Paper
10 October 2013 An improved SURF descriptor based on sector area partitioning
Luan Zeng, You Zhai, Xiu-hua Fang
Author Affiliations +
Proceedings Volume 8916, Sixth International Symposium on Precision Mechanical Measurements; 89163K (2013) https://doi.org/10.1117/12.2035753
Event: Sixth International Symposium on Precision Mechanical Measurements, 2013, Guiyang, China
Abstract
In order to improve the robustness and real time performance of SURF based image matching algorithms, a constructing method of SURF descriptor based on sector area partitioning in a circular region was proposed and the dimension of descriptors was reduced from 64 to 32. We compute the new descriptor in a circular local region (the radius set to 10s). Firstly, the local region is divided into 8 equal sector areas according to the dominant orientation in inverse time order. Secondly, Define the dominate orientation and its orthogonal orientation as x and y axis of the key-point’s local frame. Thirdly, compute the Haar wavelet response in x and y directions within the key-point local region. In order to reduce the boundary effect and outer noise, Haar wavelet response in the same Grid of different triangle is both assigned to each sector in different weight, and then a gaussian weighting function is used. Compute the histogram of Haar wavelet response and absolute Haar wavelet response, so each sector sub-region constitutes a vector with 4 dimensions. Finally, a descriptor with 32 dimensions is constituted and the descriptor is normalized to achieve illumination invariance. The experimental results indicate that the average matching speed of the new method increase of about 31.18.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Luan Zeng, You Zhai, and Xiu-hua Fang "An improved SURF descriptor based on sector area partitioning ", Proc. SPIE 8916, Sixth International Symposium on Precision Mechanical Measurements, 89163K (10 October 2013); https://doi.org/10.1117/12.2035753
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KEYWORDS
Wavelets

Detection and tracking algorithms

Computer vision technology

Image processing

Machine vision

Image filtering

Reconstruction algorithms

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