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• W3214202416 abstract "Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction." @default.
• W3214202416 created "2021-11-22" @default.
• W3214202416 creator A5037133186 @default.
• W3214202416 creator A5055839781 @default.
• W3214202416 creator A5090585779 @default.
• W3214202416 date "2021-11-17" @default.
• W3214202416 modified "2023-09-26" @default.
• W3214202416 title "Multiple Cylinder Extraction from Organized Point Clouds" @default.
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• W3214202416 doi "https://doi.org/10.3390/s21227630" @default.
• W3214202416 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8621001" @default.
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• W3214202416 hasPublicationYear "2021" @default.
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