{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T13:13:49Z","timestamp":1770297229984,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,2,24]],"date-time":"2017-02-24T00:00:00Z","timestamp":1487894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61573048"],"award-info":[{"award-number":["61573048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61620106012"],"award-info":[{"award-number":["61620106012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"International Scientific and Technological Cooperation Projects of China","award":["2015DFG12650"],"award-info":[{"award-number":["2015DFG12650"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>RGB-D sensors have been widely used in various areas of computer vision and graphics. A good descriptor will effectively improve the performance of operation. This article further analyzes the recognition performance of shape features extracted from multi-modality source data using RGB-D sensors. A hybrid shape descriptor is proposed as a representation of objects for recognition. We first extracted five 2D shape features from contour-based images and five 3D shape features over point cloud data to capture the global and local shape characteristics of an object. The recognition performance was tested for category recognition and instance recognition. Experimental results show that the proposed shape descriptor outperforms several common global-to-global shape descriptors and is comparable to some partial-to-global shape descriptors that achieved the best accuracies in category and instance recognition. Contribution of partial features and computational complexity were also analyzed. The results indicate that the proposed shape features are strong cues for object recognition and can be combined with other features to boost accuracy.<\/jats:p>","DOI":"10.3390\/s17030451","type":"journal-article","created":{"date-parts":[[2017,2,24]],"date-time":"2017-02-24T06:07:21Z","timestamp":1487916441000},"page":"451","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors"],"prefix":"10.3390","volume":"17","author":[{"given":"Zhong","family":"Liu","sequence":"first","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changchen","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingming","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihai","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., and Torr, P.H. (2015, January 11\u201318). Conditional random fields as recurrent neural networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Song, H.O., Xiang, Y., Jegelka, S., and Savarese, S. (2016, January 27\u201330). Deep metric learning via lifted structured feature embedding. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.434"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Supancic, J.S., Rogez, G., Yang, Y., Shotton, J., and Ramanan, D. (2015, January 11\u201318). Depth-based hand pose estimation: Methods, data, and challenges. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.217"},{"key":"ref_8","unstructured":"Pieroni, L., Rossi-Arnaud, C., and Baddeley, A.D. (2011). Spatial Working Memory, Psychology Press."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rosselli, F.B., Alemi, A., Ansuini, A., and Zoccolan, D. (2015). Object similarity affects the perceptual strategy underlying invariant visual object recognition in rats. Front. Neural Circuits, 9.","DOI":"10.3389\/fncir.2015.00010"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lai, K., Bo, L., Ren, X., and Fox, D. (2011, January 9\u201313). A large-scale hierarchical multi-view RGB-D object dataset. Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980382"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Miller, S., and Fei-Fei, L. (2013, January 6\u201310). Object discovery in 3D scenes via shape analysis. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630857"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1145\/2185520.2185527","article-title":"Sketch-based shape retrieval","volume":"31","author":"Eitz","year":"2012","journal-title":"ACM Trans. Graph."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1145\/2766908","article-title":"Semantic shape editing using deformation handles","volume":"34","author":"Yumer","year":"2015","journal-title":"ACM Trans. Graph."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.imavis.2014.02.002","article-title":"3D shape descriptor for object recognition based on kinect-like depth image","volume":"32","author":"Sheikh","year":"2014","journal-title":"Image Vis. Comput."},{"key":"ref_15","unstructured":"Berg, A.C., Berg, T.L., and Malik, J. (2005, January 20\u201325). Shape matching and object recognition using low distortion correspondences. