Zhou, Wei ; Ma, Caiwen ; Kuijper, Arjan (2018)
Hough-space-based hypothesis generation and hypothesis verification for 3D object recognition and 6D pose estimation.
In: Computers & Graphics, 72
doi: 10.1016/j.cag.2018.01.011
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Hypothesis Generation (HG) and Hypothesis Verification (HV) play an important role in 3D objection recognition. However, performing 3D object recognition in cluttered scenes using HG and HV still re- mains a largely unsolved problem. High False Positive (FP) in HG and HV stages are witnessed due to clutter and occlusion, which will further affect the final accuracy of recognition. To address these prob- lems, we propose a novel Hough-space-based HG approach for extracting hypotheses. Differently from the existing methods, our approach is based on a Hough space which adopts a self-adapted measure to generate hypotheses. Based on this, a novel HV-based method is proposed to verify the hypotheses obtained from HG procedures. The proposed method is evaluated on four public benchmark datasets to verify its performance. Experiments show that our approach outperforms state-of-the-art methods, and obtains a higher recognition rate without sacrificing precision both at high FP rates and high occlusion rates.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Zhou, Wei ; Ma, Caiwen ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Hough-space-based hypothesis generation and hypothesis verification for 3D object recognition and 6D pose estimation |
| Sprache: | Englisch |
| Publikationsjahr: | 2018 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Computers & Graphics |
| Jahrgang/Volume einer Zeitschrift: | 72 |
| DOI: | 10.1016/j.cag.2018.01.011 |
| URL / URN: | https://doi.org/10.1016/j.cag.2018.01.011 |
| Kurzbeschreibung (Abstract): | Hypothesis Generation (HG) and Hypothesis Verification (HV) play an important role in 3D objection recognition. However, performing 3D object recognition in cluttered scenes using HG and HV still re- mains a largely unsolved problem. High False Positive (FP) in HG and HV stages are witnessed due to clutter and occlusion, which will further affect the final accuracy of recognition. To address these prob- lems, we propose a novel Hough-space-based HG approach for extracting hypotheses. Differently from the existing methods, our approach is based on a Hough space which adopts a self-adapted measure to generate hypotheses. Based on this, a novel HV-based method is proposed to verify the hypotheses obtained from HG procedures. The proposed method is evaluated on four public benchmark datasets to verify its performance. Experiments show that our approach outperforms state-of-the-art methods, and obtains a higher recognition rate without sacrificing precision both at high FP rates and high occlusion rates. |
| Freie Schlagworte: | Verification, Object recognition |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 01 Jul 2019 08:35 |
| Letzte Änderung: | 01 Jul 2019 08:35 |
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