Multigrid block matching motion estimation for generic video coding

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Many reliable distributed systems are consensus-based and typically operate under two modes: a fast normal mode in failure-free periods, and a slower recovery mode following failures. A lot of work has been devoted to optimizing the normal mode, but little has been devoted to optimizing the recovery mode. This work seeks to understand whether the recovery mode is inherently slower than the normal mode. We focus on consensus algorithms that tolerate arbitrary periods of asynchrony as well as the crash of a m

{W}e compare four local feature extraction techniques for the task of face verification, namely (ordered in terms of complexity): raw pixels, raw pixels with mean removal, 2D Discrete Cosine Transform (DCT) and local Principal Component Analysis (PCA). The comparison is performed in terms of discrimination ability and robustness to illumination changes. We also evaluate the effectiveness of several approaches to modifying standard feature extraction methods in order to increase performance and robustness to

When devising a distributed agreement algorithm, it is common to minimize the time complexity of global decisions, which is typically measured as the number of communication rounds needed for all correct processes to decide. In practice, what we might want to minimize is the time complexity of local decisions, which we define as the number of communication rounds needed for at least one correct process to decide. The motivation of this paper is to figure out whether there is any difference between local and

Particle filtering is now established as one of the most popular method for visual tracking. Within this framework, two assumptions are generally made. The first is that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the data are correlated, and that modeling such dependency should improve tracking robustness. The second assumption consists of the use of the transition prior as proposal distribution. Thus, the current observation data is not

The aim of this project is to find a motion estimation method that works in combination with block matching in order to reduce the visible artifacts. The proposed solution tries to extract the real motions taking place in a sequence. The developed algorithm is a region based motion estimator. We associate to regions of general shape motion parameters which describe an ane transformation on the plane. The traditional block matching is then used for smaller transformations. The developed algorithm is iterativ

We present a new method for estimating heart motion from two-dimensional (2D) echocardiographic sequences. It is inspired by the Lucas-Kanade algorithm for optical flow which estimates motion parameters over a sliding window. However, instead of assuming that the motion is constant within the analysis window, we consider a model that is locally affine and can account for typical heart motions such as dilation/contraction and shear. Another refinement is spatial adaptivity which is achieved by estimating dis

Interactive systems, games, VR and multimedia systems require more and more flexible Virtual Humans with individualities. Behavioral animation seems to be the best way to develop this kind of applications, but there is still a major problem to select the right laws and rules to implement individual but believable behaviors. To create motion laws, there are mainly two approaches: 1) Recording the motion using motion capture systems, then to try to alternate such a motion to create this individuality. This pr

We present an explicit formula for B-spline convolution kernels; these are defined as the convolution of several B-splines of variable widths hi and degrees ni. We apply our results to derive spline-convolution-based algorithms for two closely related problems: the computation of the Radon transform and of its inverse. First, we present an efficient discrete implementation of the Radon transform that is optimal in the least-squares sense. We then consider the reverse problem and introduce a new spline-convo

Multiple description (MD) coding is a source coding technique for information transmission over unreliable networks. In MD coding, the coder generates several different descriptions of the same signal and the decoder can produce a useful reconstruction of the source with any received subset of these descriptions. In this paper, we study the problem of MD coding of stationary Gaussian sources with memory. First, we compute an approximate MD rate distortion region for these sources, which we prove to be asymp

Robot learning by imitation makes an increasing body of robotics research. Imitation learning complements motor learning techniques by restricting the search space to a computationally tractable subset. Imitation learning search for spatial and temporal invariants across several demonstrations. These invariants are task- dependent. This talk will present an algorithm that determines the key features of an imitation task through a comparative analysis of the data in joint space, carthesian space and visual s

The purpose of this paper is to develop methods that can reconstruct a bandlimited discrete-time signal from an irreg- ular set of samples at unknown locations. We define a solution to the problem using first a geometric and then an algebraic point of view. We find the locations of the irregular set of samples by treating the problem as a combinatorial optimization problem. We employ an exhaustive method and two descent methods: the random search and cyclic coordinate methods. The numerical simulations were

Informally, an indulgent algorithm is a distributed algorithm that tolerates unreliable failure detection: the algorithm is indulgent towards its failure detector. This paper formally characterizes such algorithms and states some of their interesting features. We show that indulgent algorithms are inherently safe and uniform. We also state impossibility results for indulgent solutions to divergent problems like consensus, and failure-sensitive problems like non-blocking atomic commit and terminating reliabl

Multigrid block matching motion estimation for generic video coding

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The Overhead of Consensus Failure Recovery

On Local Features for Face Verification

Early Local Decisions in Distributed Agreement

Embedding Motion in Model-Based Stochastic Tracking

Dynamic Region and Block-Based Motion Estimation for Video Compression

Motion Analysis of Echocardiograms Using a Local-Affine, Spatio-Temporal Model

Behavioural animation of virtual humans: what kind of laws and rules ?

Discretization of the radon transform and of its inverse by spline convolutions

Optimal Filter Banks for Multiple Description Coding: Analysis and Synthesis

Computational elements of robot learning by imitation

Reconstruction of irregularly sampled discrete-time bandlimited signals with unknown sampling locations

Indulgent Algorithms