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LIDAR sensors are usually used to provide autonomous vehicles with 3D representations of their environment. In ideal conditions, geometrical models could detect the road in LIDAR scans, at the cost of a manual tuning of numerical constraints, and a lack of flexibility. We instead propose an evidential pipeline, to accumulate road detection results obtained from neural networks. First, we introduce RoadSeg, a new convolutional architecture that is optimized for road detection in LIDAR scans. RoadSeg is used to classify individual LIDAR points as either belonging to the road, or not. Yet, such point-level classification results need to be converted into a dense representation, that can be used by an autonomous vehicle. We thus secondly present an evidential road mapping algorithm, that fuses consecutive road detection results. We benefitted from a reinterpretation of logistic classifiers, which can be seen as generating a collection of simple evidential mass functions. An evidential grid map that depicts the road can then be obtained, by projecting the classification results from RoadSeg into grid cells, and by handling moving objects via conflict analysis. The system was trained and evaluated on real-life data. A python implementation maintains a 10 Hz framerate. Since road labels were needed for training, a soft labelling procedure, relying lane-level HD maps, was used to generate coarse training and validation sets. An additional test set was manually labelled for evaluation purposes. So as to reach satisfactory results, the system fuses road detection results obtained from three variants of RoadSeg, processing different LIDAR features., Comment: 39 pages

Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a much higher computational burden. A lot of work has been done to reduce the time complexity of information fusion with Dempster's rule, which is a pointwise multiplication of two zeta transforms, and optimal general algorithms have been found to get the complete definition of these transforms. Yet, it is shown in this paper that the zeta transform and its inverse, the M��bius transform, can be exactly simplified, fitting the quantity of information contained in belief functions. Beyond that, this simplification actually works for any function on any partially ordered set. It relies on a new notion that we call focal point and that constitutes the smallest domain on which both the zeta and M��bius transforms can be defined. We demonstrate the interest of these general results for DST, not only for the reduction in complexity of most transformations between belief representations and their fusion, but also for theoretical purposes. Indeed, we provide a new generalization of the conjunctive decomposition of evidence and formulas uncovering how each decomposition weight is tied to the corresponding mass function., Accepted for publication in Elsevier Information Sciences Journal

Publication in the conference proceedings of EUSIPCO, Tampere, Finland, 2000

This paper presents an approach that incorporates Canonical Correlation Analysis (CCA) for monocular 3D face pose and facial animation estimation. The CCA is used to find the dependency between texture residuals and 3D face pose and facial gesture. The texture residuals are obtained from observed raw brightness shape-free 2D image patches that we build by means of a parameterized 3D geometric face model. This method is used to correctly estimate the pose of the face and the model's animation parameters controlling the lip, eyebrow and eye movements (encoded in 15 parameters). Extensive experiments on tracking faces in long real video sequences show the effectiveness of the proposed method and the value of using CCA in the tracking context.

In this paper we present two watermarking approaches that are robust to geometric distortions. The first approach is based on image normalization, in which both watermark embedding and extraction are carried out with respect to an image normalized to meet predefined moment criteria. We propose a new normalization procedure, which is invariant to affine transform attacks. The resulting watermarking scheme is suitable for public watermarking applications, where the original image is not available for watermark extraction. The second approach is based on a watermark resynchronization scheme aimed to alleviate the effects of random bending attacks. In this scheme, a deformable mesh is used to correct the distortion caused by the attack. The watermark is then extracted from the corrected image. In contrast to the first scheme, the latter is suitable for private watermarking applications, where the original image is needed during watermark detection. In both schemes we employ a direct-sequence code division multiple access (DS-CDMA) approach to embed a multi-bit watermark in the discrete cosine transform (DCT) domain of the image. Numerical experiments demonstrate that the proposed watermarking schemes are robust to a wide range of geometric attacks.

Facial expression interpretation, recognition and analysis is a key issue in visual communication and man to machine interaction. In this paper, we present a technique for extracting appearance parameters from a natural image or video sequence, which allow reproduction of natural looking expressive synthetic faces. This technique was used to perform face synthesis and tracking in video sequences as well as facial expression recognition and control.

International audience