Contributions to a fast and robust object recognition in images
type: Dataset
description: In this thesis, we first present a contribution to overcome this problem of robustness for the recognition of object instances, then we straightly extend this contribution to the detection and localization of classes of objects. In a first step, we have developed a method inspired by graph matching to address the problem of fast recognition of instances of specific objects in noisy conditions. This method allows to easily combine any types of local features (eg contours, textures ...) less affected by noise than keypoints, while bypassing the normalization problem and without penalizing too much the detection speed. Unlike other methods based on a global rigid transformation, our approach is robust to complex deformations such as those due to perspective or those non-rigid inherent to the model itself (e.g. a face, a flexible magazine). Our experiments on several datasets have showed the relevance of our approach. It is overall slightly less robust to occlusion than existing approaches, but it produces better performances in noisy conditions. In a second step, we have developed an approach for detecting classes of objects in the same spirit as the bag-of-visual-words model. For this we use our cascaded micro-classifiers to recognize visual words more distinctive than the classical words simply based on visual dictionaries. Training is divided into two parts: First, we generate cascades of micro-classifiers for recognizing local parts of the model pictures and then in a second step, we use a classifier to model the decision boundary between images of class and those of non-class. We show that the association of classical visual words (from keypoints patches) and our disctinctive words results in a significant improvement. The computation time is generally quite low, given the structure of the cascades that minimizes the detection time and the form of the classifier is extremely fast to evaluate.
landing page: https://theses.hal.science/tel-00694442
identifier: NNT: 2011ISAL0042
keyword: appariement-de-graphes
keyword: computer-science
keyword: graph-matching
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: informatique
keyword: mobile-robotics
keyword: optimisation
keyword: optimization
keyword: pattern-recognition
keyword: reconnaissance-de-forme
keyword: reconnaissance-dobjet
keyword: robotique-mobile
keyword: specific-object-recognition
keyword: theses
issued: 2026-05-19T21:31:33.000613
modified: 2026-05-19T21:31:33.000618
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Contributions to a fast and robust object recognition in images
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00694442
issued: 2026-05-19T21:31:33.059390
modified: 2026-05-19T21:31:32.973032