Faten Chaieb

Shape approximation for efficient progressive mesh compression

This paper introduces an original multi-resolution 3D mesh compression technique, called Shape Approximation-based Progressive Mesh (SAPM). The proposed approach losslessly compresses the mesh connectivity and exploits it in order to build a smooth approximation of the original mesh. The obtained mesh approximation is then decimated yielding a progressive mesh hierarchy. This hierarchy is used to efficiently predict and progressively transmit the geometry approximation errors. The proposed codec supports both spatial and quality scalabilities and offers high rate-distortion performances. Experimental evaluation shows that the SAPM codec is on average 38–57% more efficient than the state-of-the-art connectivity preserving 3D mesh compression techniques.

Accelerated liver tumor segmentation in four-phase computed tomography images

Segmentation and volume measurement of liver tumor are important tasks for surgical planning and cancer follow-up. In this work, a segmentation method from four-phase computed tomography images is proposed. It is based on the combination of the Expectation-Maximization algorithm and the Hidden Markov Random Fields. The latter considers the spatial information given by voxel neighbors of two contrast phases. The segmentation algorithm is applied on a volume of interest that decreases the number of processed voxels. To accelerate the classification steps within the segmentation process, a Bootstrap resampling scheme is also adopted. It consists in selecting randomly an optimal representative set of voxels. The experimental results carried out on three clinical datasets show the performance of our liver tumor segmentation method. It has been notably observed that the computing time of the classification algorithm is reduced without any significant impact on the segmentation accuracy.

Piece-wise linear function estimation for platelet-based depth maps coding using edge detection

Many researches on efficient depth maps coding issues have been carried out giving particular attention to sharp edge preservation. Platelet-based coding method is an edge-aware coding scheme that uses a segmentation procedure based on recursive quadtree decomposition. Then, the depth map is modeled using piecewise linear platelet and wedgelet functions. However, the estimation of these functions is a computationally expensive task making the platelet-based techniques not adapted to online applications. In this paper, we propose to exploit edge detection in order to reduce the encoding delay of the platelet/wedgelet estimation process. The proposed approach shows significant gain in terms of encoding delay, while providing competitive R-D performances w.r.t. the original platelet-based codec. The subjective evaluation shows significant less degradation along sharp edges.

Progressive edge-preserving depth maps coding based on sparse representation

Multiview Video plus Depth coding has received much attention in recent years because of its importance in many different fields, ranging from video games to medical imaging. An efficient coding, that causes the least possible distortion without excessive rate and complexity increase, is crucial particularly for depth maps. In this paper, we propose a coding method that deals with progressive depth maps rendering. Depth images are approximated by a compact representation of sparse coefficients and discrete cosine/B-spline dictionary atoms. To enhance tradeoff between sparsity gain and sharp edges preservation, proposed coding is parameterized using high and low sparsity criteria. Then, selected atoms are structured in layers such that they can be progressively communicated to the decoder side according to consumers capabilities, target applications requirements and transmission channel capacities. Experimental results show the perfomance of the proposed method as compared to some state-of-the-art algorithms.

A Multiscale Circum-ellipse Area Representation for Planar Shape Retrieval.

In this paper, we propose a new Multiscale Circum-ellipse Area Representation (MCAR) for planar contours. The proposed representation deals with a multiscale shape signature defined from the local area delimited by the circumscribed ellipse of the triangle formed by three contour points and the contour. This shape signature describes, at each scale level, the concavity/convexity at each contour point. Then, Fourier descriptors are obtained by applying Fourier transform to the proposed multiscale signature. Thus, the proposed MCAR based Fourier Descriptors handle the local and global shape characteristics. Furthermore, it is invariant to affine transformation and robust to local deformations. The performance of our proposed method was evaluated through the precision recall and bull’s-eye tests on the two well-known databases (MCD and MPEG7-setB). Obtained results indicate that our method outperforms the shape signatures based Fourier descriptor proposed in the literature.

