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Effect of masking techniques on computational complexity reduction of scale invariant feature transform

The Scale Invariant Feature Transform (SIFT) is algorithm use in feature detection and description, it is famous and has dominated the research community. The SIFT is a standard compared to others. Notwithstanding its sturdiness, SIFT has limitation of computational complexity, this has pose great l...

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Main Author: Sai'd, Yunusa Ali
Format: Thesis
Language:English
Published: 2015
Online Access:http://psasir.upm.edu.my/id/eprint/65602/1/FK%202015%20150IR.pdf
http://psasir.upm.edu.my/id/eprint/65602/
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spelling my.upm.eprints.656022018-10-02T04:18:51Z http://psasir.upm.edu.my/id/eprint/65602/ Effect of masking techniques on computational complexity reduction of scale invariant feature transform Sai'd, Yunusa Ali The Scale Invariant Feature Transform (SIFT) is algorithm use in feature detection and description, it is famous and has dominated the research community. The SIFT is a standard compared to others. Notwithstanding its sturdiness, SIFT has limitation of computational complexity, this has pose great limitation to real time and on-line implementations. Effort has been done by researchers to improve on SIFT performance because of it robustness, the computational cost was reduced to some extent but the distinctiveness cannot be compared with others(Descriptors). Thus, the aim of this research is to propose masking techniques to images, by eliminating areas with no or sparse keypoints in the feature extraction process, thereby reducing the computational cost of SIFT as the dominant descriptor in computer and robotic vision. Performance of the proposed approach was able to reduce the computational time to 47.27% at 0.7 Threshold with 17.94% in keypoint reduction. However, the masked SIFT was used in place categorization for performance evaluation. The evaluation was conducted using two classifiers in pattern recognition on the classification of Royal Institute of Technology-Image Data for Robot Localization (KTH-IDOL2) database. The classifiers are Nearest Neighbor (NN) and Multilayer Perceptron (MLP). Comparison of the categorization and classification accuracy produced 70% with the nearest neighbor(NN) and improved to 80% with Multilayer perceptron. In conclusion, the proposed approach was able to improved the computational time of SIFT higher than the recent published work in literature. 2015-04 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/65602/1/FK%202015%20150IR.pdf Sai'd, Yunusa Ali (2015) Effect of masking techniques on computational complexity reduction of scale invariant feature transform. PhD thesis, Universiti Putra Malaysia.
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description The Scale Invariant Feature Transform (SIFT) is algorithm use in feature detection and description, it is famous and has dominated the research community. The SIFT is a standard compared to others. Notwithstanding its sturdiness, SIFT has limitation of computational complexity, this has pose great limitation to real time and on-line implementations. Effort has been done by researchers to improve on SIFT performance because of it robustness, the computational cost was reduced to some extent but the distinctiveness cannot be compared with others(Descriptors). Thus, the aim of this research is to propose masking techniques to images, by eliminating areas with no or sparse keypoints in the feature extraction process, thereby reducing the computational cost of SIFT as the dominant descriptor in computer and robotic vision. Performance of the proposed approach was able to reduce the computational time to 47.27% at 0.7 Threshold with 17.94% in keypoint reduction. However, the masked SIFT was used in place categorization for performance evaluation. The evaluation was conducted using two classifiers in pattern recognition on the classification of Royal Institute of Technology-Image Data for Robot Localization (KTH-IDOL2) database. The classifiers are Nearest Neighbor (NN) and Multilayer Perceptron (MLP). Comparison of the categorization and classification accuracy produced 70% with the nearest neighbor(NN) and improved to 80% with Multilayer perceptron. In conclusion, the proposed approach was able to improved the computational time of SIFT higher than the recent published work in literature.
format Thesis
author Sai'd, Yunusa Ali
spellingShingle Sai'd, Yunusa Ali
Effect of masking techniques on computational complexity reduction of scale invariant feature transform
author_facet Sai'd, Yunusa Ali
author_sort Sai'd, Yunusa Ali
title Effect of masking techniques on computational complexity reduction of scale invariant feature transform
title_short Effect of masking techniques on computational complexity reduction of scale invariant feature transform
title_full Effect of masking techniques on computational complexity reduction of scale invariant feature transform
title_fullStr Effect of masking techniques on computational complexity reduction of scale invariant feature transform
title_full_unstemmed Effect of masking techniques on computational complexity reduction of scale invariant feature transform
title_sort effect of masking techniques on computational complexity reduction of scale invariant feature transform
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/65602/1/FK%202015%20150IR.pdf
http://psasir.upm.edu.my/id/eprint/65602/
_version_ 1643838355250610176
score 13.211869

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