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Improve the Image Feature-Matching Based on Scale Invariant Feature Transform Algorithm

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SIFT algorithm has strong robustness and stability, which can be applied in many bad conditions to achieve high recognition rate. The 128-dimensional feature descriptor has good independence. However, it also contains redundant information, which makes the calculation amount of following matching increase. An improved SIFT algorithm is proposed to solve the disadvantages of large computation and not meet “real-time” of the original one, which reduces the dimension of the feature vector and reduces the mismatching of SIFT algorithm. It has been proved from the experiment that the improved algorithm can significantly increase running speed and guarantee the robustness. Besides, it can meet “real-time” requirements.
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Keywords: DIMENSION; FEATURE-MATCHING; REAL-TIME; ROBUSTNESS

Document Type: Research Article

Publication date: 2012-03-01

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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