21 July 2017 Signature of position angles histograms for 3D object recognition
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042045 (2017) https://doi.org/10.1117/12.2281542
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
This paper presents a robust and rotation invariant local surface descriptor by encoding the position angles of neighboring points with a stable and unique local reference frame (LRF) into a 1D histogram. The whole procedure includes two stages: the first stage is to construct a unique LRF by performing eigenvalue decomposition on the covariance matrix formed using all the neighboring points on the local surface. On the second stage, the sphere support field of a key point was divided along the radius into several sphere shells which is similar with the Signature of Histograms OrienTations (SHOT). In each sphere shell, we calculate the cosine of the angles between the neighboring points and the x-axis, z-axis respectively to form two 1D histograms. Finally, all the 1D histograms were stitched together followed by a normalization to generate the local surface descriptor. Experiment results show that our proposed local feature descriptor is robust to noise and varying mesh-resolutions. Moreover, our local feature descriptor based 3D object recognition algorithm achieved a high average recognition rate of 98.9% on the whole UWA dataset.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rongrong Lu, Rongrong Lu, Feng Zhu, Feng Zhu, Yingming Hao, Yingming Hao, Huiying Cai, Huiying Cai, Yinshuai Zhao, Yinshuai Zhao, } "Signature of position angles histograms for 3D object recognition", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042045 (21 July 2017); doi: 10.1117/12.2281542; https://doi.org/10.1117/12.2281542


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