An improved SIFT descriptor

Luan Zeng; You Zhai

doi:10.1117/12.903778

15 November 2011 An improved SIFT descriptor

Luan Zeng, You Zhai

Proceedings Volume 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation; 83210O (2011) https://doi.org/10.1117/12.903778
Event: Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 2011, Yunnan, China

Abstract

In order to improve the robustness and real time performance of SIFT based image registration algorithms, a new descriptor is proposed. We compute the new descriptor for a log-polar location grid with 3 bins in radial direction (the radius set to 3, 6 and 8) and 12 in angular direction, which results 36 location bins. Firstly, the 3×12 log-polar location grid is rotated to align its dominant orientation to a canonical direction in a different way with SIFT with less computational complexity. The keypoint dominant orientation and its orthogonal orientation is defined as the x and y directions of the descriptor’s local coordinate system. After that a 3×3 grid is rotated to align the dominant orientation ,and then the rotated 3×3 grid is translated to each pixel within the local 3×12 log-polar location grid. Therefore the local 3×12 log-polar location grid is rotated to align the dominant orientation and the rotation invariance is achieved. Secondly, compute the gradients in x and y directions using the rotated 3×3 grid for each pixel within the keypoint local neighborhood, so each pixel constitutes a gradient vector with 2 dimensions. Thirdly, use the distance from each pixel to the corresponding keypoint and a Gaussian function to assign a weight to the gradients, and then compute the gradients location histogram for the 36 location bins in the 3×12 log-polar grid. Finally, a descriptor with 72 dimensions is constituted and the descriptor is normalized to achieve illumination invariance. The experimental results show that the computational complexity of the new descriptor is reduced about 30% compared with the standard SIFT descriptor while the performance is favorably compared to the standard SIFT descriptor .

Citation Download Citation

Luan Zeng and You Zhai "An improved SIFT descriptor", Proc. SPIE 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 83210O (15 November 2011); https://doi.org/10.1117/12.903778

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KEYWORDS

Image registration

Distortion

Image analysis

Molybdenum

Principal component analysis

3D image processing

Computer vision technology

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