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Image edge detection method based on self-adaptive neural fuzzy inference systems

A neuro-fuzzy, reasoning system technology, applied in the field of image processing, can solve difficult problems

Inactive Publication Date: 2013-09-25
JIANGNAN UNIV
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Problems solved by technology

These classic algorithms can effectively extract the edges in the image to a certain extent, but the values ​​of some parameters need to be determined in the algorithm, and the determination of the optimal values ​​of these parameters is a relatively difficult problem

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  • Image edge detection method based on self-adaptive neural fuzzy inference systems
  • Image edge detection method based on self-adaptive neural fuzzy inference systems
  • Image edge detection method based on self-adaptive neural fuzzy inference systems

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Embodiment Construction

[0032] Combine below Figure 1 to Figure 7 The present invention is further described in detail.

[0033] Step 1: Construct a network consisting of four adaptive neuro-fuzzy inference systems and a post-processing block. Before using the network to perform edge detection on noisy images, a training image is artificially constructed, and the four adaptive neuro-fuzzy inference systems are trained using a hybrid learning algorithm. The fuzzy reasoning system is trained separately to determine the parameters in the system;

[0034] Specific steps are as follows:

[0035] Step A: Each adaptive neuro-fuzzy inference system has four inputs and one output, artificially construct an original image, and add 30% salt and pepper impulse noise to the image to obtain a noise image, which is used as the output of each adaptive neuro-fuzzy inference system The input training image of , from the original image, the edge mark image can be obtained as the training image of the expected output...

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Abstract

The invention relates to an image edge detection method based on self-adaptive neural fuzzy inference systems. With regard to an image polluted by spiced salt pulse noises, the method has the advantages that a network containing four self-adaptive neural fuzzy inference systems and a post-processing module is constructed; before the network is used for carrying out edge detection on a noise image, a training image is artificially constructed and a mixed learning algorithm is used for independently training the four self-adaptive neural fuzzy inference systems to determine parameters in the systems; after the four self-adaptive neural fuzzy inference systems are trained, one network can be formed by the four self-adaptive neural fuzzy inference systems and one post-processing module to carry out the edge detection on a testing image. The image edge detection method disclosed by the invention has the characteristics that even if the testing image is polluted by the noises, the method can effectively extract edge information in the image and does not need to carry out image filtering pre-processing process.

Description

technical field [0001] The invention relates to an image edge detection method based on an adaptive neuro-fuzzy reasoning system, which belongs to the technical field of image processing, in particular to a salt and pepper pulse noise image edge detection method. Background technique [0002] Edge detection is the basis of many image processing operations such as image segmentation, object recognition, image registration, image classification, etc., and its detection quality largely determines the effect of these subsequent operations. [0003] Edge detection algorithms solve image segmentation problems by detecting edges that contain distinct regions. Edges are composed of edge pixels, and edge pixels are those pixels with sudden changes in grayscale in the image. Edge detection algorithms generally use the maximum value of the first derivative of the image or the zero-crossing information of the second derivative to provide the basic basis for judging the edge point. Rob...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T1/40G06T7/13
Inventor 罗海驰李岳阳费赓柢孙俊
Owner JIANGNAN UNIV
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