A stereo image matching method based on a convolutional neural network

A convolutional neural network and stereoscopic image technology, applied in the field of stereoscopic image matching based on convolutional neural network, to achieve good stability, good robustness, and high complexity

Inactive Publication Date: 2019-04-05
BEIHANG UNIV
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Problems solved by technology

The main consideration in stereo vision is the semi-occlusion phenomenon. Since there is no corresponding matching point fo

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  • A stereo image matching method based on a convolutional neural network
  • A stereo image matching method based on a convolutional neural network
  • A stereo image matching method based on a convolutional neural network

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[0068] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0069] An embodiment of the present invention provides a stereoscopic image matching method based on a convolutional neural network, referring to figure 1 shown, including:

[0070] S1. Obtain a plurality of stereo matching image pairs and the corresponding real parallax, and use them as a data set;

[0071] S2. Construct a convolutional neural network, and select a linear correction unit RELU function for activation;

[0072] S3...

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Abstract

The invention relates to a three-dimensional image matching method based on a convolutional neural network, which comprises the following steps: acquiring a plurality of three-dimensional matching image pairs and corresponding real disparity, and taking the three-dimensional matching image pairs and the corresponding real disparity as a data set; Constructing a convolutional neural network, and selecting a linear correction unit (RELU) function for activation; Training the convolutional neural network by adopting a back propagation algorithm, and determining a network error function and a learning rate; Through the calculation of the convolutional neural network, the network outputs a matching cost space diagram of the left and right image blocks; And performing matching cost aggregation,disparity selection and disparity refinement on the cost space graph, and selecting a pixel point with the minimum cost as a matching point to obtain a final disparity map. The image can be directly used as network input, the overall disparity map obtained through the convolutional neural network matching algorithm is relatively smooth, a relatively good matching effect can be achieved in a non-texture region and a depth value abrupt change region, and relatively good robustness is still achieved for an image pair with illumination change and incomplete correction.

Description

technical field [0001] The present invention relates to the technical field of three-dimensional video, in particular to a stereoscopic image matching method based on a convolutional neural network. Background technique [0002] Stereo matching techniques are now widely used in problems such as robot navigation, depth-of-field rendering, and video processing. The binocular stereo matching in the stereo matching technology generally acquires two images of the same scene from different angles, and calculates the parallax information, and then obtains the three-dimensional depth information of the object according to the parallax through the principle of triangulation. [0003] For the human eye, it is very easy to restore the three-dimensional information from the scene, but for the computer, it is extremely difficult to find the matching corresponding point of the same scene point in the binocular image pair, and the stereo vision technology is still very difficult. It is no...

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Application Information

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IPC IPC(8): G06T7/593
CPCG06T2207/10021G06T2207/20081G06T2207/20084G06T7/596
Inventor 祝世平徐豪闫利那
Owner BEIHANG UNIV
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