Notice bibliographique
Résumé
This thesis presents the development of image processing algorithms based on a Microsoft Kinect camera system. The algorithms developed during this thesis are applied on the depth image received from Kinect and are supposed to model a three dimensional object based representation of the recorded scene. The motivation behind this thesis is to develop a system which assists visually impaired people by navigating through unknown environments. The developed system is able to detect obstacles in the recorded scene and to warn about these obstacles. Since the goal of this thesis was not to develop a complete real time system but to invent reliable algorithms solving this task, the algorithms were developed in MATLAB. Additionally a control software was developed by which depth as well as color images can be received from Kinect. The developed algorithms are a combination of already known plane fitting algorithms and novel approaches. The algorithms perform a plane segmentation of the 3D point cloud and model objects out of the received segments. Each obstacle is defined by a cuboid box and thus can be illustrated easily to the blind person. For plane segmentation different approaches were compared to each other to find the most suitable approach. The first algorithm analyzed in this thesis is a normal vector based plane fitting algorithm. This algorithm supplies very accurate results but also has a high computation effort. The second approach, which was finally implemented, is a gradient based 2D image segmentation combined with a RANSAC plane segmentation (6) in a 3D points cloud. This approach has the advantage to find very small edges within the scene but also builds planes based on global constrains. Beside the development of the algorithm results of the image processing, which are really promising, are presented. Thus the algorithm is worth to be improved by further development. The developed algorithm is able to detect very small but significant obstacles but on the other hand does not represent the scene too detailed such that the result can be illustrated accurately to a blind person.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».