RGB-D image variable metric super voxel segmentation method

Abstract

The invention discloses an RGB-D image variable metric super voxel segmentation method. By adopting Poisson disk sampling, seed points are selected in data, and iteration clustering is carried out according to color distances and spatial distances between data points and the various seed points, and then an initial super voxel segmentation result is acquired. The super voxels acquired by the initial segmentation are used as vertexes, and the adjacency relations of the super voxels are used as edges to form an undirected graph, and the super voxel integration is carried out by adopting a method based on a graph theory. Super voxel segmentation results having different dimensions are acquired in the same RGB-D image, and the variable metric super voxel segmentation is realized. The RGB-D image variable metric super voxel segmentation method belongs to data pretreatment, and by using the acquired super voxels by segmentation, the large-scale super voxels are acquired in the area having high data consistency, and the small-scale super voxels are acquired in the area having the poor data consistency, and the method is capable of satisfying a human visual cognition characteristic in a better way.

Description

technical field [0001] The invention relates to a super-voxel segmentation method, in particular to a variable-scale super-voxel segmentation method suitable for RGB-D images. 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. [0003] At present, the commonly used supervoxel segmentation algorithm obtains a high consistency in the size of each supervoxel after the scale parameter is determined. If multi-scale analysis is required in the ...

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Problems solved by technology

[0003] At present, the commonly used supervoxel segmentation algorithm obtains a high consistency in the size of each supervoxel 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. In this way, 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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