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Image border testing algorithm based on cellular automata

A cellular automata and image edge technology, which is applied in image analysis, image data processing, calculation, etc., can solve the problems of reducing edge detection accuracy, affecting processing results, and interfering with noise sensitivity, so as to overcome the defect of noise sensitivity and prolong Number of iterations, effect of improving accuracy

Inactive Publication Date: 2009-03-04
SHANGHAI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, since the second-order derivative is more sensitive to interference noise than the first-order derivative, when the image has certain noise interference, using the second-order derivative to detect the edge of the image will generate a large number of false zero-crossing points, thereby reducing the accuracy of edge detection.
In addition, when calculating the derivative of the image, the calculation result is a continuous value distributed in the interval [0, 255], while the gray value of the image is a discrete value distributed in the discrete space {0, 1, 2, ..., 255} value, which leads to rounding errors and affects the final processing results

Method used

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  • Image border testing algorithm based on cellular automata
  • Image border testing algorithm based on cellular automata
  • Image border testing algorithm based on cellular automata

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] Embodiment 1: Edge detection of nuclear magnetic resonance image

[0042] 1) Convert the image (such as image 3 Shown) Each pixel point is regarded as a cell; each cell and its four neighbors form a Moore neighborhood (such as figure 1 .a); The state set of each cell is defined as the number of particles that may be contained in the cell; the number of particles contained in each cell is greater than 0 and less than 256.

[0043] 2) Initialization, defining that the initial state of each cell is equal to the gray value of the corresponding pixel in the image.

[0044] 3) At time t, generate a random sequence u x，y (i)={1, 2, 3, 4, ...}, where u x，y (i) The probability of equaling 1, 2, 3, and 4 is 1 / 4.

[0045] 4) Calculate the number of particles moving to its four neighbors in the cell N i ( x , y ) = Σ j ...

Embodiment 2

[0065] Embodiment 2: edge detection of CT image

[0066] 1) Convert the image (such as Figure 5 Shown) Each pixel point is regarded as a cell; each cell and its four neighbors form a Moore neighborhood (such as figure 1 .a); The state set of each cell is defined as the number of particles that may be contained in the cell; the number of particles contained in each cell is greater than 0 and less than 256.

[0067] 2) Initialization, defining that the initial state of each cell is equal to the gray value of the corresponding pixel in the image.

[0068] 3) At time t, generate a random sequence u x，y (i)={1, 2, 3, 4, ...}, where u x，y (i) The probability of equaling 1, 2, 3, and 4 is 1 / 4.

[0069] 4) Calculate the number of particles moving to its four neighbors in the cell N i ( x , y ) = Σ j = ...

Embodiment 3

[0089] Embodiment 3: edge detection of digital image

[0090] 1) Convert the image (such as Figure 7 Shown) Each pixel point is regarded as a cell; each cell and its four neighbors form a Moore neighborhood (such as figure 1 .a); The state set of each cell is defined as the number of particles that may be contained in the cell; the number of particles contained in each cell is greater than 0 and less than 256.

[0091] 2) Initialization, defining that the initial state of each cell is equal to the gray value of the corresponding pixel in the image.

[0092] 3) At time t, generate a random sequence u x，y (i)={1, 2, 3, 4, ...}, where u x，y (i) The probability of equaling 1, 2, 3, and 4 is 1 / 4.

[0093] 4) Calculate the number of particles moving to its four neighbors in the cell N i ( x , y ) = Σ j = ...

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Abstract

The invention relates to an image edge detecting algorism based on cell automata. It calculates the second derivative of image by simulating the physics diffusion model, and realizes the exact solution for the second derivative of image, and restrains pseudo-zero point by lengthen the iteration time, the operation steps are: (1) simulating the physics diffusion equation; (2) calculating the second derivative of image; (3) searching the pseudo-zero point and determining the edge. The invention overcomes the disadvantage that the second derivative is sensitive for noise, and improves greatly the anti-noise capability, improves the accuracy of solving the second derivative, and improves the accuracy of detecting edge.

Description

technical field [0001] The invention relates to an image edge detection algorithm, in particular to an image edge detection algorithm based on cellular automata. Background technique [0002] Edge detection is the most frequently used image analysis method in the field of image analysis. It greatly reduces the amount of image data while retaining the important information of the analysis target in the image - the edge. The edge is the boundary between the analysis object and the background, which means that if the edge in an image can be accurately located, then the analysis object in the image can be identified, and various aspects of the object can be measured. Attributes such as: perimeter, area, shape, etc. In computer vision and many other image processing fields, this kind of identification and classification of analysis objects in images is widely used, so edge detection has become a very necessary tool in the field of image analysis. [0003] In an image, an edge r...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/60
Inventor 严壮志陈玉刘书朋
Owner SHANGHAI UNIV
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