Pushbroom cameras are widely used for earth observation applications. This sensor acquires 1D images over time and uses the straight motion of the satellite to sweep out a region of space and build a 2D image. The stability of the satellite is critical during the pushbroom acquisition process. Therefore its attitude is assumed to be constant overtime. However, the recent manufacture of smaller and lighter satellites to reduce launching cost has weakened this assumption. Small oscillations of the satellite's attitude can result in noticeable warps in images, and geolocation information is lost as the satellite does not capture what it ought to. Current solutions use inertial sensors to control the attitude and correct the images, but they are costly and of limited precision. As the warped images do contain information about attitude variations, we suggest using image registration to estimate them. We exploit the geometry of the focal plane and the stationary nature of the disturbances to recover undistorted images. We embed the estimation in a Bayesian framework where image registration, a prior on attitude variations and a radiometric correction model are fused to retrieve the motion of the satellite. We illustrate the performance of our algorithm on four satellite datasets.

The focus of this work is calibration of the land-use module of an integrated land-use and transportation model (ILUTM). The calibration task involves estimating key parameters that dictate the output of the land-use module. Hence, an algorithm based on maximum-likelihood estimation (MLE) is developed for calibration. Furthermore, the observed values of the outputs from the land-use module are assumed to admit a Gaussian error. The ILUTM methodology used here is TRANUS which is used to model the city of Grenoble in France. The aforementioned algorithm is then applied to calibrate the land-use module of the Grenoble model. The covariance of the Gaussian error term is assumed to be unknown. It is represented as a function of the land-use module inputs and "hyperparameters.â' The resulting MLE optimization problem has 111 parameters to be estimated, 90 of which are land use parameters and 21 are hyperparameters of the Gaussian covariance kernel. The performance of the proposed calibration methodology is then compared to the traditional calibration techniques used for land use and transportation models, when applied to the Grenoble land-use model. It is observed that the proposed method outperforms the traditional technique when compared based upon a given quantity of interest.

Linear pushbroom cameras are widely used in passive remote sensing from space as they provide high resolution images. In earth observation applications, where several pushbroom sensors are mounted in a single focal plane, small dynamic disturbances of the satellite's orientation lead to noticeable geometrical distortions in the images. In this paper, we present a global method to estimate those disturbances, which are effectively vibrations. We exploit the geometry of the focal plane and the stationary nature of the disturbances to recover undistorted images. To do so, we embed the estimation process in a Bayesian framework. An autoregressive model is used as a prior on the vibrations. The problem can be seen as a global image registration task where multiple pushbroom images are registered to the same coordinate system, the registration parameters being the vibration coefficients. An alternating maximisation procedure is designed to obtain Maximum a Posteriori estimates (MAP) of the vibrations as well as of the autoregressive model coefficients. We illustrate the performance of our algorithm on various datasets of satellite imagery1.

In this paper, we propose an algorithm that recovers binocular disparities in accordance with the surface properties of the scene under consideration. To do so, we estimate the disparity as well as the normals in the disparity space, by setting the two tasks in a unified framework. A novel joint probabilistic model is defined through two random fields to favor both intra field (within neighboring disparities and neighboring normals) and inter field (between disparities and normals) consistency. Geometric contextual information is introduced in the models for both normals and disparities, which is optimized using an appropriate alternating maximization procedure. We illustrate the performance of our approach on synthetic and real data.

Satellite image registration has been investigated for several years. Nevertheless, little attention has been paid to the linear geometry of the satellite's imaging sensor, often consisting of several pushbroom cameras. Each pushbroom camera captures 1-D image and uses straight motion of the satellite to build a 2-D image. Yet, attitude variations of the satellite during the aquisition process can lead to significant distortions in the 2-D image. In this paper, we expose the problem and present a constrained image registration method to estimate the satellite's attitude variations, and thus correct the distorted images. We use a Lucas Kanade framework and a piecewise polynomial model under constraints to deduce the registration equation. The performances of our algorithm are shown on two satellite datasets.

Pushbroom sensors are widely used for earth observation from space; this camera captures 1-D image and uses straight motion of the satellite to build a 2-D image. Yet, attitude variations of the satellite during the aquisition process can lead to significant distortions in the 2-D image. Current solutions use inertial sensors to record attitude variations and correct images, but they are expensive and not accurate enough for high frequency variations. In this paper, we expose the problem and present an image registration method to estimate the satellite's attitude variations, and thus correct the distorted images. We use a Lucas Kanade framework to deduce the registration equation; the performances of our algorithm are shown on two satellite datasets.

