nils-herrmann/clean_scirepeval huggingface dataset:
Peer-to-peer videoconferencing with H.264 software codec for mobiles

A rapidly growing number of carriers offer wireless video services to their customers, taking advantage of high quality video codecs implemented in dedicated hardware of selected mobile devices. In this paper we introduce a video conferencing software, which seamlessly integrates mobile with stationary users in a provider and device independent fashion. Innovations of this work are twofold. At first we report on a mobile realization of an H.264 video codec and its performance on a standard consumer Smartphone. Operating within the tight bounds of real-time compliance on mobiles, this software is an adapted version of a highly optimized H.264 codec. This DAVC codec, which we introduce along the line, significantly out-performs compatible H.264 realizations and allows for a scalable adaptation of its frame rate. In the second part we present a barrier-resistant peer-to-peer group communication scheme, which scales well for medium-size conferences and accounts for the heterogeneous nature of mobile and stationary participants. An outlook on mobility related group communication issues and future optimizations based on structured communication layers concludes the work.
The New MPEG-4/FAMC Standard for Animated 3D Mesh Compression

This paper presents a new compression technique for 3D dynamic meshes, referred to as FAMC - Frame-based Animated Mesh Compression, recently promoted within the MPEG-4 standard as Amen-dement 2 of part 16 (AFX -Animation Framework extension). The FAMC approach combines a model-based motion-compensation strategy with transform/predictive coding of residual errors. First, a skinning motion-compensation model is automatically derived from a frame-based representation. Subsequently, either 1) DCT/lifting wavelets or 2) layer-based predictive coding is employed to exploit remaining spatio-temporal correlations in the residual signal. Both motion model parameters and residual signal components are finally encoded by using context-based adaptive binary arithmetic coding (CABAC). The proposed FAMC encoder offers high compression performance with gains of 60% in terms of bit-rate savings relative to previous MPEG-4 technology and of 20% to 40% relative to state-of-the-art techniques. FAMC is well suited for compressing both geometric and photometric (normal vectors, colors...) attributes. In addition, FAMC also supports a rich set of functionalities including streaming, scalability (spatial, temporal and quality) and progressive transmission.
Improved video compression technology and the emerging high efficiency video coding standard

Today, H.264/AVC is the state-of-the-art video coding standard. Especially after the 2004 development of its High Profile (HP), it has become one of the primary formats in high definition video content delivery. Recently, a joint Call for Proposals (CfP) on video compression technology has been issued by ISO/IEC MPEG and ITU-T VCEG, targeting at the next generation of video compression standards with substantially higher compression capability than H.264/AVC. As a response to this CfP, Fraunhofer HHI proposed a newly developed video coding scheme which achieves bit rate savings of around 30% when compared to H.264/AVC HP. This paper describes the proposed video coding scheme and discusses its innovative features.
On the convergence rates of proximal splitting algorithms

In this work, we first provide iteration-complexity bounds (pointwise and ergodic) for the inexact Krasnosel'skî-Mann iteration built from nonexpansive operators. Moreover, under an appropriate regularity assumption on the fixed point operator, local linear convergence rate is also established. These results are then applied to analyze the convergence rate of various proximal splitting methods in the literature, which includes the Forward-Backward, generalized Forward-Backward, Douglas-Rachford, ADMM and some primal-dual splitting methods. For these algorithms, we develop easily verifiable termination criteria for finding an approximate solution, which is a generalization of the termination criterion for the classical gradient descent method. We illustrate the usefulness of our results on a large class of problems in signal and image processing.
Robust Sparse Analysis Regularization

This paper investigates the theoretical guarantees of l1-analysis regularization when solving linear inverse problems. Most of previous works in the literature have mainly focused on the sparse synthesis prior where the sparsity is measured as the l1 norm of the coefficients that synthesize the signal from a given dictionary. In contrast, the more general analysis regularization minimizes the l1 norm of the correlations between the signal and the atoms in the dictionary, where these correlations define the analysis support. The corresponding variational problem encompasses several well-known regularizations such as the discrete total variation and the fused Lasso. Our main contributions consist in deriving sufficient conditions that guarantee exact or partial analysis support recovery of the true signal in presence of noise. More precisely, we give a sufficient condition to ensure that a signal is the unique solution of the l1 -analysis regularization in the noiseless case. The same condition also guarantees exact analysis support recovery and l2-robustness of the l1-analysis minimizer vis-à-vis an enough small noise in the measurements. This condition turns to be sharp for the robustness of the sign pattern. To show partial support recovery and l2 -robustness to an arbitrary bounded noise, we introduce a stronger sufficient condition. When specialized to the l1-synthesis regularization, our results recover some corresponding recovery and robustness guarantees previously known in the literature. From this perspective, our work is a generalization of these results. We finally illustrate these theoretical findings on several examples to study the robustness of the 1-D total variation, shift-invariant Haar dictionary, and fused Lasso regularizations.
Separation of traveling waves in cortical networks using optical imaging

