Improved Harris corner detector algorithm for image co-registration

Safy, Mohammed; Shi, Guangming; Li, Zhenfeng; Amein, Ahmed S.

doi:10.1109/iccsnt.2013.6967055

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Viewpoint stabilization is a field of image registration process. There are lots of research on image registration using Scale-Invariant Feature Transform (SIFT), Random Sample Consensus (RANSAC) [1,2], Speeded Up Robust Features (SURF) [3], Harris Corner Detection, Optical Flow (Lucas-Kanade method) algorithm using motion vector. But all the existing process are not very robust from practical points.…”

Section: Introductionmentioning

confidence: 99%

Efficient 3D stereo vision stabilization for multi-camera viewpoints

Bhuiyan¹,

Khalifa²

2019

Bulletin EEI

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In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method. Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.

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Section: Introductionmentioning

confidence: 99%

Efficient 3D stereo vision stabilization for multi-camera viewpoints

Bhuiyan¹,

Khalifa²

2019

Bulletin EEI

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A comprehensive review on remote sensing image registration

Paul

Pati

2021

International Journal of Remote Sensing

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Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity

Chen

Peng

Huang

et al. 2013

JSIP

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This paper presents a corner-based image alignment algorithm based on the procedures of corner-based template matching and geometric parameter estimation. This algorithm consists of two stages: 1) training phase, and 2) matching phase. In the training phase, a corner detection algorithm is used to extract the corners. These corners are then used to build the pyramid images. In the matching phase, the corners are obtained using the same corner detection algorithm. The similarity measure is then determined by the differences of gradient vector between the corners obtained in the template image and the inspection image, respectively. A parabolic function is further applied to evaluate the geometric relationship between the template and the inspection images. Results show that the corner-based template matching outperforms the original edge-based template matching in efficiency, and both of them are robust against non-liner light changes. The accuracy and precision of the corner-based image alignment are competitive to that of edge-based image alignment under the same environment. In practice, the proposed algorithm demonstrates its precision, efficiency and robustness in image alignment for real world applications.

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Improved Harris corner detector algorithm for image co-registration

Cited by 3 publications

References 7 publications

Efficient 3D stereo vision stabilization for multi-camera viewpoints

Efficient 3D stereo vision stabilization for multi-camera viewpoints

A comprehensive review on remote sensing image registration

Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity

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