Image segmentation method fusing color and depth information

Abstract

The invention discloses an image segmentation method fusing color and depth information. According to the method, firstly, a meanshift algorithm is used for segmenting an input color image to obtain an over-segmentation region set, and then similarities among all the regions are calculated and include color similarities, depth similarities and fusions of the color similarities and the depth similarities; then according to a depth image, seed regions of a target and seed regions of a background are automatically selected; finally, an MSRM algorithm is used for merging the regions, so that a final segmentation result is obtained. In the process of calculating the similarities among the regions, the color information is used, besides, the depth information is dynamically fused, and the problem that when the target color and the background color are similar, and namely a long-scale contrast edge exists between objects, correct segmentation can not be achieved is solved. The seed regions are automatically selected by the utilization of the depth information of the image, the seed regions of the target and the seed regions of the background do not need to be marked manually and alternately, region characteristics of the depth image are directly used for determining the seed regions instead of edge characteristics, and therefore high robustness is achieved.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to an image segmentation method for fusing color and depth information. Background technique [0002] Image segmentation is the technology and process of dividing an image into several specific regions with unique properties and proposing objects of interest. It is a key step from image processing to image analysis. The objects extracted after image segmentation can be used in image semantic recognition, image retrieval and other fields. Traditional image segmentation methods are generally based on image appearance features, such as color, brightness, texture, shape, structure, etc. Real objects exist in the three-dimensional world, and objects should be defined by physical connectivity. Combining image depth information to segment images can effectively overcome the problems of over-segmentation and under-segmentation, and obtain image segmentation results with certain ...

AI Technical Summary

Problems solved by technology

Limitations are: need markers to cover major feature regions, segmentation fails when shadows, low-contrast edges, and blurred regions appear

The limitations of this algorithm are: (1) when using the graph cut algorithm (graph cut) for global optimization, only color information is used; (2) the obtained image binarization depth discontinuous edge strongly depends on the experimental threshold, and the edge The line itself is intermittent and discontinuous, which will affect the reliability of seed point selection

This method requires training and is computationally intensive

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