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A new adaptive threshold technique for improved matching in SIFT
- Issue Date
- Aug-2011
- Publisher
- IEEE
- Keywords
- Object Detection; Keypoints; Face recognition; Object recognition; Adaptive thresholds; Local extremum; Scale invariant feature transforms; False matches; Adaptive thresholding; Feature extraction; Descriptors; Vision systems; Adaptive threshold technique
- Citation
- 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS), pp 1 - 4
- Pages
- 4
- Indexed
- SCOPUS
- Journal Title
- 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS)
- Start Page
- 1
- End Page
- 4
- ISSN
- 1548-3746
1558-3899
- Abstract
- Scale Invariant Feature Transform (SIFT) is widely used in vision systems for various applications such as object detection and face recognition. In SIFT, threshold is applied to determine local extrema (keypoint selection) and global extrema (keypoint refinement). Next, descriptor matching is performed with selected keypoints. This paper presents a new method of adaptive thresholding which improves keypoint selection in SIFT. The value of adaptive threshold depends upon the average regional intensity of an image. Experimental results show that our method is robust for matching the keypoints among the images with illumination differences. Our new adaptive threshold technique for keypoint selection reduces false matches and shows significantly improved performance in experimental results. © 2011 IEEE.
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