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Titre: La vision par ordinateur

Auteurs: R. Lepage

Ecole: École de Technologie Supérieure

Résumé: Un système de vision peut être considéré comme un processus de traitement d’information : un stimulus est présenté à l’entrée du système et l’information ainsi reçue est traitée afin d’en extraire une représentation symbolique. Cette dernière pourra par la suite être utilisée comme entrée à un processus de traitement de plus haut niveau ou actionner des mécanismes de navigation ou d’adaptation à l’environnement. Le système de vision peut donc être avantageusement analysé comme un système de traitement d’information (Marr, 1982; Jolion, 2001). Pour être complète, l’analyse du système de vision doit alors être entreprise à trois niveaux de compréhension, comme l’illustre le tableau 1.1. Cette analyse à trois niveaux assure que toutes les facettes du problème posé seront évaluées afin de proposer une solution qui tienne compte de ces différents niveaux d’abstraction.

La section 1.1 présente une telle analyse et décrit un modèle algorithmique du système de vision. Dans ce modèle, le processus de vision est décomposé en représentations internes de l’information et en algorithmes qui permettent de passer d’une représentation interne à une autre. La section 1.1 précise les formes de représentations internes utilisées aux différents niveaux du système de vision. La section 1.2 propose une modélisation algorithmique modifiée du système de vision. C’est ce modèle modifié qui sera utilisé tout au long du cours. La section 1.2 décrit le modèle modifié de même que les différents algorithmes retenus pour sa réalisation.

Extrait du sommaire:
1. Les modèles computationnels en vision par ordinateur 1
1.1 Modélisation algorithmique du système de vision 2
1.1.1 Images d’éclairement 4
1.1.2 Croquis. 4
1.1.3 Esquisse 6
1.1.4 Esquisse 2,5D. 7
1.1.5 Description 3D 10
1.2 Modèle à base de connaissances 12
1.2.1 Détection des arêtes 14
1.2.2 Analyse de la profondeur 14
1.2.3 Analyse de l’éclairement 15
1.2.4 Fusion et interpolation 15
1.3 Modèle simplifié pour la reconnaissance 17
Conclusion 19
Références. 20
2. Formation des images d’éclairement 25
2.1 Formation des images 25
2.1.1 Équation du rayon lumineux 26
2.1.2 Équation de formation des images. 30
2.1.3 Surface lambertienne 33
2.2 Synthèse de l’image d’éclairement à partir d’une image 3D 35
2.2.1 Images de profondeur 35
2.2.2 Images d’éclairement 36
Hypothèses sur la scène 3D 37
Géométrie 37
Équation de formation des images 42
Calcul des gradients de surface par dérivée partielle 44
Calcul des gradients de surface par la méthode du plan tangent. 46
Programmation de l’algorithme 51
2.3 Génération d’images stéréo d’éclairement à partir d’une image 3D. 53
3. L’analyse multirésolution en vision informatique 57
3.1 Analyse d’une scène à multiple résolution spatiale 58
3.1.1 Primitives de l’image à multiple résolutions 61
3.1.2 Discrétisation de l’échelle d’espace. 65
3.1.3 Filtre passe-bas pour la génération des niveaux de résolution. 65
3.2 Représentation pyramidale 68
Conclusion 73
Références. 75
4. Détection des arêtes d’une image. 81
4.1 Attributs de la scène observée. 82
4.2 Détection des variations brusques d’éclairement 84
4.2.1 Maximum de la dérivée première directionnelle 86
4.2.2 Maximum de la dérivée première isotrope 93
4.2.3 Passage par zéro de la dérivée seconde directionnelle. 94
4.2.4 Passage par zéro de la dérivée seconde isotrope 94
4.2.5 Détection des arêtes par apprentissage supervisé 97
4.2.6 Détection multirésolution des arêtes par réseau neuronique 100
4.3 Détection neuronique de l’amplitude du gradient. 100
4.4 Amincissement des arêtes 101
4.5 Renforcement des arêtes 103
4.6 Résultats expérimentaux 106
Conclusion 108
Références. 110

Formation-Traitement d'image-cours 7

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