Publications de Pascal Frossard

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Multiple description coding offers an elegant and competitive solution for data transmission over lossy packetbased networks, with a graceful degradation in quality as losses increase. On the other hand, coding techniques based on redundant transforms give a very promising alternative for the generation of multiple descriptions, mainly due to redundancy inherently given by a transform itself, that offers intrinsic resiliency to losses. In this paper, we show how the partitioning of a generic redundant dicti

We consider an interactive multiview video streaming (IMVS) system where clients select their preferred viewpoint in a given navigation window. To provide high quality IMVS, many high quality views should be transmitted to the clients. However, this is not ...

We propose a method to compute scale invariant features in omnidirectional images. We present a formulation based on Riemannian geometry for the definition of differential operators on non-Euclidian manifolds that correspond to the particular form of mirro ...

Deep convolutional neural networks have been shown to be vulnerable to arbitrary geometric transformations. However, there is no systematic method to measure the invariance properties of deep networks to such transformations. We propose ManiFool as a simpl ...

Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the development of graph networks, this setup has become a de facto benchmark for a sign ...

Manifold models provide low-dimensional representations that are useful for processing and analyzing data in a transformation-invariant way. In this paper, we study the problem of learning smooth pattern transformation manifolds from image sets that are ob ...

Instead of lossily coding depth images resulting in undesirable geometric distortion, graph-based representation (GBR) describes disparity information as a graph with a controllable accuracy. In this paper, we propose a more compact graphical representatio ...

This paper proposes a multiresolution Matching Pursuit decomposition of natural images. Matching Pursuit is a greedy algorithm that decomposes any signal into a linear expansion of waveforms taken from a redundant dictionary, by iteratively picking the waveform that best matches the input signal. Since the computational cost rapidly grows with the size of the signal, we propose a multiresolution strategy that, together with a dictionary training, significantly reduces the encoding complexity while still pro

This paper addresses the problem of 3D face recognition using simultaneous sparse approximations on the sphere. The 3D face point clouds are first aligned with a novel and fully automated registration process. They are then represented as signals on the 2D ...

We propose a new approach for describing the geometry information of multiview image representations. Rather than transmitting the raw geometry of the scene, under the form of depth information, we build a graph that represents the connections between corr ...

This paper proposes a joint reconstruction algorithm for compressed correlated images that are given under the form of linear measurements. We consider the particular problem where one image is selected as the reference image and it is used as the side inf ...

The spatial arrangement of cells plays a critical role in determining their functions and interactions within tissues. However, single-cell RNA sequencing dissociates cells from their native tissue context, resulting in a loss of spatial information. Here, we show that complex tissue structures can be reassembled from the gene expression profiles of dissociated cells. To achieve this, we developed LUNA, a generative AI model that reconstructs tissues conditioned solely on the gene expression of cells by lea

This paper proposes a multiresolution Matching Pursuit decomposition of natural images. Matching Pursuit is a greedy algorithm that decomposes any signal into a linear expansion of waveforms taken from a redundant dictionary, by iteratively picking the waveform that best matches the input signal. Since the computational cost rapidly grows with the size of the signal, we propose a multiresolution strategy that, together with an efficient dictionary, significantly reduces the encoding complexity while still p

Relationships between entities in datasets are often of multiple types, which can naturally be modeled by a multi-layer graph; a common vertex set represents the entities and the edges on different layers capture different types of relationships between th ...

We elaborate on a scheme that combines batch patching at an origin server and prefix/interval caching at an edge server receiving the clients' requests. We derive a cost function that factors in the aggregate backbone rate, the cache occupancy and the disk bandwidth utilization. We define the optimal batched patch caching strategy as a function of the client request rate. Finally, we show how various strategies including full caching, no caching and pure prefix caching with no patching are optimal derivatio

This paper introduces a sparse signal representation algorithm in redundant dictionaries, called the M-Term Pursuit (MTP), with an application to image representation and scalable coding. The MTP algorithm belongs to the framework of the matching pursuit (MP); it expands the image into a linear combination of atoms, selected from a large collection of spatial atoms. The MTP relies on the concept of dictionary partitioning, i.e., as splitting the dictionary into

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disjoint sub-dictionaries, each carrying s

This paper presents a novel algorithm for computing the relative motion between images from compressed linear measurements. We propose a geometry based correlation model that describes the relative motion between images by translational motion of visual f ...

In this paper, we address the design of any number of balanced multiple descriptions using the multiple description scalar quantization(MDSQ) technique. The proposed scheme has the advantages of low complexity, the possibility of being extended easily to any number of descriptions and the possibility to trade off between the side, partial and central distortions. Unlike existing schemes, it can produce balanced descriptions at low rates, at the price however of a slightly higher distortion. The behavior of

Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference. While optimizations have been proposed for inference latency, memory footprint, and energy consumption, prior hardware-aware neural architecture ...

Invasive Coronary Angiography (ICA) is the clinical gold standard for the assessment of coronary artery disease. However, its interpretation remains subjective and prone to intra- and inter-operator variability. In this work, we introduce ODySSeI: an Open-source end-to-end framework for automated Detection, Segmentation, and Severity estimation of lesions in ICA images. ODySSeI integrates deep learning-based lesion detection and lesion segmentation models trained using a novel Pyramidal Augmentation Scheme

Pascal Frossard

Chattez avec Graph Search

Multiple description coding with redundant expansions and application to image communication

Optimal Layered Representation for Adaptive Interactive Multiview Video Streaming

OmniSIFT: Scale Invariant Features in Omnidirectional Images

Geometric robustness of deep networks: analysis and improvement

On The Choice of Graph Neural Network Architectures

Learning Smooth Pattern Transformation Manifolds

Graph-Based Representation and Coding of Multiview Images

Evolutionary Multiresolution Matching Pursuit and its relations with the Human Visual System

3D Face Recognition with Sparse Spherical Representations

Graph-based representation and coding of multiview geometry

Image Reconstruction from Compressed Linear Measurements with Side Information

Tissue Reassembly with Generative AI

Evolutionary Multiresolution Matching Pursuit and its Relations with the Human Visual System

Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds

Batched Patch Caching for Streaming Media

The M-Term Pursuit for Image Representation and Progressive Compression

Motion Estimation from Compressed Linear Measurements

Index Assignment for N Balanced Multiple Description Scalar Quantization

U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search

ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images