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A Depth Image Segmentation Method Based on Mean Shift Algorithm and Mathematical Morphology

A mean shift algorithm and mathematical morphology technology, applied in the field of depth map segmentation, can solve problems such as background segmentation with high degree of automation, and achieve the effect of high degree of automation

Active Publication Date: 2016-02-17
哈尔滨工业大学高新技术开发总公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that the existing segmentation method cannot segment the background with a high degree of automation and segment the background into a whole, the present invention provides a depth map segmentation method based on the mean shift algorithm and mathematical morphology

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  • A Depth Image Segmentation Method Based on Mean Shift Algorithm and Mathematical Morphology
  • A Depth Image Segmentation Method Based on Mean Shift Algorithm and Mathematical Morphology
  • A Depth Image Segmentation Method Based on Mean Shift Algorithm and Mathematical Morphology

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specific Embodiment approach 1

[0027] Specific implementation mode 1. Combination Figure 4 to Figure 9 This specific embodiment will be described. This specific implementation mode is:

[0028] Step 1: Generate a grayscale histogram hist(k) of the depth map according to the depth image whose grayscale is f(x, y); said L is grayscale, L=0, 1...255;

[0029] The abscissa of the gray histogram is the gray level L, and the ordinate is the frequency of occurrence of the gray level;

[0030] Step 2: Smooth the gray histogram by using a one-dimensional Gaussian window function, and set the foreground threshold T;

[0031] The foreground threshold T is the first minimum value that includes a certain semantic range according to the gray value from high to low, and the certain semantic range is that the number of pixels contained in this area is greater than 10% of the number of pixels in the entire image, That is, hist(k>T)>10%*hist(0...L-1); the minimum value is hist(k)=min(hist(k-4), hist(k-3)...hist(k +4)); ...

specific Embodiment approach 2

[0048] Specific embodiment 2. The difference between this specific embodiment and specific embodiment 2 is that the process of generating the gray histogram hist(k) of the depth map according to the depth image whose gray level is f(x, y) in step 1 is:

[0049] Step 1A: Initialize hist(k)=0; k=0,1...L-1;

[0050] Step 1B: Statistics f(x, y)=k; hist(k+1), x=0, 1...M-1; y=0, 1...N-1.

specific Embodiment approach 3

[0051] Specific embodiment three, the difference between this specific embodiment and specific embodiment three is that the grayscale histogram is smoothed using the one-dimensional Gaussian modulus described in step 2, and the Gaussian modulus used is [0.10.20.40.20.1]; after smoothing The result is hist(k)=0.1hist(k-2)+0.2hist(k-1)+0.4hist(k)+0.2hist(k+1)+0.1hist(k+2); k=0, 1...L-1.

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Abstract

The invention discloses a depth image segmentation method based on a mean shift algorithm and mathematical morphology, and relates to the field of depth image segmentation. The depth image segmentation method based on the mean shift algorithm and the mathematical morphology solves the problem that an existing segmentation method is incapable of enabling an uneven background across a whole image grey level range to be segmented into a whole in a high automation degree mode. The method comprises the steps of generating a grey level histogram of a depth image according to the depth image, smoothly processing the grey level histogram by using a one-dimensional Gaussian window function, adjusting a grey level of the depth image in the step one according to a foreground threshold value T, enabling a grey level of a pixel to be improved by 10, wherein the grey level is higher than the foreground threshold value T, modifying the obtained depth image through an off operation of the mathematical morphology, clustering and segmenting the obtained depth image through the mean shift algorithm, assigning grey levels on segment areas, wherein the assigned grey level of each area is a mean value of the grey levels inside an original depth image, and achieving segmentation of the depth image. The depth image segmentation method based on the mean shift algorithm and the mathematical morphology can be widely applied to work for segmentation of foregrounds and backgrounds of depth images.

Description

technical field [0001] The present invention relates to the field of depth map segmentation. Background technique [0002] A depth map is a grayscale image of the same size as a 2D image, such as figure 1 and figure 2 As shown, the gray value of each pixel reflects the depth value of the pixel at the same position in the two-dimensional image, that is, the distance between the real object and the observer represented by the pixel. The higher the gray value, the closer the distance, and vice versa. Far. Depth z can be obtained by the following equation: [0003] z ( r , c ) = 1.0 ( P ( r , c ) / 255.0 ) × ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00
Inventor 关宇东提纯利滕艺丹戴翊轩李尔佳杜克仲小挺于博良
Owner 哈尔滨工业大学高新技术开发总公司