IPAB Seminar - 24/01/2019 Abstract: The work that I will present reviews the main results of my current research activities, which cover three main issues; namely 3D shape analysis, cross domain retrieval, and 3D action recognition. For 3D shape analysis, we proposed three main contributions. First, we proposed covariance matrices as new descriptors for 3D shape matching. Second, we proposed the use of a set of Riemannian metrics for computing distances between covariance matrices and analysed their clustering behaviour. Using a Riemannian metric on the manifold of covariance matrices, we introduced the concepts of Bag of Covariance matrices (BoC) and spatially-sensitive BoC, as extensions of the standard bag of words originally introduced in Euclidean spaces, for 3D shape retrieval and classification. We also extended the standard kernel methods, such as kernel Support Vector Machines (kSVM), to the space of covariance matrices for 3D shape analysis. For the cross domain retrieval, we addressed the issue of searching 3D shapes using different modalities. First, we proposed different view based 3D shape retrieval methods. Second, we proposed 3D shape retrieval method that uses 3D sketch as a query. Finally, we proposed a generic framework for multimodal shape querying that involves 3D shapes (3D meshes), 2D images (photos) and hand-drawn sketches. For 3D action recognition, we proposed different types of action features computed from the skeletons in the action sequences. We also proposed to learn feature combination in order to improve the recognition accuracy. We further proposed a multiple classifier combination system which offers the possibility to derive a measure of contradiction between the classifier decisions to be fused. This allowed us boosting the accuracy on the accepted data, which is desired in situations where a miss-classification is very expensive or must not happen. Biography: Hedi Tabia received the M.S. degree in computer science from the INSA of Rouen - Public school of engineers, France. In 2011, he obtained the PhD degree in computer science from the University of Lille. From October 2011 to august 2012, he held a postdoctoral research associate position at the IEF laboratory (University of Paris-sud). Since September 2012, he is associate professor at the ENSEA in the ETIS Laboratory. Jan 24 2019 13.00 - 14.00 IPAB Seminar - 24/01/2019 3D data analysis (shapes and motions) Hedi Tabia, ENSEA IF 4.31/33
IPAB Seminar - 24/01/2019 Abstract: The work that I will present reviews the main results of my current research activities, which cover three main issues; namely 3D shape analysis, cross domain retrieval, and 3D action recognition. For 3D shape analysis, we proposed three main contributions. First, we proposed covariance matrices as new descriptors for 3D shape matching. Second, we proposed the use of a set of Riemannian metrics for computing distances between covariance matrices and analysed their clustering behaviour. Using a Riemannian metric on the manifold of covariance matrices, we introduced the concepts of Bag of Covariance matrices (BoC) and spatially-sensitive BoC, as extensions of the standard bag of words originally introduced in Euclidean spaces, for 3D shape retrieval and classification. We also extended the standard kernel methods, such as kernel Support Vector Machines (kSVM), to the space of covariance matrices for 3D shape analysis. For the cross domain retrieval, we addressed the issue of searching 3D shapes using different modalities. First, we proposed different view based 3D shape retrieval methods. Second, we proposed 3D shape retrieval method that uses 3D sketch as a query. Finally, we proposed a generic framework for multimodal shape querying that involves 3D shapes (3D meshes), 2D images (photos) and hand-drawn sketches. For 3D action recognition, we proposed different types of action features computed from the skeletons in the action sequences. We also proposed to learn feature combination in order to improve the recognition accuracy. We further proposed a multiple classifier combination system which offers the possibility to derive a measure of contradiction between the classifier decisions to be fused. This allowed us boosting the accuracy on the accepted data, which is desired in situations where a miss-classification is very expensive or must not happen. Biography: Hedi Tabia received the M.S. degree in computer science from the INSA of Rouen - Public school of engineers, France. In 2011, he obtained the PhD degree in computer science from the University of Lille. From October 2011 to august 2012, he held a postdoctoral research associate position at the IEF laboratory (University of Paris-sud). Since September 2012, he is associate professor at the ENSEA in the ETIS Laboratory. Jan 24 2019 13.00 - 14.00 IPAB Seminar - 24/01/2019 3D data analysis (shapes and motions) Hedi Tabia, ENSEA IF 4.31/33
Jan 24 2019 13.00 - 14.00 IPAB Seminar - 24/01/2019 3D data analysis (shapes and motions) Hedi Tabia, ENSEA
