A Classified Image Edge Detection Method Based on Visual Path Orientation Sensitivity

An image edge and detection method technology, which is applied in the field of visual neural computing, can solve the problems of ignoring the effect of grading processing and reducing the contrast of image edges, and achieve the effects of protecting weak details, removing false edges and texture noise, and accurate positioning

Active Publication Date: 2017-12-26
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, in practical applications, due to the influence of unfavorable factors such as lighting and shadows, the contrast of image edges is reduced, and it is difficult for traditional detection methods to meet the above two requirements at the same time; and the current edge detection method based on the visual neural mechanism simplifies the process of real neurons. The electrophysiological characteristics in signal processing ignore the hierarchical processing effect of different hierarchical structures on the visual pathway in contour perception, and essentially use a black-box mathematical model to simulate the visual mechanism

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  • A Classified Image Edge Detection Method Based on Visual Path Orientation Sensitivity
  • A Classified Image Edge Detection Method Based on Visual Path Orientation Sensitivity
  • A Classified Image Edge Detection Method Based on Visual Path Orientation Sensitivity

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Embodiment Construction

[0017] combine figure 1 , the specific implementation steps of the present invention are:

[0018] Step (1) According to the size of the original image IO(x, y) (x=1,2,...M; y=1,2...N, the variables x and y are the same below, M and N respectively represent the width and height of the image) , to construct an orientation-sensitive first-level neuron network GC(x,y) of the same size, in which a single neuron adopts the LIF model shown in formula (1):

[0019]

[0020] where v is the neuronal membrane voltage, c m is the film capacitance, g l is the leakage conductance, RF represents the receptive field range of the neuron, w x,y is the synaptic connection weight. I x,y is the excitation current, corresponding to the normalized gray value of the image IO(x,y) (x=1,2,...M; y=1,2...N) at the position (x,y). v th is the pulse emission threshold, v reset is the static potential. when v is greater than v th When , the neuron will generate a spike, and at the same time v ...

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Abstract

The invention discloses an image edge classification detection method based on visual path orientation sensitivity. The present invention utilizes the role of neuron synaptic connections in the centripetal distribution of the optimal orientation of the receptive field, constructs a first-level neuron network sensitive to multiple specific directions, uses image pixels as network input, and records neurons in a certain time window The pulse firing sequence within is calculated, and the discharge frequency is calculated as the network output; the network output in multiple directions is fused and mapped to the gray level to form an edge-sensitive image; for the edge-sensitive image, the lateral inhibition range and inhibition amount in the receptive field are determined , form the second-level neuron network, and output the image after lateral suppression; finally, after threshold processing, the edge detection result is obtained. The invention considers important visual mechanisms such as directional receptive fields and lateral inhibition, simulates the hierarchical processing effect of different hierarchical structures on the visual path in contour perception, and can effectively improve the edge detection performance of low-contrast images.

Description

technical field [0001] The invention belongs to the field of visual neural computing, and relates to an image edge classification detection method based on visual path orientation sensitivity. Background technique [0002] Contour feature extraction will provide important dimensionality reduction information for image understanding or moving target behavior analysis. The extraction process usually needs to meet: (1) under the premise of accurately locating edges, no missed detection occurs; (2) avoid false edges. However, in practical applications, due to the influence of unfavorable factors such as lighting and shadows, the contrast of image edges is reduced, and it is difficult for traditional detection methods to meet the above two requirements at the same time; and the current edge detection method based on the visual neural mechanism simplifies the process of real neurons. The electrophysiological characteristics in signal processing ignore the hierarchical processing e...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/13
Inventor 范影乐王典郭斌李晓春
Owner HANGZHOU DIANZI UNIV
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