Fast image matching algorithm based on projection characteristics

Zhou, Lijuan; Xiao-bo, Yue; Zhou, Linna

doi:10.1117/12.896533

Cited by 3 publications

(4 citation statements)

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“…The above traditional image matching methods based on SIFT [11][12] , SURF [13][14][15] , BRIEF [16][17] , and ORB [18][19][20] all rely on manually designed descriptors. Although they have certain effects, considering the on-site environment of the intelligent sorting robot for coal gangue, there are still some shortcomings.…”

Section: Related Workmentioning

confidence: 99%

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

Ye,

Hongwei,

Peng

et al. 2024

Preprint

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The coal gangue sorting robot may encounter variations in the pose of the target coal gangue due to belt slippage, deviation, and speed fluctuations, leading to failed or missed grasping attempts during the sorting process of coal gangue. In response to this issue, we propose a special two-stage network coal gangue image fast matching method to re-obtain the target coal gangue pose information, further improving the grasping accuracy and efficiency of the robot. In the first stage, we use SuperPoint to enhance the scene adaptability and credibility of feature point extraction. The improved Multi-scale Retinex with Color Restoration enhancement algorithm is used to further enhance the ability of Superpoint to detect feature points. In the second stage, we introduce SuperGlue for feature matching to improve the robustness of the matching network. Integrating the Progressive Sample Consensus algorithm to further eliminate erroneous feature matching point pairs and improve the accuracy of image matching. We conducted matching experiments of coal gangue under different object distances, scales, and rotation angles using various methods on the double-manipulator truss-type coal gangue sorting robot experimental platform independently developed by our team. The results showed that the matching rate of the proposed method was 98.2%, with a matching time of 84.6ms. It has the characteristics of a high matching rate, good real-time performance, and strong robustness, and can meet the requirements of efficient and accurate matching between coal gangue recognition images and sorting images.

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Section: Related Workmentioning

confidence: 99%

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

Ye,

Hongwei,

Peng

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…6) Registration points mapping downward. The registration relationship between each layer can reflect to each layer of the pyramid through downward mapping calculation [7]. Registration points are mapped to the next layer, which will lead to small changes in the scope of the registration relationship because of the resolution improvement.…”

Section: Registration Steps For Ccd Imagementioning

confidence: 99%

“…Image registration algorithm is usually divided into two categories: one is gray correlation algorithm [2]; the other is feature-based registration algorithm [3]. Gray-based match the image data directly, which include normalized cross-correlation registration, template registration, the fast Fourier algorithm, projection registration, sequential similarity detection registration and so on [4][5][6][7][8]. The other called feature registration includes point, line, area and other significant features primitives [9].…”

Section: Introductionmentioning

confidence: 99%

A Image Registration Algorithm Based on Wavelet Pyramid for CCD Image

Lü

Kang

Zhai

2014

AMM

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Image registration is the key process in analyzing images and data from satellites. Feature-based methods find correspondence pixels which point to the same place between two images. In this paper, a wavelet pyramid hierarchical image registration algorithm is presented. First mismatching exclusion policy on the top of pyramid is used. Other hand search strategy which gets the scope of the search layer on the bottom of the pyramid is adopted. Both of them rely on pair of matching-right points. Experimental results show that the algorithm can significantly improve the search efficiency, and obtain a good match accuracy and reliability.

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“…The infrared LED markers are fixed on the four corners of each frame, which is easy to be detected. The relationship of the registration coordinate transformation [4] is:…”

Section: Tracking Of Human Position By Computer Visionmentioning

confidence: 99%

A 3D Perspective Display Cube for Multiview Tracking

Yang

Liu

2011

AMR

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This paper describes an eye naked 3D display cube, which can track a viewer’s position and render the stereo perspective scene in each display surface. The viewers can look the details of 3D models and scenes from any viewpoints. It seems as the real 3D objects lay in the display cube. It supports multi-viewer interactive in one scene. The viewers also can see the position of each other in own screen. They can co-operate in the same scene, although their field of view is independent of each other. The technologies of 3D perspective rendering, marker tracking and image processing are used for the 3D display cube.

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Fast image matching algorithm based on projection characteristics

Cited by 3 publications

References 0 publications

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

A Image Registration Algorithm Based on Wavelet Pyramid for CCD Image

A 3D Perspective Display Cube for Multiview Tracking

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