This paper presents an original algorithm for the computation of optical ow called Orthogonal Dynamic Programming (ODP) as well as several enhancements to it. The principle is to minimize a sum of square di erences (SSD) between a pair of images. The originality of the approach is that an optimal matching is searched for entire image strips rather than for pixel neighborhoods. Dynamic programming is used to provide very robust strip alignmentsandamultiresolution iterative process is used to compute the velocity eld. Extensions to the computation of the velocity eldfornoninteger image indexes, to the use of more than two images, and to the search for subpixel velocities, are presented. Results obtained for the Barron, Fleet and Beauchemin performance tests appear to be at least as good as or better than those obtained using classical optical ow detection methods. 1

This paper introduces a new and original algorithm for optical flow detection. It is based on an iterative search for a displacement field that minimizes the L 1 or L 2 distance between two images. Both images are sliced into parallel and overlapping strips. Corresponding strips are aligned using dynamic programming exactly as 2D representations of speech signal are with the DTW algorithm. Two passes are performed using orthogonal slicing directions. This process is iterated in a pyramidal fashion by reducing the spacing and width of the strips. This algorithm provides a very high quality matching for calibrated patterns as well as for human visual sensation. The results appears to be at least as good as those obtained with classical optical flow detection methods. 1. INTRODUCTION Optical flow detection is a very essential and generic procedure that needs to be implemented in computer vision systems. It is necessary in a wide range of applications such as: image matching for stereo...

This paper presents an original algorithm called the \Orthogonal Algorithm " for image matching using dynamic programming and experimental results from its application to stereovision and image interpolation. The algorithm provides a dense, continuous and di erentiable eld of bidimensional displacements like classical optical ow detection algorithms. It is based on an iterative search for a displacement eld that minimizes the L1 or L2 distance between two images. Both images are sliced into parallel and overlapping strips. Corresponding strips are aligned using dynamic programming exactly as 2D representations of speech signal are with the DTW algorithm. Two passes are performed using orthogonal slicing directions. This process is iterated in a pyramidal fashion while reducing the spacing and width of the strips. Very good results have been obtained for stereovision and image interpolation. 1.

CONTENTS 1 Contents 1 Introduction 3 2 Probl'ematique 4 2.1 Architecture des machines informatiques . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Exp'erimentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Sp'ecificit'es de la perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3 Les processeurs de reconnaissance des formes 8 3.1 Le coprocesseur de programmation dynamique . . . . . . . . . . . . . . . . . . . 9 3.2 Le microprocesseur de Comparaison Dynamique (PCD) . . . . . . . . . . . . . 12 3.3 Le syst`eme multiprocesseur de reconnaissance phon'etique . . . . . . . . . . . . . 14 3.4 Bilan et perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Le Calculateur Fonctionnel 18 4.1 Le mod`ele d'ex'ecution flots de donn'ees cabl'es . . . . . . . . . . . . . . . . . . . . 19 4.2 La programmation fonctionnelle . . . .