Abinocular stereoscopic vision matching method for generalizing belief propagation

Abstract

The invention relates to an abinocular stereoscopic vision matching method for generalizing belief propagation. The method comprises the following steps of: (1) acquiring a left image and a right image of two eyes to establish Markov random fields; (2) generating multiple scales of Markov random fields, wherein the kth layer is one fourth the size of the (k+1)th layer; (3) solving the Markov random fields in an order from 1 to n, decomposing the problem of searching a minimum value in a two-dimensional space into the problem of searching a minimum value in a plurality of one-dimensional spaces, reducing a state space by using a state space reduction strategy of the scale spaces and transferring a calculation result of the ith layer to the (i+1)th layer after iteration is finished; and (4) using the state with the minimum iteration value as the final state of a variable, i.e. a parallax value of points in an image, corresponding to the variable, after the Markov random field at the lowest layer is solved. The invention effectively lowers the complexity and reduces the amount of calculation.

Description

technical field [0001] The invention relates to the fields of image processing, computer vision, calculation method, mathematics and numerical method, especially the binocular stereo vision matching method of computer vision. Background technique [0002] At present, the research on stereo vision matching problem has made great progress. Especially the matching algorithm based on global optimization has become the main method to solve the matching problem and has been widely used. The reason it has received so much attention is because the matching problem can be well modeled as an optimization problem of Markov Random Field (MRF) or Conditional Random Field (CRF). This type of problem has been designed in many disciplines, and many of the resulting algorithms can be applied to the solution of matching problems. [0003] Among them, the algorithm based on belief propagation (Belief Propagation) is a method that has received extensive attention at present. Its main idea is...

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Problems solved by technology

[0006] In order to overcome the high complexity of the existing binocular image matching method for the generalized confidence propagation algorithm and the lack of a large amount of calculation, the present invention proposes a cache acceleration strategy based on the minimum sum, so that the generalized confidence propagation algorithm The performance of the method has been greatly enhanced, and it has been successfully applied to the solution of the matching problem in binocular vision, providing a binocular stereo vision matching method that effectively reduces the complexity and reduces the amount of calculations.

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