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

Image analysis method and device based on pcnn model

An image analysis and model technology, applied in image analysis, image data processing, instruments, etc., can solve the problems of poor adaptability of the PCNN model and achieve the effect of improving accuracy

Active Publication Date: 2017-02-15
赵彦明
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the embodiments of the present invention is to provide an image analysis method and device based on the PCNN model to solve the problem of relatively poor adaptability of the above-mentioned PCNN model

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 analysis method and device based on pcnn model
  • Image analysis method and device based on pcnn model
  • Image analysis method and device based on pcnn model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0022] Hereinafter, the present invention will be described in detail with reference to the drawings and in conjunction with the embodiments. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other if there is no conflict.

[0023] Considering that the PCNN model is based on the biological optic neurocortex theory, the network parameters M and W can effectively describe the global information of the image, and can also describe the change relationship of each pixel relative to the neighboring pixels. Therefore, according to the biological vision contained in the image itself The information self-adaptive setting network parameters W and M can accurately describe the characteristics of the image itself globally and locally. Based on this, the embodiment of the present invention provides an image analysis method and device based on the PCNN model.

[0024] Such as figure 1 The shown flow chart of the image analys...

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 an image analysis method and device based on a PCNN model. The image analysis method comprises the steps that a pixel is randomly selected from a current image; the maximum energy of the pixel and corresponding direction channels in adjacent domains is calculated by means of changing the size of a window of a visual information calculation model; the maximum size and effective direction of the visual information calculation model are determined according to the maximum energy of each direction channel; parameters W and M of the PCNN model are determined according to the maximum size and effective direction, wherein the M is a connection matrix of a feedback input domain, and the W is a connection matrix of a coupling connection domain; the current image is subjected to image analysis based on the PCNN model corresponding to the determined W and M. The image analysis method and device based on the PCNN model solve the problem that in the correlation technique, the self-adaptivity of the PCNN model is relatively poor.

Description

Technical field [0001] The invention relates to the field of image processing and analysis, in particular to an image analysis method and device based on a PCNN (Pulse Coupled Neural Networks, pulse coupled neural network) model. Background technique [0002] The parameters in the PCNN model can determine the performance of the model in the fields of digital image understanding, analysis and pattern recognition. Therefore, a variety of research schemes for setting PCNN network parameters have been proposed in related technologies. According to the research content, it can be divided into: 1) Adaptive setting method of one or more parameters of PCNN according to the statistical information of the image; 2) Setting method of PCNN parameters according to the receptive field; 3) Setting according to the structural characteristics of the PCNN model Method of setting network parameters. [0003] Most of the above-mentioned PCNN models are parameter settings for images in a specific fie...

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 赵彦明
Owner 赵彦明
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