Tracking Articulated Bodies using Generalized Expectation Maximization

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

Bayesian Optimization (BO) is typically used to optimize an unknown function f that is noisy and costly to evaluate, by exploiting an acquisition function that must be maximized at each optimization step. Even if provably asymptotically optimal BO algorith ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

In this work, we analyze space-time reduced basis methods for the efficient numerical simulation of haemodynamics in arteries. The classical formulation of the reduced basis (RB) method features dimensionality reduction in space, while finite difference sc ...

We consider the problem of comparing several samples of stochastic processes with respect to their second-order structure, and describing the main modes of variation in this second order structure, if present. These tasks can be seen as an Analysis of Vari ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

For a high-dimensional problem, a randomized Gram-Schmidt (RGS) algorithm is beneficial in terms of both computational cost and numerical stability. We apply this dimension reduction technique by random sketching to Krylov subspace methods, e.g., to the ge ...

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

In recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with th ...

The time-resolved visualization of the dynamics of a cavitation bubble usually requires the use of expensive high-speed cameras, which often provide a limited spatial resolution. In the present study, we propose an alternative to these high-speed imaging t ...

This toolkit has everything you need to perform imaging with a lensless camera. We make use of a low-cost implementation of DiffuserCam, namely replacing the lens with a piece of tape and using the Raspberry Pi HQ camera sensor. Similar principles and meth ...

Reinterpretable cameras are defined by their post-processing capabilities that exceed traditional imaging. We present "SoDaCam" that provides reinterpretable cameras at the granularity of photons, from photon-cubes acquired by single-photon devices. Photon ...

Tensor trains are a versatile tool to compress and work with high-dimensional data and functions. In this work we introduce the streaming tensor train approximation (STTA), a new class of algorithms for approximating a given tensor ' in the tensor train fo ...

Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems. Recent works mostly focus on learning a deep feature encoder by minimizing the discrepa ...

Optical microscopy is an essential tool for biologists, who are often faced with the need to overcome the spatial and temporal resolution limitations of their devices to capture finer details. As upgrading imaging hardware is expensive, computational metho ...

To obtain a more complete understanding of material microstructure at the nanoscale and to gain profound insights into their properties, there is a growing need for more efficient and precise methods that can streamline the process of 3D imaging using a tr ...

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantitie ...

In Bourlard and Kamp (Biol Cybern 59(4):291-294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called "auto-associative multilayer perceptrons") were, in the best case, implementing singular value decomposit ...

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. Our method learns the features necessary for an effective low-dimensi ...

Tracking Articulated Bodies using Generalized Expectation Maximization

Chat with Graph Search

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization

Aggregating Spatial and Photometric Context for Photometric Stereo

Towards Trustworthy Deep Learning for Image Reconstruction

SPACE-TIME REDUCED BASIS METHODS FOR PARAMETRIZED UNSTEADY STOKES EQUATIONS

Transportation-based functional ANOVA and PCA for covariance operators

Spectral Estimators for High-Dimensional Matrix Inference

Randomized flexible GMRES with deflated restarting

Scalable and Privacy-Preserving Federated Principal Component Analysis

Towards learning-based denoising of light fields

Ex uno plures: how to construct high-speed movies of collapsing cavitation bubbles from a single image

LenslessPiCam: A Hardware and Software Platform for Lensless Computational Imaging with a Raspberry Pi

SoDaCam: Software-defined Cameras via Single-Photon Imaging

STREAMING TENSOR TRAIN APPROXIMATION

DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

Practical computational imaging by use of spatiotemporal light modulation: from simulations to applications in biological microscopy

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

Autoencoders reloaded

A machine learning approach to portfolio pricing and risk management for high-dimensional problems