Improved rotational matching of SIFT and SURF

K. M. Goh; M. M. Mokji; S. A. R. Abu-Bakar

doi:10.1117/12.953950

8 June 2012 Improved rotational matching of SIFT and SURF

K. M. Goh, M. M. Mokji, S. A. R. Abu-Bakar

Proceedings Volume 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012); 83341Y (2012) https://doi.org/10.1117/12.953950
Event: Fourth International Conference on Digital Image Processing (ICDIP 2012), 2012, Kuala Lumpur, Malaysia

Abstract

Scale-Invariant Feature Transform(SIFT) and Speeded-Up Robust Feature(SURF) are common techniques used for extracting robust features that can be used to perform matching between different viewpoints of scenes. Both methods basically involve three main stages, which are feature extraction, orientation assignment and feature descriptor extraction for matching. SURF is computation efficient compared to SIFT because the integral image is used for the convolutions to reduce computation time. However, both methods also do not focus much on the technique of matching. This paper introduces a method which can help to improve the rotational matching performance in term of accuracy by establishing a decision matrix and an approximated rotational angle within two corresponding images. The proposed method generally improved the matching rate around 10% to 20% in terms of accuracy.

Citation Download Citation

K. M. Goh, M. M. Mokji, and S. A. R. Abu-Bakar "Improved rotational matching of SIFT and SURF", Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 83341Y (8 June 2012); https://doi.org/10.1117/12.953950

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KEYWORDS

Image processing

Convolution

Feature extraction

Computer vision technology

Machine vision

Cameras

Image filtering

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