JOURNAL ARTICLE

Scale Invariant Feature Transform using oriented pattern

Abstract

Image matching plays an important role in many aspects of computer vision. Our proposed method is based on Scale Invariant Feature Transform (SIFT) which is one of the popular image matching methods. The main ideas behind our method are removing the excess keypoints, adding oriented patterns to descriptor, and decreasing the size of the descriptors. By doing these changes to SIFT, we would have oriented patterns of keypoints. In addition, the numbers of keypoints have been reduced and the places of keypoints would be selected more accurately, and also the size of the descriptors has been reduced.

Keywords:
Scale-invariant feature transform Artificial intelligence Pattern recognition (psychology) Invariant (physics) Image matching Matching (statistics) Computer science Feature matching Computer vision Feature (linguistics) Scale invariance Scale (ratio) Image (mathematics) Feature extraction Mathematics Statistics Geography

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Topics

Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering
Image Retrieval and Classification Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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