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- W2909417188 abstract "Image registration has received great attention from researchers over the last few decades. SIFT (Scale Invariant Feature Transform), a local descriptor-based technique is widely used for registering and matching images. To establish correspondences between images, SIFT uses a Euclidean Distance ratio metric. However, this approach leads to a lot of incorrect matches and eliminating these inaccurate matches has been a challenge. Various methods have been proposed attempting to mitigate this problem. In this paper, we propose a scale and orientation harmony-based pruning method that improves image matching process by successfully eliminating incorrect SIFT descriptor matches. Moreover, our technique can predict the image transformation parameters based on a novel adaptive clustering method with much higher matching accuracy. Our experimental results have shown that the proposed method has achieved averages of approximately 16% and 10% higher matching accuracy compared to the traditional SIFT and a contemporary method respectively." @default.
- W2909417188 created "2019-01-25" @default.
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- W2909417188 date "2018-12-01" @default.
- W2909417188 modified "2023-09-25" @default.
- W2909417188 title "Enhancing the Effectiveness of Local Descriptor Based Image Matching" @default.
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- W2909417188 doi "https://doi.org/10.1109/dicta.2018.8615800" @default.
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