Contour-based object detection in range images
Autor(en): | Stiene, S. Lingemann, K. Nüchter, A. Hertzberg, J. |
Stichwörter: | Data handling; Data visualization; Extraction; Learning algorithms; Mathematical transformations; Object detection; Object recognition; Scanning; Three dimensional computer graphics; Visualization, Angular radial transformation; Contour description; Contour-based object detections; Detection performance; Object recognition systems; Receiver operating characteristic analysis; Recognition systems; Sources of informations, Feature extraction | Erscheinungsdatum: | 2006 | Herausgeber: | IEEE Computer Society | Journal: | Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006 | Startseite: | 168 | Seitenende: | 175 | Zusammenfassung: | This paper presents a novel object recognition approach based on range images. Due to its insensitivity to illuminaHon, range data is well suited for reliable silhouette extracHon. Silhouette or contour descriptions are good sources of information for object recognition. We propose a complete object recognition system, based on a 3D laser scanner, reliable contour extraction with floor interpretation, feature extraction using a new, fast Eigen-CSS method, and a supervised learning algorithm. The recognition system was successfully tested on range images acquired with a mobile robot, and the results are compared to standard techniques, i.e., Geometric features, Hu and Zernike moments, the Border Signature method and the Angular Radial Transformation. An evaluation using the receiver operating characteristic analysis completes this paper. The Eigen-CSS method has proved to be comparable in detection performance to the top competitors, yet faster than the best one by an order of magnitude in feature extraction time. © 2006 IEEE. |
Beschreibung: | Conference of 3rd International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006 ; Conference Date: 14 June 2006 Through 16 June 2006; Conference Code:72412 |
ISBN: | 9780769528250 | DOI: | 10.1109/3DPVT.2006.46 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-47249134276&doi=10.1109%2f3DPVT.2006.46&partnerID=40&md5=54a4020333eccf7fae696cc116d3943f |
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geprüft am 17.05.2024