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- W4250453583 abstract "Corners play an important role in object identification methods used in machine vision and image processing systems. Single-scale feature detection finds it hard to detect both fine and coarse features at the same time. On the other hand, multi-scale feature detection is inherently able to solve this problem. This paper proposes an improved multi-scale corner detector with dynamic region of support, which is based on curvature scale space (CSS) technique. The proposed detector first uses an adaptive local curvature threshold instead of a single global threshold as in the original and enhanced CSS methods. Second, the angles of corner candidates are checked in a dynamic region of support for eliminating falsely detected corners. The proposed method has been evaluated over a number of images and compared with some popular corner detectors. The results showed that the proposed method offers a robust and effective solution to images containing widely different size features." @default.
- W4250453583 created "2022-05-12" @default.
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- W4250453583 date "2004-01-01" @default.
- W4250453583 modified "2023-10-18" @default.
- W4250453583 title "Curvature scale space corner detector with adaptive threshold and dynamic region of support" @default.
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- W4250453583 doi "https://doi.org/10.1109/icpr.2004.1334377" @default.
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