This paper introduces a mathematical model of the spatio-temporal patterns of visually evoked activity observed using Voltage-Sensitive Dye Imaging (VSDI) of the visual cortex. The cortical activity is described using a linear superposition of waves traveling with different speeds. This model improves the quality of the wave detection and still respects the previous approaches, as it integrates several biologically plausible constraints: 1) separability of the sources in terms of cortical location; 2) separability of the waves in terms of propagation speed, and 3) additivity of the depolarizing effects of the waves. Under these assumptions, a traveling component analysis algorithm performs a full separation of the set of waves and recovers the locations of the neural sources. Both features could help to better understand the dynamics of evoked activity in cortical sensory networks.
Non-local segmentation and inpaiting

This article introduces a new variational image segmentation method that makes use of non-local comparisons between pairs of patches in the image and is robust to missing data (e.g. damaged pixels or large missing regions). The resulting segmentation is at the heart of a novel inpainting algorithm that also uses a non-local regularization. This segmentation and inpainting approach only requires a local homogeneity of the features inside and outside the region to be segmented. In contrast to existing region-based segmentation methods, it allows us to segment regions with smoothly varying intensity as well as multiple objects with different intensities. This comparison principle is also less sensitive to initialization than edge-based approaches.
Discrete bandelets with geometric orthogonal filters

This paper describes the construction of second generation bandelet orthogonal bases. The decomposition on a bandelet basis is computed using a wavelet filter bank followed by adaptive geometric orthogonal filters, that require O(N) operations. The resulting geometry is multiscale and calculated with a fast procedure that minimizes a Lagrangian cost at each scale. Image compression with the resulting bandelet transform code gives significantly better results than a wavelet transform code.
Surface segmentation using geodesic centroidal tesselation

We solve the problem of mesh partition using intrinsic computations on the 3D surface. The key concept is the notion of centroidal tesselation that is widely used in eucidan settings. Using the Fast Marching algorithm, we are able to recast this powerful tool in the language of mesh processing. This method naturally fits into a framework for 3D geometry modelling and processing that uses only fast geodesic computations. With the use of classical geodesic-based building blocks, we are able to take into account any available information or requirement such as a 2D texture or the curvature of the surface.
Wasserstein active contours

In this paper, we propose a novel and rigorous framework for region-based active contours that combines the Wasserstein distance between statistical distributions in arbitrary dimension and shape derivative tools. To speed-up the computation and be able to handle high-dimensional features and large-scale data, we introduce an approximation of the differential of the Wasserstein distance between histograms. The framework is flexible enough to allow either minimization of the Wasserstein distance to prior distributions, or maximization of the distance between the distributions of the regions to be segmented (i.e. region competition). Numerical results reported demonstrate the advantages of the proposed optimal transport distance with respect to point-wise metrics.
Geodesic Computation for Adaptive Remeshing

This video presents an application of geodesic computation on 3D meshes to surface remeshing. The connectivity of the resulting mesh is computed using a geodesic Delaunay triangulation of the sampling points. The user can provide a speed function to conform the remeshing to various contraints such as curvature variation or texture gradient. This remeshing method is fast thanks to the use of the fast marching algorithm. It is simple to implement, robust and can serve as a basis building block for further processing of the surface such as segmentation or flattening.
Landmark-Based Geodesic Computation for Heuristically Driven Path Planning

This paper presents a new method to quickly extract geodesic paths on images and 3D meshes. We use a heuristic to drive the front propagation procedure of the classical Fast Marching. This results in a modification of the Fast Marching algorithm that is similar to the A algorithm used in artificial intelligence. In order to find very quickly geodesic paths between any given couples of points, we advocate for the initial computation of distance maps to a set of landmark points and make use of these distance maps through a relevant heuristic. We show that our method brings a large speed up for large scale applications that require the extraction of geodesics on images and 3D meshes. We introduce two distortion metrics in order to find an optimal seeding of landmark points for the targeted applications. We also propose a compression scheme to reduce the memory requirement without impacting the quality of the extracted paths.
On growth and formlets: Sparse multi-scale coding of planar shape

