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Photometric Intensity Profiles Analysis for Thick Segment Recognition and Geometric Measures

Nicolas Aubry 1 Bertrand Kerautret 1 Philippe Even 1 Isabelle Debled-Rennesson 1 
1 ADAGIO - Applying Discrete Algorithms to Genomics and Imagery
LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
Abstract : The segmentation or the geometric analysis of specular objects is known as a difficult problem in the computer vision domain. It is also true for the problem of line detection where the specular reflection implies numerous false positive line detection or missing lines located on the dark parts of the object. This limitation reduces its potential use for concrete industrial applications where metallic objects are frequent. In order to overcome this limitation, a new strategy to detect thick segment is proposed. It is not based on the image gradient as usually, but rather exploits the image intensity profile defined inside a parallel strip primitive. Associated to a digital straight segment recognition algorithmwhich is robust to noise, this strategy was implemented to track metallic tubular objects in gray-level images. The efficiency of the proposed method is demonstrated through extensive tests using an actual industrial application. An alternate release intended to overcome the possible impact of the digitization process on the achieved performance is also introduced. Both strategies are discussed at the end of the article.
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https://hal.inria.fr/hal-02077892
Contributor : Philippe Even Connect in order to contact the contributor
Submitted on : Sunday, March 24, 2019 - 1:49:57 PM
Last modification on : Friday, March 4, 2022 - 2:51:40 PM

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Nicolas Aubry, Bertrand Kerautret, Philippe Even, Isabelle Debled-Rennesson. Photometric Intensity Profiles Analysis for Thick Segment Recognition and Geometric Measures. Mathematical Morphology - Theory and Applications, 2017, 2 (1), pp.34-54. ⟨10.1515/mathm-2017-0003⟩. ⟨hal-02077892⟩

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