Dynamic video image segmentation method based on dual-channel convolution kernel and multi-frame feature fusion

A video image and feature fusion technology, which is applied in image analysis, image enhancement, image data processing, etc., can solve problems such as noise processing, poor image segmentation effect, and inability to obtain boundary images, etc., to reduce the accumulation of impurities and improve image quality. Segmentation effect, overcome the effect that the boundary is not closed and discontinuous

Pending Publication Date: 2021-02-02
HUNAN UNIV OF SCI & TECH
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Problems solved by technology

[0004] Although the existing image segmentation method based on edge detection suppresses the noise to a certain extent, it does not further process the noise. Get a completely closed boundary image

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  • Dynamic video image segmentation method based on dual-channel convolution kernel and multi-frame feature fusion
  • Dynamic video image segmentation method based on dual-channel convolution kernel and multi-frame feature fusion
  • Dynamic video image segmentation method based on dual-channel convolution kernel and multi-frame feature fusion

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Embodiment Construction

[0052] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0053] Such as figure 1 As shown, a dynamic video image segmentation method based on dual-channel convolution kernel and multi-frame feature fusion includes the following steps:

[0054] Step 1: Extract edge features: First, convert the original image into a grayscale image, and use the prewitt operator to extract edge features from the grayscale image by using edge pixel transformation to obtain an edge feature image. Its conversion result is as figure 2 shown.

[0055] Step 2: Perform edge feature screening on the edge feature image through dual-channel convolution kernels of different sizes, and multiply the two screened images to obtain the edge image.

[0056] After edge extraction, most of the places where the color changes drastically, including most of the edge images, also contain a lot of impurities, which need to be screened.

[0057] In...

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Abstract

The invention discloses a dynamic video image segmentation method based on dual-channel convolution kernel and multi-frame feature fusion, and the method comprises the following steps: 1, converting an original image into a gray level image, violently extracting edge features through edge pixel transformation, and obtaining an edge feature image; 2, performing edge feature screening on the edge feature images through the dual-channel convolution kernels of different sizes, and performing multiplication operation on the two screened images to obtain edge images; 3, constructing two types of multi-frame feature target extraction; 4, obtaining a segmented image through filling and restoring operation. According to the method, the image can be effectively segmented through a double convolutionkernel and multi-frame feature fusion method, a complete target image is obtained without much impurity interference, the defect that a conventional edge segmentation boundary is not closed or continuous is overcome, multi-frame feature target extraction is dynamically updated, impurity accumulation is reduced, and a good image segmentation effect is obtained.

Description

technical field [0001] The invention relates to an image segmentation method, in particular to a dynamic video image segmentation method based on dual-channel convolution kernel and multi-frame feature fusion. Background technique [0002] As an important part of image processing, image segmentation plays a leading role in the process of image processing, and it is also one of the difficult problems in image processing. Image segmentation technology has received great attention since the 20th century. Although researchers have proposed many methods for various problems, there is still no generally applicable theory and method so far. Therefore, it is more important to propose appropriate methods for specific problems. [0003] In the process of aluminum electrolysis, the fire eye image has large dust, low contrast and strong background interference. There are many segmentation methods for Huoyan image segmentation, such as the image segmentation method based on edge detect...

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Application Information

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IPC IPC(8): G06K9/34G06K9/62G06N3/04G06T5/00G06T7/12G06T7/90
CPCG06T7/90G06T7/12G06V10/267G06N3/045G06F18/253G06T5/70
Inventor 陈祖国唐至强刘洋龙陈超洋卢明吴亮红张胥卓
Owner HUNAN UNIV OF SCI & TECH
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