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Image matching method based on lateral inhibition and chaos quantum particle swarm optimization

A technology of quantum particle swarm and matching method, applied in the field of computer vision information processing, can solve complex intelligent optimization and other problems

Inactive Publication Date: 2010-09-15
BEIHANG UNIV
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Problems solved by technology

[0028] The present invention proposes an image matching method based on lateral suppression and chaotic quantum particle swarm optimization (LICQPSO), the purpose of which is to improve the efficiency and accuracy of image matching, and can also solve other complex intelligent optimization problems

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  • Image matching method based on lateral inhibition and chaos quantum particle swarm optimization
  • Image matching method based on lateral inhibition and chaos quantum particle swarm optimization
  • Image matching method based on lateral inhibition and chaos quantum particle swarm optimization

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Embodiment Construction

[0095] Such as figure 1 and figure 2 As shown, an image matching method based on side suppression and chaotic quantum particle swarm optimization, the specific implementation steps are as follows:

[0096] Step 1: The principle of side suppression preprocesses the image and extracts the edge of the image.

[0097] 1.1 Read the image and convert it to a grayscale image;

[0098] According to the conversion matrix between images in different formats, the images in different formats are first converted into grayscale images, and the grayscale value of each pixel in the grayscale image is obtained, which is convenient for the next step to extract the image edge.

[0099] 1.2 Extract the original image and the image edge of the template to be matched;

[0100] Select the appropriate side suppression model, suppression field and side suppression coefficient distribution, and extract the image edge from the matching template and the original image.

[0101] The lateral inhibitio...

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Abstract

The invention relates to an image matching method based on lateral inhibition and chaos quantum particle swarm optimization, comprising the following steps of: (1) pre-processing images by using lateral inhibition principles, and extracting image edges; (2) initiating each parameter of particle swarm optimization; (3) calculating the fitness value of each particle; (4) comparing the fitness valueof each particle of the cycle, i.e. the kth cycle, and the fitness value of each particle obtained from the former step, wherein the maximal fitness value is the global optimization gbest; simultaneously comparing the fitness value of each particle obtained from the former step respectively with the fitness value of the particle obtained in the (k-1)th cycle, wherein the greater fitness value is the optimization Pbest of the particle self of the kth cycle; (5) carrying out particle swarm optimization according to the fitness values of the particles; (6) skipping to the step 3 when the currentcycle time plus 1, repeating the operation till the cycle time is greater than the maximum cycle time N; and (7) ending the operation and outputting the optimized allocation position and the optimized fitness value.

Description

【Technical field】 [0001] The invention is an image matching method based on lateral inhibition and chaotic quantum particle swarm optimization (Chaotic mutated Quantum-BehavedPSO Based on Lateral Inhibition, LICQPSO), which belongs to the field of computer vision information processing. 【Background technique】 [0002] Image matching is a very important research topic in computer vision, widely used in image registration, object detection, object recognition and image retrieval and other fields. Image matching refers to identifying points with the same name between two or more images through a certain matching method. Its essence is to search for the most similar region according to a certain matching criterion under the condition of primitive similarity. A commonly used method for image matching is based on grayscale matching. A good match requires high precision and high speed, which are also crucial measurement factors in the field of computer vision. Research shows that...

Claims

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Application Information

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IPC IPC(8): G06K9/64G06T7/00
Inventor 段海滨刘芳吴江徐春芳
Owner BEIHANG UNIV
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