Variable-scale image segmentation method based on RGB-D

A RGB-D, variable scale technology, applied in image analysis, image enhancement, image data processing and other directions, can solve problems such as increasing the amount of calculation, and achieve the effect of multi-scale analysis, strong robustness, and strong interpretability

Inactive Publication Date: 2019-12-27
NANJING UNIV OF SCI & TECH
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Problems solved by technology

The currently commonly used supervoxel segmentation method has a high consistency in the size of each supervoxel obtained after the scale parameter is determined. If multi-scale analysis is required in the future, the size of the supervoxel can be controlled by setting different scale factors. When analyzing each scale, it is necessary to calculate the super-voxel segmentation result of a scale, which increases the amount of calculation

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  • Variable-scale image segmentation method based on RGB-D
  • Variable-scale image segmentation method based on RGB-D
  • Variable-scale image segmentation method based on RGB-D

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Embodiment

[0073] figure 1 It is the result map obtained after the processing of one frame of RGB-D image obtained by shooting one frame of mynt_eye in the present invention. The following method is used to segment the picture, including the following steps.

[0074] Step 1. Acquisition of the depth image gradient map. Set a frame of RGB-D image data as P, with a resolution of 480 rows and 640 columns. Each data point includes 3 color channels (R, G, B) and a depth channel ( d), use formula (1) to solve the gradient of the depth image, and obtain the gradient map D of the depth image.

[0075]

[0076] Step 2:

[0077] For the selection of seed points, randomly select a point p0 in D as the seed point, set the radius R as the minimum threshold distance of the seed point to be generated, and use Poisson castellation sampling algorithm to sample in D to obtain the set of seed points;

[0078] Step 2-1. Randomly select a seed point seed in the gradient map of the depth image D obtained...

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Abstract

The invention provides a variable-scale image segmentation method based on RGB-D. The method comprises: performing Poisson sampling by using a gradient map corresponding to a depth map to select seedpoints, obtaining initial super-pixel points based on gradient change, then establishing a map model according to the adjacency relation of super-pixels, performing super-pixel fusion by using a map method, and finally obtaining a segmented final result. According to the method, the gradient information of the depth map is fully utilized, and the method is more convenient and quicker.

Description

technical field [0001] The invention belongs to image segmentation technology, in particular to a variable-scale image segmentation method based on RGB-D. Background technique [0002] With the advancement of sensor technology, the acquisition cost of RGB-D images is getting lower and lower. How to preprocess RGB-D images more effectively is an important research content of computer vision in recent years. In order to make full use of the three-dimensional geometric information in RGB-D images, similar to the concept of super-pixel over-segmentation in two-dimensional images, over-segmenting RGB-D images into super-voxels is an effective preprocessing method, which can effectively Reduce the amount of data processed by subsequent algorithms. The currently commonly used supervoxel segmentation method has a high consistency in the size of each supervoxel obtained after the scale parameter is determined. If multi-scale analysis is required in the future, the size of the superv...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T7/187
CPCG06T7/11G06T7/187G06T2207/10028
Inventor 钟易潘丁永良宋迪袁夏
Owner NANJING UNIV OF SCI & TECH
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