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Circle Detection Performance Evaluation Revisited

Elisa Barney Smith 1 Bart Lamiroy 2
2 SYNALP - Natural Language Processing : representations, inference and semantics
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Circle and circular arc detection in images have been a long standing topic in image analysis. It nds numerous applications for both scanned document images as well as in photographic images. As a result, circle detection algorithms are published regularly and benchmarking data sets and contests have been organized on a regular basis over the last decades. Unfortunately, they have not been able to achieve a very clear image establishing which approaches perform best and under what exact conditions. This paper contributes to circle and arc detection, by providing an open and fully reproducible framework for benchmarking and evaluating circle and circular arc detection methods. It builds upon the current state of the art and commonly used metrics by providing a complementary approach through the introduction of synthetic evaluation data for benchmarking versus two noise types at gradually varying noise levels and new performance metrics that are compatible with previous evaluation approaches.
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Submitted on : Thursday, November 24, 2016 - 3:48:18 PM
Last modification on : Wednesday, November 3, 2021 - 4:00:44 AM
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Elisa Barney Smith, Bart Lamiroy. Circle Detection Performance Evaluation Revisited. Graphics Recognition. Current Trends and Challenges : 11th IAPR International Workshop on Graphics Recognition, GREC 2015, Aug 2015, Nancy, France. ⟨10.1007/978-3-319-52159-6_1⟩. ⟨hal-01402369⟩



Les métriques sont temporairement indisponibles