An Algorithm for Motion Parameter Direct Estimate

Motion estimation in image sequences is undoubtedly one of the most studied research fields, given that motion estimation is a basic tool for disparate applications, ranging from video coding to pattern recognition. In this paper a new methodology which, by minimizing a specific potential function, directly determines for each image pixel the motion parameters of the object the pixel belongs to is presented. The approach is based on Markov random fields modelling, acting on a first-order neighborhood of each point and on a simple motion model that accounts for rotations and translations. Experimental results both on synthetic (noiseless and noisy) and real world sequences have been carried out and they demonstrate the good performance of the adopted technique. Furthermore a quantitative and qualitative comparison with other well-known approaches has confirmed the goodness of the proposed methodology

Region-based representations of image and video: segmentation tools for multimedia services

This paper discusses region-based representations of image and video that are useful for multimedia services such as those supported by the MPEG-4 and MPEG-7 standards. Classical tools related to the generation of the region-based representations are discussed. After a description of the main processing steps and the corresponding choices in terms of feature spaces, decision spaces, and decision algorithms, the state of the art in segmentation is reviewed. Mainly tools useful in the context of the MPEG-4 and MPEG-7 standards are discussed. The review is structured around the strategies used by the algorithms (transition based or homogeneity based) and the decision spaces (spatial, spatio-temporal, and temporal). The second part of this paper proposes a partition tree representation of images and introduces a processing strategy that involves a similarity estimation step followed by a partition creation step. This strategy tries to find a compromise between what can be done in a systematic and universal way and what has to be application dependent. It is shown in particular how a single partition tree created with an extremely simple similarity feature can support a large number of segmentation applications: spatial segmentation, motion estimation, region-based coding, semantic object extraction, and region-based retrieval.Peer ReviewedPostprint (published version

An active mesh for movement estimation with discontinuities

We aim at modeling a motion vector field by processing a sequence of images. We focus on the detection of motion discontinuities experimented by a moving deformable object. The method is based on a multiscale approach. A Markov Random (MR) label field is built at each scale from an initial distribution of the field. The ground of the motion estimation is a spatial partition of the image given by an elastic mesh superimposed onto the data. The mesh deforms, driven by some selected image features (intensity gradients), under the constraint that the motion field remains locally coherent and uniform within each patch. The motion vector field and the elastic mesh are obtained by minimizing a non-convex energy function considering the image features and the motion vector field simultaneously. Each term of the energy function is defined in a multiscale Markovian context and minimized according to the maximum a posteriori (MAP) criterion. The mesh deformation and the modeling of the related vector field both contribute to the iterative « top-down » optimization process within an alternate relaxation scheme. The model copes with discontinuities thanks to the adaptive partition of the image. The ridges of the mesh progressively move toward the motion discontinuities. The results on noisy complex synthetic sequences show a good estimation of the motion vector field with strong discontinuities at the object interfaces. We apply the proposed method to real short-axis IRM cardiac sequence.L'objectif de la méthode est d'introduire la notion de discontinuité dans l'estimation de mouvement d'objet déformable. L'approche est multi-échelle. Un champ de primitives de vecteurs de déplacement est construit à chaque niveau d'échelle à partir d'une distribution initiale du champ. Le support d'estimation du champ de vecteurs de déplacement est une partition spatiale de l'observation donnée par un maillage élastique plaqué sur l'image. Celui-ci se déforme selon des critères image sous contraintes géométriques, tout en assurant un mouvement cohérent uniforme dans chaque maille. Le champ et la partition associée sont obtenus par minimisation d'une énergie non convexe. Chaque terme de la fonctionnelle est défini dans un contexte markovien et minimisé selon le critère du MAP. L'estimation du champ de vecteurs de déplacement et la déformation du maillage contribuent alternativement au processus « haut-bas » de relaxation itératif. Ainsi, les arêtes du maillage se déplacent-elles progressivement vers les discontinuités du champ de vecteurs de déplacement, lorsqu'elles existent. Les performances de la méthode sont mises en valeur sur des séquences d'images de synthèse complexes en présence de bruit ; la méthode est ensuite utilisée pour effectuer l'estimation de mouvement cardiaque à partir de séquence d'images IRM petit axe

Segmenting and tracking objects in video sequences based on graphical probabilistic models

Ph.DDOCTOR OF PHILOSOPH