Method for extracting local salient objects in depth image based on multi-way tree

Abstract

The invention relates to a method for extracting local salient objects in a depth image based on multi-way tree. The method includes the following steps that: pixel-by-pixel neighborhood difference is performed on an inputted depth image, and a multi-way tree data structure-based depth tree model and a mapping graph of depth tree nodes and image pixels are established; local optimization is performed on leaf nodes of a depth tree, so that noises can be removed; the leaf nodes of the depth tree are traversed, so that a local extremum area of the depth image can be obtained, and the positions of subtree root nodes of the leaf nodes are determined through utilizing a decision function, and therefore, local salient objects areas can be extracted out. With the method adopted, a plurality of salient objects areas can be extracted out quickly and accurately, and the accuracy of the detection of the salient objects in the depth image can be improved.

Description

technical field [0001] The invention relates to an object detection technology in the technical field of computer vision, in particular to a method for extracting local salient objects based on a multi-fork tree in a depth image. Background technique [0002] With the popularity of depth sensors (such as Microsoft's Kinect, etc.) and the development of binocular stereo vision (such as Intel's RealSense3D camera, etc.), stereo vision has gradually become a popular field in the last decade. Compared with traditional two-dimensional plane images, depth images Added 3D depth information. The potential application prospects of depth information are huge, and one of the important application scenarios is object detection. [0003] Object detection is an important problem in the field of computer vision, and it is also a difficult problem. Obtaining as few object regions as possible and not related to the object category is the top priority of object detection. Traditional method...

AI Technical Summary

Problems solved by technology

Traditional methods are mostly based on rectangular frames, that is, a series of rectangular frames are used to frame objects in the image as tightly as possible (such as Pascal Visual Object Classes Challenge, etc.). This type of method is widely used in the field of traditional two-dimensional images. Taking into account the speed while achieving better detection accuracy, but this method also has problems such as generating more candidate boxes, low compactness of the candidate boxes, and a large amount of useless information in the boxes.

Another method is based on region segmentation, which is to segment out object regions with specific practical significance in the image, that is, semantic objects. The accuracy of this type of method is generally high, but the algorithm complexity is high and the speed is generally slow

Traditional object detection mostly uses grayscale information, but rarely involves depth information. In view of the application requirements in reality, there is an urgent need for a local protruding object extraction method in depth images that takes into account both speed and accuracy.

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