Soumaya Mathlouthi - Academia.edu (original) (raw)
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Papers by Soumaya Mathlouthi
Advanced Concepts for Intelligent Vision Systems
In this paper, we intend to introduce a novel curved 3D face representation. It is constructed on... more In this paper, we intend to introduce a novel curved 3D face representation. It is constructed on some static parts of the face which correspond to the nose and the eyes. Each part is described by the level curves of the superposition of several geodesic potentials generated from many reference points. We propose to describe the eye region by a bipolar representation based on the superposition of two geodesic potentials generated from two reference points and the nose by a three-polar one (three reference points). We use the BU-3DFE database of 3D faces to test the accuracy of the proposed approach. The obtained results in the sense of the Hausdorff shape distance prove the performance of the novel representation for 3D faces identification. The obtained scores are comparable to the state of the art methods in the most of cases.
Signal Processing: Image Communication
We present a novel method for 3D-object retrieval using Bag-of-Feature (BoF) approaches . The met... more We present a novel method for 3D-object retrieval using Bag-of-Feature (BoF) approaches . The method starts by selecting and then describing a set of points from the 3D-object. The proposed descriptor is an indexed collection of closed curves in R 3 on the 3D-surface. Such descriptor has the advantage of being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster those descriptors to form a shape vocabulary. Then, each point selected in the object is associated to a cluster (word) in that vocabulary. Finally, a BoF histogram counting the occurrences of every word is computed. In order to assess our method, we used shapes from the TOSCA and Sumner datasets. The results clearly demonstrate that the method is robust to many kind of transformations and produces higher precision compared with some state-of-the-art methods.
Advanced Concepts for Intelligent Vision Systems
In this paper, we intend to introduce a novel curved 3D face representation. It is constructed on... more In this paper, we intend to introduce a novel curved 3D face representation. It is constructed on some static parts of the face which correspond to the nose and the eyes. Each part is described by the level curves of the superposition of several geodesic potentials generated from many reference points. We propose to describe the eye region by a bipolar representation based on the superposition of two geodesic potentials generated from two reference points and the nose by a three-polar one (three reference points). We use the BU-3DFE database of 3D faces to test the accuracy of the proposed approach. The obtained results in the sense of the Hausdorff shape distance prove the performance of the novel representation for 3D faces identification. The obtained scores are comparable to the state of the art methods in the most of cases.
Signal Processing: Image Communication
We present a novel method for 3D-object retrieval using Bag-of-Feature (BoF) approaches . The met... more We present a novel method for 3D-object retrieval using Bag-of-Feature (BoF) approaches . The method starts by selecting and then describing a set of points from the 3D-object. The proposed descriptor is an indexed collection of closed curves in R 3 on the 3D-surface. Such descriptor has the advantage of being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster those descriptors to form a shape vocabulary. Then, each point selected in the object is associated to a cluster (word) in that vocabulary. Finally, a BoF histogram counting the occurrences of every word is computed. In order to assess our method, we used shapes from the TOSCA and Sumner datasets. The results clearly demonstrate that the method is robust to many kind of transformations and produces higher precision compared with some state-of-the-art methods.