Journal
Object Recognition using SIFT
| Identifier | --shivakanth-658 |
|---|---|
| Penulis / Pengarang | Shivakanth, Archana Mane |
| Kata Kunci | SIFT - Scale Invariant Feature Transform NSS – Nearest Neighbor Search DOG-Difference of Gaussian. |
| Tanggal Publikasi | 08 Jun 2014 |
| Bahasa | Bahasa Indonesia |
| Abstrak / Deskripsi | Image matching is a fundamental aspect of computer vision, including object recognition or scene recognition. For the Human recognition system despite of different viewpoints and differences, it is easier to identify the images. This task is still a challenge for computer vision for extracting the scale – invariant and shift – invariant features from images to perform reliable object recognition. In this paper, the object recognition system which can resolve the difficulty of rotations of object, scale changes and illumination areresolved with the help of “SIFT algorithm”. It is implemented with different phases such as scale space extreme detection, key point localization. The SIFT algorithm implemented with MATLAB, which is one of efficient tool to perform image processing. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to single object, and finally performing verification through least-square solution for consistent pose parameters. This approach to recognition can robustly identify objects between clutter and occlusion while achieving near real-time performance. |
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IJISET_V1_I4_56.pdf
Image matching is a fundamental aspect of computer vision, including object recognition or scene recognition.…
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