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

Image Strong and Weak Edge Detection Method Based on Lattice Neuron Response Spatial-Temporal Information

An edge detection and neuron technology, applied in the field of image processing, can solve the problems of image over-segmentation, loss of weak details, etc., to achieve the effect of satisfying subjective evaluation

Active Publication Date: 2016-04-06
永春县产品质量检验所福建省香产品质量检验中心国家燃香类产品质量监督检验中心福建
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional image edge detection uses the gradient method such as Roberts operator to process the image, which can detect the strong edge of the image, but usually loses the weak details, and sometimes causes the image to be over-segmented

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 Strong and Weak Edge Detection Method Based on Lattice Neuron Response Spatial-Temporal Information
  • Image Strong and Weak Edge Detection Method Based on Lattice Neuron Response Spatial-Temporal Information
  • Image Strong and Weak Edge Detection Method Based on Lattice Neuron Response Spatial-Temporal Information

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0018] The following is attached figure 1 The present invention is described further, in figure 1 Where I_old(i,j) represents the original input image; f k (i,j)(k=0,1,...,7) is the Log-Gabor filter with angle θ i (θ i =22.5 0 *i,i=0,1,...,7) filtered results; Neuron(i,j) indicates the lattice neuron used; T(i,j) indicates the first time recorded after the lattice neuron model The time matrix of discharge time; D(i,j) represents the variance matrix after variance processing; F(i,j) represents the edge matrix after neuron lateral inhibition; I_new(i,j) represents the final result image .

[0019] The concrete steps of the inventive method are:

[0020] Step (1) makes the original image I_old(i,j) (i=1,2,...,M;j=1,2,...,N) preprocessed by the Log-Gabor filter to obtain The angle is θ i (θ i =22.5 0 *i,i=0,1,...,7) results in 8 directions, denoted as f k (i,j)(k=0,1,...,7). Then use formula (1) to reconstruct the edge information of the image:

[0021] ...

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 relates to an image strong and weak edge detection method based on spatio-temporal information responded by dot matrix nerve cells. Firstly, a multi-direction Log-Gabor filtering result of an image in a view is utilized and edge information of the image is reconstructed; a reconstruction result serves as input of the dot matrix nerve cells; time in releasing action potentials of all the nerve cells is recorded to form a time matrix; a constructed reception field window slides on the time matrix, the improved variance is calculated according to a time order of all time elements and a center point of the window undergoes assignment and thus, a variance matrix containing nerve cell responding time and the spatio-temporal information is obtained; afterwards, the reception field window continues sliding on the variance matrix, the side direction rejection characteristic of the nerve cells in space is achieved and an edge matrix is obtained; finally, the edge matrix is mapped into a result image inversely. According to the image strong and weak edge detection method based on the spatio-temporal information responded by the dot matrix nerve cells, the spatio-temporal information responded by the dot matrix nerve cells is taken into consideration, the edges of the image can be detected and additionally, a strong and week relationship of the edges can be effectively reflected.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image strong and weak edge detection method based on lattice neuron response time and space information. Background technique [0002] The edge of the image refers to the place where the color or gray value changes abruptly, which usually reflects the main information of the image, so the effective detection of image strength and edge information is useful for subsequent image processing and other related tasks, such as pattern recognition, Object tracking etc. are crucial. The traditional image edge detection uses the gradient method such as Roberts operator to process the image, which can detect the strong edge of the image, but usually loses weak details, and sometimes produces over-segmentation of the image. Contents of the invention [0003] In view of the above problems, the present invention proposes a method for detecting image strength edges based on lattice neuron re...

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 范影乐廖进文方芳罗佳骏武薇
Owner 永春县产品质量检验所福建省香产品质量检验中心国家燃香类产品质量监督检验中心福建
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