A binocular stereo matching method and system based on dense network depth learning

A binocular stereo matching and dense technology, which is applied in neural learning methods, biological neural network models, image data processing and other directions, can solve the problems of inability to accurately find weak texture areas, poor performance of pixel matching point detail features, etc. The effect of performance improvement and performance enhancement

Inactive Publication Date: 2019-03-29
NANCHANG HANGKONG UNIVERSITY
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Problems solved by technology

[0004] The purpose of the present invention is to provide a binocular stereo matching method and system based on dense network deep learning to solve the problem that the existing stereo matching network model cannot accurately find pixel matching points in weak texture areas and the performance of detail features is poor

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  • A binocular stereo matching method and system based on dense network depth learning
  • A binocular stereo matching method and system based on dense network depth learning
  • A binocular stereo matching method and system based on dense network depth learning

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Embodiment Construction

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0053] The purpose of the present invention is to provide a binocular stereo matching method and system based on dense network deep learning. By proposing a novel dense convolutional neural network model, the feature map is repeatedly used in the feature extraction process of the convolutional layer. A better matching cost is found to solve the problems that the existing stereo matching methods cannot accurately find pixel matching points in weak texture areas ...

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Abstract

The invention discloses a binocular stereo matching method and a binocular stereo matching system based on dense network depth learning. Firstly, stereoscopic image pairs with real values are preprocessed to construct training samples for network learning. Then a dense convolution neural network model is trained to calculate the matching cost. Finally, cross-aggregation algorithm is used to optimize the matching cost, and the optimal disparity is calculated by WTA strategy, and the corresponding disparity map is obtained. A method for extracting features of convolution layer features that a dense convolution neural network model is used to extract features of convolution lay, Attempt to find a better matching cost, effectively solve the problem that pixel matching point can not be found accurately in weak texture region and the performance of detail features is poor, with higher computational accuracy and better robustness.

Description

technical field [0001] The invention relates to the technical field of image sequence stereo matching, in particular to a binocular stereo matching method and system based on dense network deep learning. Background technique [0002] Stereo matching is the process of finding the same feature point from the left and right images and calculating its disparity. A classic stereo algorithm is summarized in four steps: matching cost calculation, cost aggregation, computing disparity, and disparity refinement. With the substantial improvement of computer software and hardware levels, stereo matching technology has gradually become a hot research direction in many fields such as computer vision, image processing and pattern recognition, and its research results are widely used in many fields such as robotics, military, aerospace, medical information Fields, such as unmanned automatic driving systems, robot vision systems, drone navigation and take-off and landing systems, industria...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/33G06N3/04G06N3/08
CPCG06N3/084G06T7/337G06T2207/20228G06T2207/10012G06N3/045
Inventor 陈震吴俊劼张聪炫黎明葛利跃江少锋陈昊
Owner NANCHANG HANGKONG UNIVERSITY
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