3DOR 13

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https://diglib.eg.org/handle/10.2312/308

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Browsing 3DOR 13 by Subject "Computational Geometry and Object Modeling"

Now showing 1 - 5 of 5
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    3D Human Video Retrieval: from Pose to Motion Matching
    (The Eurographics Association, 2013) Slama, Rim; Wannous, Hazem; Daoudi, Mohamed; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    3D video retrieval is a challenging problem lying at the heart of many primary research areas in computer graphics and computer vision applications. In this paper, we present a new 3D human shape matching and motion retrieval framework. Our approach is formulated using Extremal Human Curve (EHC) descriptor extracted from the body surface and a local motion retrieval achieved after motion segmentation. Matching is performed by an efficient method which takes advantage of a compact EHC representation in open curve Shape Space and an elastic distance measure. Moreover, local 3D video retrieval is performed by dynamic time warping (DTW) algorithm in the feature space vectors. Experiments on both synthetic and real 3D human video sequences show that our approach provides an accurate shape similarity in video compared to the best state-of-the-art approaches. Finally, results on motion retrieval are promising and show the potential of this approach.
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    Geometric Histograms of 3D Keypoints for Face Identification with Missing Parts
    (The Eurographics Association, 2013) Berretti, Stefano; Werghi, Naoufel; Bimbo, Alberto del; Pala, Pietro; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    In this work, an original solution to 3D face identification is proposed, which supports recognition also in the case of probes with missing parts. Distinguishing traits of the face are captured by first extracting 3D keypoints of a face scan, then measuring how the face surface changes in the keypoints neighborhood using a local descriptor. To this end, an adaptation of the meshDOG algorithm to the case of 3D faces is proposed, together with a multi-ring geometric histogram descriptor. Face similarity is then evaluated by comparing local keypoint descriptors across inlier pairs of matching keypoints between probe and gallery scans. Experiments have been performed to assess the keypoints distribution and repeatability. Recognition accuracy of the proposed approach has been evaluated on the Bosphorus database, showing competitive results with respect to existing 3D face biometrics solutions.
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    Local Signature Quantization by Sparse Coding
    (The Eurographics Association, 2013) Boscaini, Davide; Castellani, Umberto; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    In 3D object retrieval it is very important to define reliable shape descriptors, which compactly characterize geometric properties of the underlying surface. To this aim two main approaches are considered: global, and local ones. Global approaches are effective in describing the whole object, while local ones are more suitable to characterize small parts of the shape. Some strategies to combine these two approaches have been proposed recently but still no consolidate work is available in this field. With this paper we address this problem and propose a new method based on sparse coding techniques. A set of local shape descriptors are collected from the shape. Then a dictionary is trained as generative model. In this fashion the dictionary is used as global shape descriptor for shape retrieval purposes. Preliminary experiments are performed on a standard dataset by showing a drastic improvement of the proposed method in comparison with well known local-to-global and global approaches.
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    SHREC'13 Track: Retrieval of Objects Captured with Low-Cost Depth-Sensing Cameras
    (The Eurographics Association, 2013) Machado, J.; Ferreira, A.; Pascoal, P. B.; Abdelrahman, M.; Aono, M.; El-Melegy, M.; Farag, A.; Johan, H.; Li, B.; Lu, Y.; Tatsuma, A.; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    The SHREC'13 Track: Retrieval of Objects Captured with Low-Cost Depth-Sensing Cameras is a first attempt at evaluating the effectiveness of 3D shape retrieval algorithms in low fidelity model databases, such as the ones captured with commodity depth cameras. Both target and query set are composed by objects captured with a Kinect camera and the objective is to retrieve the models in the target set who were considered relevant by a human-generated ground truth. Given how widespread such devices are, and how easy it is becoming for an everyday user to capture models in his household, the necessity of algorithms for these new types of 3D models is also increasing. Three groups have participated in the contest, providing rank lists for the set of queries, which is composed of 12 models from the target set.
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    SymPan: 3D Model Pose Normalization via Panoramic Views and Reflective Symmetry
    (The Eurographics Association, 2013) Sfikas, Konstantinos; Pratikakis, Ioannis; Theoharis, Theoharis; Umberto Castellani and Tobias Schreck and Silvia Biasotti and Ioannis Pratikakis and Afzal Godil and Remco Veltkamp
    A novel pose normalization method, based on panoramic views and reflective symmetry, is presented. Initially, the surface of a 3D model is projected onto the lateral surface of a circumscribed cylinder, aligned with the primary principal axis of space. Based on this cylindrical projection, a normals' deviation map is extracted and using an octree-based search strategy, the rotation which optimally aligns the primary principal axis of the 3D model and the cylinder's axis is computed. The 3D model's secondary principal axis is then aligned with the secondary principal axis of space in a similar manner. The proposed method is incorporated in a hybrid scheme, that serves as the pose normalization method in a state-of-the-art 3D model retrieval system. The effectiveness of this system, using the hybrid pose normalization scheme, is evaluated in terms of retrieval accuracy and the results clearly show improved performance against current approaches.