An improved SIFT algorithm based on KFDA in image registration

Peng Chen; Lijuan Yang; Jinfeng Huo

doi:10.1117/12.2234856

2 March 2016 An improved SIFT algorithm based on KFDA in image registration

Peng Chen, Lijuan Yang, Jinfeng Huo

Proceedings Volume 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015); 99010G (2016) https://doi.org/10.1117/12.2234856
Event: 2015 ISPRS International Conference on Computer Vision in Remote Sensing, 2015, Xiamen, China

Abstract

As a kind of stable feature matching algorithm, SIFT has been widely used in many fields. In order to further improve the robustness of the SIFT algorithm, an improved SIFT algorithm with Kernel Discriminant Analysis (KFDA-SIFT) is presented for image registration. The algorithm uses KFDA to SIFT descriptors for feature extraction matrix, and uses the new descriptors to conduct the feature matching, finally chooses RANSAC to deal with the matches for further purification. The experiments show that the presented algorithm is robust to image changes in scale, illumination, perspective, expression and tiny pose with higher matching accuracy.

Citation Download Citation

Peng Chen, Lijuan Yang, and Jinfeng Huo "An improved SIFT algorithm based on KFDA in image registration", Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 99010G (2 March 2016); https://doi.org/10.1117/12.2234856

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KEYWORDS

Image registration

Feature extraction

Analytical research

Image compression

Statistical analysis

Nickel

Associative arrays

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