Recursive-iterative estimation of displacement vector in a sequence of images

this paper we present an estimator of the 2D displacement vector based on the differential approach . The estimator is composed of three parts: a predictor or a priori estimator, which is recursive, a discontinuity detector and an a posteriori estimator, which is iterative . The proposed estimator can be used for the predictive coding of images using motion compensation . This paper contributes in the modelization of the a priori estimation problem, as well as in the a posteriori improvement of the estimation by successive iterations . The performance evaluation is effected on a normalized sequence of TV images (COST 211 bis) .Nous présentons un estimateur du vecteur bidimensionnel de vitesse utilisant l'approche différentielle . L'estimateur comprend trois modules : un prédicteur ou estimateur a priori de type récursif, un détecteur de discontinuités et un estimateur a posteriori de type itératif. Cet estimateur est conçu de manière à pouvoir être utilisé en codage prédictif avec compensation du mouvement . Cet article contribue à la modélisation de l'estimation a priori, ainsi qu'à l'amélioration a posteriori de l'estimation par itérations successives . L'évaluation de la performance est effectuée sur une séquence d'images TV normalisée (COST 211 bis)

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions

Rate Distortion Theory for Image and Video Coding

Wegive the rate distortion function for three image sequence coders: intra-frame, interframe and motion compensating inter-frame. The intensity function distribution is assumed to be Gaussian, and we suppose spatio-temporal separability and an isotropic model for the spatial part of the intensity function. Wegivequantitative results justifing the use of the above three coding modes in an image sequence coder. Moreover the obtained results showthatthe hybrid coding (motion compensating prediction + spatial compression) is more interesting as motion analysis becomes more effective

Gradient-bared optical flow estimation

The object of this article falls in the domain of motion analysis in computer vision . In this article we present a new method for estimating the two-dimensional velocity field from the single normal component on a contour . The 2-D velocity field results from the orthographic or perspective projection of three-dimensional rigid objects in motion . A hypothesis of local planarity is made . Under this hypothesis the question of unique determination of the 2-D velocity field is studied . We conclude that at least all the curves of second degree admit an infinity of solutions . But there exist most of curves admitting a unique solution. Autoregressive spatial relations on the velocities are given, founded on the planarity and rigidity hypotheses . The autoregressive relations are employed for estimating the 2-D velocity field using a Kalman filter . As observation the normal component is utilized . An application in the case of a simulated motion of a plane curve is given .Présentation d'une nouvelle méthode d'estimation du champ bidimensionnel des vitesses à partir de la seule composante normale sur un contou

A New Pel-Recursive Kalman-Based Motion Estimation Method

We present in this paper a new pel-recursive algorithm for estimating the displacement vector field in image sequences. Firstly we use a brightness-offset term in the motion constraint equation. We follow the Kalman approach to formulate the estimation problem. The state vector of dimension 3 is composed of the displacement vector and the brightness offset. The extended Kalman filter is used to estimate this state vector. The new algorithm is applied on two normalized TV sequences and it is shown that the new algorithm is always better than the commonly used algorithms. The mean square displaced frame difference obtained is about 20% less in comparison with the commonly used algorithms. 1

Panoramic View Construction

In this paper, the problem ofcjVy;jTFy;j the whole view of ascjj bacByTFy; from an imagesequenc is cTAxxfiVTFy First, point orbloc ccyfiLTFyVfifix betweeneac pair ofsucBAfiTFy frames is determined. Three parametric motion models are used: 2-D translation with schT chTLB affine, andprojecVTFy Motion parameters are estimated using either robustcbustTj and the Levenberg--Marquardt algorithm, or affine moment invariants. Then the parametric models areceTjyfij and all the frames are aligned, yielding a whole view of thescTy bacx;yVTFy A new tecTAxxj is introducL for the cTjBAyETF ofacjVj;TFyL frame alignment errors