Representations, Analysis and Recognition of Shape and Motion from Imaging Data

In this paper, we present a novel approach to compute 3D canonical forms which is useful for non-rigid 3D shape retrieval. We resort to using the feature space to get a compact representation of points in a small-dimensional Euclidean space. Our aim is to improve the classical Multi-Dimensional Scaling MDS algorithm to avoid the super-quadratic computational complexity. To this end, we compute the canonical form of the local geodesic distance matrix between pairs of a small subset of vertices in local feature patches. To preserve local shape details, we drive the mesh deformation by the local weighted commute time. When used as a spatial relationship between local features, the invariant properties of the Biharmonic distance improve the final results. We evaluate the performance of our method by using two different measures: the compactness measure and the Haussdorf distance.

A New Multi-resolution Affine Invariant Planar Contour Descriptor

In this paper, a novel affine invariant shape descriptor for planar contours is proposed. It is based on a multi-resolution representation of the contour. For each contour resolution, a shape signature is defined from the contour points and the initial contour centroid and points. Finally, Fourier descriptors are computed for each signature. The proposed descriptor is invariant to affine transformations. Experiments carried on the MPEG-7 coutour database and the Multiview Curve Dataset (MCD) show that our proposed descriptor outperforms other contour shape descriptors proposed in the literature.

Tuned depth signal analysis on merged transform domain for view synthesis in free viewpoint systems

Completely embedded in the 3D era, depth maps coding becomes a must in order to favour 3D admission to different fields of application, ranging from video games to medical imaging. This study presents a novel depth coding approach that, after a decimation step favouring the foreground, decomposes depth maps onto a set of sparse coefficients and redundant mixed discrete cosine and B-splines atoms highly correlated to depth maps piece-wise linear nature. Depth decomposition searches the best rate/distortion tradeoff through minimisation of an adaptive cost function, where its weight parameter is manipulated according to depth homogeneity. The bigger the parameter is, the more the sparsity is favoured at the expense of synthesis quality. Furthermore, handled distortion measure of the cost function quantifies the effect of depth maps coding on rendered views quality. The experiments show the relevance of the proposed method, able to obtain considerable tradeoffs between bitrate and synthesised views distortion.

Adaptive sparse representation of depth maps targeting view synthesis quality

Completely embedded in the 3D era, depth maps coding becomes a must in order to favor 3D admission to different fields of application, ranging from video games to medical imaging. This paper presents a novel depth coding approach that decomposes a decimated version of the original depth image on a sparse set of coefficients and mixed discrete cosine and B-splines atoms. The upstream decimation step reduces encoding bitrate without significant loss of virtual views quality. Depth decomposition is performed through minimization of an adaptive Rate/Distortion cost function, where we manipulate its weight parameter according to depth discontinuities. We then refine the choice of distortion metric in order to quantify the effect of depth maps coding on rendered views quality. Experiments show the relevance of the proposed method, able to obtain considerable tradeoffs between bitrate and synthesized views distortion.

Tuned sparse depth map coding using redundant predefined transform domain

Multiview video plus depth is the most popular 3D video representation that would support novel applications including free viewpoint television. These applications highly depend on high quality rendering of interpolated views which, as well, highly depends on the quality of decoded depth images. Therefore, a depth map coding that preserves perceptual quality, particularly on high frequency regions, is primary. In this paper, we propose a coding depth maps method that deals with emerging sparse signal decomposition technique. Depth images are approximated by a linear combination of few nonzero coefficients and dictionary atoms. Selected atoms are elementary signals based on a mixture of discrete cosine and B-splines of first degree. Sparse depth maps coding is tuned using a couple quality criterion such that depth discontinuities are preserved. The results investigated by objective evaluations over several depth maps imply that the proposed depth maps coding achieves better Rate-Distortion than JPEG and JPEG 2000. Subjective evaluation is also presented to stress the visual quality of interpolated views.