targets in view of the problems of low recognition rate and easy to be affected by the environment in the traditional sea surface target recognition method

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
  • K-means clustering-based target recognition method
  • K-means clustering-based target recognition method
  • K-means clustering-based target recognition method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0030] In order to make the purpose, content, and advantages of the present invention clearer, the specific implementation manners of the present invention will be further described in detail below in conjunction with examples.

[0031] Color is usually described by three relatively independent attributes, and the combined effects of three independent variables constitute a spatial coordinate. The traditional color space for image recognition is RGB space, which is mainly used in monitors, televisions, projectors and other equipment. This space is closest to human subjective visual experience and is easy to use. However, since each color in the RGB space has a nonlinear correlation, and it is not easy to display the influence of the surrounding environment, such as lighting, reflection, etc., it is difficult to extract colors in a complex background environment. Therefore, in the image preprocessing stage, the HLS color space conversion can be used for the image. In this space...

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 digital image object recognition optimization method technical field and relates to a K-means clustering-based target recognition method. The invention aims to solve problems such as low recognition rate and high possibility of being affected by environments of a traditional sea-surface target recognition algorithm. According to the K-means clustering-based target recognition method of the invention, the RGB color space of an original image is converted into an HLS color space, so that influence on target recognition caused by sea surface, sky background color and the brightness of light can be eliminated; the clustering center of a classifier is gradually optimized through an ant colony algorithm; and therefore, target recognition rate can be improved, and the robustness of the algorithm can be enhanced.

Description

technical field [0001] The invention belongs to the technical field of optimization methods for digital image target recognition, and relates to a target recognition method based on K-means clustering. Background technique [0002] At present, our country attaches great importance to the development of marine resources. The monitoring of the situation of the ocean water surface and the identification of sea targets are a powerful guarantee for the efficient exploitation of marine resources. Maritime targets generally refer to ships or floating objects at sea, and the monitoring of maritime targets requires digital image target recognition technology. Target recognition refers to the use of special attributes of the target of interest in the digital image to identify the target, and its properties include shape, color, position, etc. [0003] The image target recognition steps mainly include image preprocessing, feature extraction, classifier design, and classification decis...

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): G06K9/62
CPCG06F18/21G06F18/23213
Inventor 李鹏
Owner TIANJIN JINHANG COMP TECH RES INST
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