This paper presents a sparse representation of 2D planar shape through the composition of warping functions, termed formlets, localized in scale and space. Each formlet subjects the 2D space in which the shape is embedded to a localized isotropic radial deformation. By constraining these localized warping transformations to be diffeomorphisms, the topology of shape is preserved, and the set of simple closed curves is closed under any sequence of these warpings. A generative model based on a composition of formlets applied to an embryonic shape, e.g., an ellipse, has the advantage of synthesizing only those shapes that could correspond to the boundaries of physical objects. To compute the set of formlets that represent a given boundary, we demonstrate a greedy coarse-to-fine formlet pursuit algorithm that serves as a non-commutative generalization of matching pursuit for sparse approximations. We evaluate our method by pursuing partially occluded shapes, comparing performance against a contour-based sparse shape coding framework.
Model Consistency of Partly Smooth Regularizers

This paper studies least-square regression penalized with partly smooth convex regularizers. This class of penalty functions is very large and versatile, and allows to promote solutions conforming to some notion of low complexity. Indeed, such penalties/regularizers force the corresponding solutions to belong to a low-dimensional manifold (the so-called model), which remains stable when the argument of the penalty function undergoes small perturbations. Such a good sensitivity property is crucial to make the underlying low-complexity (manifold) model robust to small noise. In a deterministic setting, we show that a generalized “irrepresentable condition” implies stable model selection under small noise perturbations in the observations and the design matrix, when the regularization parameter is tuned proportionally to the noise level. As an algorithmic implication, we also prove that this condition is almost necessary for stable model recovery. We then turn to the random setting, where the design matrix and the noise are random, and the number of observations grows large. We show that under our generalized “irrepresentable condition,” and a proper scaling of the regularization parameter, the regularized estimator is model consistent. In plain words, with a probability tending to one as the number of measurements tends to infinity, the regularized estimator belongs to the correct low-dimensional model manifold. This paper unifies and generalizes a large body of literature, where model consistency was known to hold, for instance for the Lasso, group Lasso, total variation (fused Lasso), and nuclear/trace norm regularizers. As an algorithmic implication, we show that under the deterministic model selection conditions, the forward–backward proximal splitting algorithm used to solve the penalized least-square regression problem is guaranteed to identify the model manifold after a finite number of iterations. Finally, we detail how our results extend from the quadratic loss to an arbitrary smooth and strictly convex loss function. We illustrate the usefulness of our results on the problem of low-rank matrix recovery from random measurements using nuclear norm minimization.
Texture Synthesis with Grouplets

This paper proposes a new method to synthesize and inpaint geometric textures. The texture model is composed of a geometric layer that drives the computation of a new grouplet transform. The geometry is an orientation flow that follows the patterns of the texture to analyze or synthesize. The grouplet transform extends the original construction of Mallat and is adapted to the modeling of natural textures. Each grouplet atoms is an elongated stroke located along the geometric flow. These atoms exhibit a wide range of lengths and widths, which is important to match the variety of structures present in natural images. Statistical modeling and sparsity optimization over these grouplet coefficients enable the synthesis of texture patterns along the flow. This paper explores texture inpainting and texture synthesis, which both require the joint optimization of the geometric flow and the grouplet coefficients.
Compact representations of stationary dynamic textures

This paper addresses the problem of modeling stationary color dynamic textures with Gaussian processes. We detail two particular classes of such processes that are parameterized by a small number of compactly supported linear filters, so-called dynamical textons (dynTextons). The first class extends previous works on the spot noise texture model to the dynamical setting. It directly estimates the dynTexton to fit a translation-invariant covariance from the exemplar. The second class is a specialization of the auto-regressive (AR) dynamic texture method to the setting of space and time stationary textures. This allows one to parameterize the process covariance using only a few linear filters. Numerical experiments on a database of stationary textures shows that the methods, despite their extreme simplicity, provide state of the art results to synthesize space stationary dynamical texture.
Best basis compressed sensing

This paper proposes a best basis extension of compressed sensing recovery. Instead of regularizing the compressed sensing inverse problem with a sparsity prior in a fixed basis, our framework makes use of sparsity in a tree-structured dictionary of orthogonal bases. A new iterative thresholding algorithm performs both the recovery of the signal and the estimation of the best basis. The resulting reconstruction from compressive measurements optimizes the basis to the structure of the sensed signal. Adaptivity is crucial to capture the regularity of complex natural signals. Numerical experiments on sounds and geometrical images indeed show that this best basis search improves the recovery with respect to fixed sparsity priors.
Sparse reconstruction from a limited projection number of the coronary artery tree in X-ray rotational imaging

