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RGBD image salience detection method based on hypergraph model

A technology of hypergraph model and detection method, which is applied in the field of image processing and can solve the problem that the background area cannot be detected.

Active Publication Date: 2016-08-24
ZHEJIANG UNIV
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Problems solved by technology

However, this method will misjudgment when the background area is segmented by the foreground object, and the isolated background area cannot be detected.

Method used

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  • RGBD image salience detection method based on hypergraph model
  • RGBD image salience detection method based on hypergraph model
  • RGBD image salience detection method based on hypergraph model

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Embodiment Construction

[0057] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0058] The RGBD image saliency detection method based on the hypergraph model of the present invention comprises the following steps:

[0059] (1) Input the color image RGB to be detected and its corresponding depth information D; the color image to be detected is composed of three color channels of red, blue and green; the depth information D is the actual depth corresponding to each pixel of the color image;

[0060] (2) Perform color space conversion on the color image input in step 1, transform from RGB color space to CIELab color space, use SLIC algorithm to realize superpixel segmentation of the image, and divide the image into a collection of multiple regions where r i Indicates the i-th region obtained by segmentation, n represents the total number of regions, Represents a collection of integers;

[0061] (3) For each region r of the image ...

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Abstract

The invention discloses an RGBD image salience detection method based on a hypergraph model. The method includes conducting hyperpixel segmentation for a color image to be detected and a depth image, calculating neighborhood depth contrast graphs for each hyperpixel area of the depth image, constructing a depth background hyperedge according to neighborhood depth contrast ratio, extracting hyper pixel areas on the boundary of the image to construct a boundary background hyperedge, calculating the weight of the two hyperedges, expanding hyperedges according to a hypergraph learning algorithm, building an induce graph, prior calculating boundary background salient map by using the boundary connectivity based on the spatial adjacent relation of the induce graph and the edge weight, and obtaining a final salient detection map based on salience degree updating algorithm of cellular automaton and a fusion algorithm in combination with depth prior. Deficiency of conventional 2D boundary background prior is overcome. The improvement is made based on depth information and a hypergraph model, and better effects are achieved than a conventional image salience detection method that combines color and depth information.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a hypergraph model-based RGBD image saliency detection method. Background technique [0002] Image saliency detection is one of the hot topics in the field of computer vision and pattern recognition. The study found that the human visual mechanism can always quickly extract the important and interesting regions in the image, and then analyze and process these regions, but basically does not process the remaining insignificant regions in the image. This principle provides a lot of inspiration for researchers in the field of computer vision, that is, it can detect the salient areas in the image, extract the salient objects in the image for subsequent processing, save the time for processing the whole image, and greatly improve the efficiency of image processing . Therefore, image saliency detection can be widely used in image segmentation, object recognition ...

Claims

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Application Information

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IPC IPC(8): G06T7/00
Inventor 任健强龚小谨
Owner ZHEJIANG UNIV
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