Parametric image alignement: unified formalisation with application to the alignment of noisy images and object tracking
type: Dataset
description: Parametric image alignment is a fundamental task of many vision applications such as object tracking, image mosaicking, video compression and augmented reality. To recover the motion parameters, direct image alignment works by optimizing a pixel-based difference measure between a moving image and a fixed-image called template. In the last decade, many efficient algorithms have been proposed for parametric object tracking. However, those approaches have not been evaluated for aligning images of low SNR (Signal to Noise ratio) such as images captured in low-light conditions. In this thesis, we propose a new formulation of image alignment called Bidirectional Framework for unifying existing state of the art algorithms. First, this framework allows us to produce new insights on existing approaches and in particular on the ESM (Efficient Second-order Minimization) algorithm. Subsequently, we provide a theoretical analysis of image noise on the alignment process. This yields the definition of two new approaches : the ACL (Asymmetric Composition on Lie Groups) algorithm and the BCL (Bidirectional Composition on Lie Groups) algorithm, which outperform existing approaches in presence of images of different SNR. Finally, experiments on synthetic and real images captured under low-light conditions allow to evaluate the new and existing approaches under various noise conditions.
issued: 2026-05-27T16:43:50.630632
language: fr
modified: 2026-05-27T16:43:50.630638
contact point: Organization
  fn: CCSD
keyword: alignement-dimages-asymetrique
keyword: asymmetric-image-registration
keyword: bidirectional-framework
keyword: estimation-de-mouvement-parametrique
keyword: formalisme-bidirectionnel
keyword: gradient
keyword: gradient-methods
keyword: groupe-de-lie
keyword: image-alignment
keyword: images-bruitees
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-tscomputer-science-cssignal-and-image-processing
keyword: jacobian-matrix
keyword: lie-groups
keyword: matrice-jacobienne
keyword: noisy-images
keyword: object-tracking
keyword: parametric-motion-estimation
keyword: spisignalengineering-sciences-physicssignal-and-image-processing
keyword: suivi-dobjets
keyword: theses
landing page: https://theses.hal.science/tel-00672589

Parametric image alignement: unified formalisation with application to the alignment of noisy images and object tracking
type: Distribution
format: HTML
issued: 2026-05-27T16:43:50.767728
modified: 2026-05-27T16:43:50.584413
access URL: https://theses.hal.science/tel-00672589

test_moissonnage_selune
type: Agent