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• W2000370119 endingPage "4143" @default.
• W2000370119 startingPage "4126" @default.
• W2000370119 abstract "Image corner detection is a fundamental task in computer vision. Many applications require reliable detectors to accurately detect corner points, commonly achieved by using image contour information. The curvature definition is sensitive to local variation and edge aliasing, and available smoothing methods are not sufficient to address these problems properly. Hence, we propose Mean Projection Transform (MPT) as a corner classifier and parabolic fit approximation to form a robust detector. The first step is to extract corner candidates using MPT based on the integral properties of the local contours in both the horizontal and vertical directions. Then, an approximation of the parabolic fit is calculated to localize the candidate corner points. The proposed method presents fewer false-positive (FP) and false-negative (FN) points compared with recent standard corner detection techniques, especially in comparison with curvature scale space (CSS) methods. Moreover, a new evaluation metric, called accuracy of repeatability (AR), is introduced. AR combines repeatability and the localization error (Le) for finding the probability of correct detection in the target image. The output results exhibit better repeatability, localization, and AR for the detected points compared with the criteria in original and transformed images." @default.
• W2000370119 created "2016-06-24" @default.
• W2000370119 creator A5033249451 @default.
• W2000370119 creator A5049817112 @default.
• W2000370119 creator A5086762117 @default.
• W2000370119 date "2014-02-28" @default.
• W2000370119 modified "2023-09-26" @default.
• W2000370119 title "Contour-Based Corner Detection and Classification by Using Mean Projection Transform" @default.
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• W2000370119 doi "https://doi.org/10.3390/s140304126" @default.
• W2000370119 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4003934" @default.
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• W2000370119 hasPublicationYear "2014" @default.
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