Dimensionality Reduction Based on Feature Points of Underwater Image Mosaic Algorithm

Hong Hai Zhuang; Guo Gao Liu; Xue Wu Zhang; Zhuo Zhang; Min Li; Xin Nan Fan

doi:10.4028/www.scientific.net/AMM.462-463.308

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 462-463Dimensionality Reduction Based on Feature Points...

Dimensionality Reduction Based on Feature Points of Underwater Image Mosaic Algorithm

Abstract:

Image mosaic is a wide perspective to create high resolution for image processing, computer graphics and new field of interdisciplinary research. According to the fact that the real-time performance of Harris feature mosaic algorithm is poor, this paper proposes an improved Harris algorithm based on feature point mosaic of principal component analysis to reduce the dimensionality of the feature points. The algorithm constructs feature descriptors with the feature points, and then uses PCA to reduce the dimension of feature vector descriptor to improve the real-time of the algorithm. Experimental results show that the algorithm can realize the underwater image mosaic and improve the real-time performance of the algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 462-463)

Pages:

308-311

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.462-463.308

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Online since:

November 2013

Authors:

Hong Hai Zhuang, Guo Gao Liu, Xue Wu Zhang, Zhuo Zhang, Min Li, Xin Nan Fan

Keywords:

Feature Descriptor, Harris Feature Points, Image Mosaic, Principal Component Analysis (PCA)

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