In this paper, aiming at the problem of 3D real - time Monitoring for the coal inspection robot in the environment of coal underground. An improved sift matching algorithm to improve the correctness of the feature mat...
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ISBN:
(纸本)9781538619377
In this paper, aiming at the problem of 3D real - time Monitoring for the coal inspection robot in the environment of coal underground. An improved sift matching algorithm to improve the correctness of the feature matching and the rate of the feature matching is put forward, which combines the simplified siftmatching operator, the geometric constraint condition and the RANSAC algorithm. Firstly the simplified siftmatching operator is used to match the feature, then the wrong matching pair is removed by combining the geometric constraint condition and the RANSAC algorithm. The experimental simulation results show that the improved sift matching algorithm has a good effect on the 3D real - time Monitoring for the coal underground inspection robot.
Stereo vision matching is to search the corresponding relation of one spatial object observed from different angles in the projected image, and obtain the parallax image depending on the deviation (parallax is the geo...
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ISBN:
(数字)9781510627130
ISBN:
(纸本)9781510627130
Stereo vision matching is to search the corresponding relation of one spatial object observed from different angles in the projected image, and obtain the parallax image depending on the deviation (parallax is the geometric distance between different points projected from the same spatial point on different image. In a parallel binocular stereo vision system, two cameras have the same focal length and parallel imaging planes, so there is no rotation and scale conversion for images obtained by two cameras. sift binocular stereo vision system does not need multi-scale conversion and coordinate axis rotation, therefore the algorithm is simplified and the fault tolerance for sift target matching is maintained.
A depth-map estimation method, which converts two-dimensional images into three-dimensional (3-D) images for multi-view autostereoscopic 3-D displays, is presented. The proposed method utilizes the Scale Invariant Fea...
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A depth-map estimation method, which converts two-dimensional images into three-dimensional (3-D) images for multi-view autostereoscopic 3-D displays, is presented. The proposed method utilizes the Scale Invariant Feature Transform (sift) matchingalgorithm to create the sparse depth map. The image boundaries are labeled by using the Sobel operator. A dense depth map is obtained by using the Zero-Mean Normalized Cross-Correlation (ZNCC) propagation matching method, which is constrained by the labeled boundaries. Finally, by using depth rendering, the parallax images are generated and synthesized into a stereoscopic image for multi-view autostereoscopic 3-D displays. Experimental results show that this scheme achieves good performances on both parallax image generation and multi-view autostereoscopic 3-D displays.
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