Abstract Due to the growing need for large quantities of human animation data in the entertainment industry, it has become a necessity to compress motion capture sequences in order to ease their storage and transmission. We present a novel, lossy compression method for human motion data that exploits both temporal and spatial coherence. Given one motion, we first approximate the poses manifold using Principal Geodesics Analysis (PGA) in the configuration space of the skeleton. We then search this approximate manifold for poses matching end‐effectors constraints using an iterative minimization algorithm that allows for real‐time, data‐driven inverse kinematics. The compression is achieved by only storing the approximate manifold parametrization along with the end‐effectors and root joint trajectories, also compressed, in the output data. We recover poses using the IK algorithm given the end‐effectors trajectories. Our experimental results show that considerable compression rates can be obtained using our method, with few reconstruction and perceptual errors.

In this paper we carry out cooperatively both disparity and object boundary estimation by setting the two tasks in a unified Markovian framework. We introduce a new joint probabilistic model that allows to estimate disparities through a Markov random field model. Boundary estimation then cooperates with disparity estimation to gradually and jointly improve accuracy. The feedback from boundary estimation to disparity estimation is made through the use of an auxiliary field referred to as a displacement field. This field suggests the corrections that need to be applied at disparity discontinuities in order that they align with object boundaries. The joint model reduces to a Markov random field model when considering disparities while it reduces to a Markov chain when focusing on the displacement field. The performance of our approach is illustrated on real stereo images sets, demonstrating the power of this cooperative framework.

This paper describes the acquisition and content of a new multi-modal database. Some tools for making use of the data streams are also presented. The Computational Audio-Visual Analysis (CAVA) database is a unique collection of three synchronised data streams obtained from a binaural microphone pair, a stereoscopic camera pair and a head tracking device. All recordings are made from the perspective of a person; i.e. what would a human with natural head movements see and hear in a given environment. The database is intended to facilitate research into humans' ability to optimise their multi-modal sensory input and fills a gap by providing data that enables human centred audio-visual scene analysis. It also enables 3D localisation using either audio, visual, or audio-visual cues. A total of 50 sessions, with varying degrees of visual and auditory complexity, were recorded. These range from seeing and hearing a single speaker moving in and out of field of view, to moving around a 'cocktail party' style situation, mingling and joining different small groups of people chatting.

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Because of their lack of rules, general broadcast videos are more difficult to analyze than news or sport videos. To retrieve human interventions in this context, a robust face tracker is needed. The approach we investigate for face tracking combines three main modules that are a face detector, a region-based tracker and an eye tracker. The region-based tracker relies on a robust parametric motion estimation technique. The eye tracker is based on a Kalman filter. The analysis of the coherence of the trackers output provides an efficient way to detect profile positions and tracking errors. We have thus defined an entirely automatic tracker, able to manage several appearing/disappearing faces, without any a priori knowledge on the image sequence. Experimental results on broadcast videos demonstrate its efficiency to deal with large and rapid motions, occlusions and faces in profile position.

In this paper we present a technique for the tracking of textured almost planar object. The target is modeled as a noisy planar cloud of points. The tracking is led with an appropriate non linear stochastic filter. The particular system that we devised is conditionally Gaussian and can be efficiently implemented through variance reduction principle known as Rao-Blackwellisation. Our model allows also to melt a correlation measurements with dynamic model estimated from the images. Such a cooperation within a stochastic filtering framework allows the tracker to be robust to occlusions and target's unpredictable changes of speed and direction. We demonstrate the efficiency of the tracker on different types of real world sequences.

In this paper, a new conditional formulation of classical filtering methods is proposed. This formulation is dedicated to image sequence-based tracking. These conditional filters allow solving systems whose measurements and state equation are estimated from the image data. In particular, the model that is considered for point tracking combines a state equation relying on the optical flow constraint and measurements provided by a matching technique. Based on this, two point trackers are derived. The first one is a linear tracker well suited to image sequences exhibiting global-dominant motion. This filter is determined through the use of a new estimator, called the conditional linear minimum variance estimator. The second one is a nonlinear tracker, implemented from a conditional particle filter. It allows tracking of points whose motion may be only locally described. These conditional trackers significantly improve results in some general situations. In particular, they allow for dealing with noisy sequences, abrupt changes of trajectories, occlusions, and cluttered background.