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Heisele, B., and Rocha, C. (2008, January 8\u201311). Local shape features for object recognition. Proceedings of the 19th International Conference on Pattern Recognition (ICPR), Tampa, FL, USA.","DOI":"10.1109\/ICPR.2008.4761194"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s11263-012-0521-z","article-title":"Shape-based object detection via boundary structure segmentation","volume":"99","author":"Toshev","year":"2012","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.asoc.2014.09.051","article-title":"Moving object classification using local shape and hog features in wavelet-transformed space with hierarchical SVM classifiers","volume":"28","author":"Liang","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bo, L., Ren, X., and Fox, D. (2011, January 25\u201330). Depth kernel descriptors for object recognition. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6095119"},{"key":"ref_20","first-page":"1065","article-title":"Shape recognition with spectral distances","volume":"5","author":"Bronstein","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1145\/1183287.1183289","article-title":"Parametrizations for triangular g(k) spline surfaces of low degree","volume":"25","author":"Prautzsch","year":"2006","journal-title":"ACM Trans. Graph."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., Marton, Z.C., and Beetz, M. (2008, January 22\u201326). Aligning point cloud views using persistent feature histograms. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650967"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., and Beetz, M. (2009, January 12\u201317). Fast Point Feature Histograms (FPFH) for 3D registration. Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Bradski, G., Thibaux, R., and Hsu, J. (2010, January 18\u201322). Fast 3D recognition and pose using the Viewpoint Feature Histogram. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5651280"},{"key":"ref_25","unstructured":"Bo, L., and Sminchisescu, C. (2009, January 7\u201310). Efficient match kernel between sets of features for visua recognition. Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"117202","DOI":"10.1117\/1.3506200","article-title":"Support vector machine\u2013based facial-expression recognition method combining shape and appearance","volume":"49","author":"Han","year":"2010","journal-title":"Opt. Eng."},{"key":"ref_27","unstructured":"Ekman, W.F.P. (2010). Facial Action Coding System: A Technique for the Measurement of Facial Movement, Consulting Psychologists Press."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"123110","DOI":"10.1117\/1.OE.54.12.123110","article-title":"Recognition of interior photoelectric devices by using dual criteria of shape and local texture","volume":"54","author":"Qian","year":"2015","journal-title":"Opt. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"024301","DOI":"10.1117\/1.OE.52.2.024301","article-title":"Object shape classification and scene shape representation for three-dimensional laser scanned outdoor data","volume":"52","author":"Ning","year":"2013","journal-title":"Opt. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.3390\/s16081180","article-title":"Object detection applied to indoor environments for mobile robot navigation","volume":"16","author":"Carolina","year":"2016","journal-title":"Sensors"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1037\/a0029333","article-title":"A century of gestalt psychology in visual perception: I. perceptual grouping and figure\u2013ground organization","volume":"138","author":"Wagemans","year":"2012","journal-title":"Psychol. Bull."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hauagge, D.C., and Snavely, N. (2012, January 16\u201321). Image matching using local symmetry features. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247677"},{"key":"ref_33","unstructured":"Sun, Y. (2012). Symmetry and Feature Selection in Computer Vision. [Ph.D. Dissertation, University of California]."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huebner, K., and Zhang, J. (2006, January 9\u201315). Stable symmetry feature detection and classification in panoramic robot vision systems. Proceedings of the 2006 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Beijing, China.","DOI":"10.1109\/IROS.2006.282581"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A Tutorial on Support Vector Machines for Pattern Recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1145\/571647.571648","article-title":"Shape distributions","volume":"21","author":"Osada","year":"2002","journal-title":"ACM Trans. Graph."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1007\/s11263-010-0319-9","article-title":"Shape similarity for 3d video sequences of people","volume":"89","author":"Huang","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/3-540-48482-5_14","article-title":"3D shape histograms for similarity search and classification in spatial databases","volume":"Volume 1651","author":"Ankerst","year":"1999","journal-title":"Advances in Spatial Databases"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/3\/451\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:29:04Z","timestamp":1760207344000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/3\/451"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,24]]},"references-count":38,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2017,3]]}},"alternative-id":["s17030451"],"URL":"https:\/\/doi.org\/10.3390\/s17030451","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,2,24]]}}}