This paper deals with the 3D reconstruction of sparse data in X-ray rotational imaging. Due to the cardiac motion, the number of available projections for this reconstruction is equal to four, which leads to a strongly under-sampled reconstruction problem. We address thus this illness problem through a regularized iterative method. The whole algorithm is divided into two steps. Firstly, a minimal path segmentation step extracts artery tree boundaries. Secondly, a MAP reconstruction comparing L0-norm and L1-norm priors is applied on this extracted coronary tree. The reconstruction optimization process relies on a separable paraboloidal (SPS) algorithm. Some preliminary results are provided on simulated rotational angiograms.
3D shape matching by geodesic eccentricity

This paper makes use of the continuous eccentricity transform to perform 3D shape matching. The eccentricity transform has already been proved useful in a discrete graph-theoretic setting and has been applied to 2D shape matching. We show how these ideas extend to higher dimensions. The eccentricity transform is used to compute descriptors for 3D shapes. These descriptors are defined as histograms of the eccentricity transform and are naturally invariant to Euclidean motion and articulation. They show promising results for shape discrimination.
Total Variation Projection With First Order Schemes

This article proposes a new algorithm to compute the projection on the set of images whose total variation is bounded by a constant. The projection is computed through a dual formulation that is solved by first order non-smooth optimization methods. This yields an iterative algorithm that applies iterative soft thresholding to the dual vector field, and for which we establish convergence rate on the primal iterates. This projection algorithm can then be used as a building block in a variety of applications such as solving inverse problems under a total variation constraint, or for texture synthesis. Numerical results are reported to illustrate the usefulness and potential applicability of our TV projection algorithm on various examples including denoising, texture synthesis, inpainting, deconvolution and tomography problems. We also show that our projection algorithm competes favorably with state-of-the-art TV projection methods in terms of convergence speed.
Extraction of tubular structures over an orientation domain

This paper presents a new method to extract tubular structures from bi-dimensional images. The core of the proposed algorithm is the computation of geodesic curves over a four-dimensional space that includes local orientation and scale. These shortest paths follow closely the centerline of tubular structures, provide an estimation of the radius and can deal robustly with crossings over the image plane. Numerical experiments on a database of synthetic and natural images show the superiority of the proposed approach with respect to several method based on shortest paths extractions.
Total variation projection with first order schemes

This paper proposes a new class of algorithms to compute the projection onto the set of images with a total variation bounded by a constant. The projection is computed on a dual formulation of the problem that is minimized using either a one-step gradient descent method or a multi-step Nesterov scheme. This yields iterative algorithms that compute soft thresholding of the dual vector fields. We show the convergence of the method with a convergence rate of O(1/k) for the one step method and O(1/k2) for the multi-step one, where k is the iteration number. The projection algorithm can be used as a building block in several applications, and we illusrtate it by solving linear inverse problems under total variation constraint. Numerical results show that our algorithm competes favorably with state-of-the-art TV projection methods to solve denoising, inpainting and deblurring problems.
Wasserstein regularization of imaging problem

This paper introduces a novel and generic framework embedding statistical constraints for variational problems. We resort to the theory of Monge-Kantorovich optimal mass transport to define penalty terms depending on statistics from images. To cope with the computation time issue of the corresponding Wasserstein distances involved in this approach, we propose an approximate variational formulation for statistics represented as point clouds. We illustrate this framework on the problem of regularized color specification. This is achieved by combining the proposed approximate Wasserstein constraint on color statistics with a generic geometric-based regularization term in a unified variational minimization problem. We believe that this methodology may lead to some other interesting applications in image processing, such as medical imaging modification, texture synthesis, etc.
Best basis denoising with non-stationary wavelet packets

This article introduces a best basis search algorithm in a non-stationary (NS) wavelet packets dictionary. It computes an optimized labeled quad-tree that indexes the filters used for the NS wavelet packets decomposition. This algorithm extends the classical best basis search by exploring in a hierarchical manner all possible NS wavelet packets coefficients. The scale-by-scale variation of the filters allows to adapt the transform to the frequency content of complex textures. The resulting denoising method is made translation invariant by cycle spinning. Numerical results show that NS wavelet packets give better results than wavelet packets and wave-atoms for the denoising of natural images, in particular in textured areas. Moreover, the cycle spinning method increases significantly the denoising abilities of our algorithm.
Challenging restricted isometry constants with greedy pursuit

This paper proposes greedy numerical schemes to compute lower bounds of the restricted isometry constants that are central in compressed sensing theory. Matrices with small restricted isometry constants enable stable recovery from a small set of random linear measurements. We challenge this compressed sampling recovery using greedy pursuit algorithms that detect ill-conditioned sub-matrices. It turns out that these sub-matrices have large isometry constants and hinder the performance of compressed sensing recovery.