Point localization in Multi-camera system

Soumyabrata Dev, Alireza Ghasemi
pas d'éditeur, 2015
Rapport ou document de travail

Résumé

Point localization in multi-camera setups has been widely studied in computer vision. Recently, in finite-resolution camera settings, a consistent and optimal point localization algorithm called SHARP has been proposed, under the assumption of noiseless camera poses and error-free matching. In this work, we relax this assumption on noiseless camera poses and propose a new point localization algorithm. We formulate this point localization task as a gradient-ascent optimization function, for maximizing the objective function under computational geometric constraints. Experimental results verify the efficacy of our approach as compared to the current state-of-the-art localization algorithms.

Official source

https://infoscience.epfl.ch/record/216902?ln=fr

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Concepts associés

Chargement

Publications associées

Chargement

The More you Learn, the Less you Store: Memory-controlled Incremental SVM for Visual Place Recognition

Barbara Caputo, Jie Luo

The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM- -based algorithm, capable of learning model representations incrementally while keeping under control memory requirements. We combine an incremental extension of SVMs [45] with a method reducing the number of support vectors needed to build the decision function without any loss in performance [15] introducing a parameter which permits a user-set trade-off between performance and memory. The resulting algorithm is able to achieve the same recognition results as the original incremental method while reducing the memory growth. Our method is especially suited to work for autonomous systems in realistic settings. We present experiments on two common scenarios in this domain: adaptation in presence of dynamic changes and transfer of knowledge between two different autonomous agents, focusing in both cases on the problem of visual place recognition applied to mobile robot topological localization. Experiments in both scenarios clearly show the power of our approach.

Idiap2010

Chargement

The More you Learn, the Less you Store: Memory-controlled Incremental SVM for Visual Place Recognition

Barbara Caputo, Jie Luo

The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM-based algorithm, capable of learning model representations incrementally while keeping under control memory requirements. We combine an incremental extension of SVMs with a method reducing the number of support vectors needed to build the decision function without any loss in performance introducing a parameter which permits a user-set trade-off between performance and memory. The resulting algorithm is able to achieve the same recognition results as the original incremental method while reducing the memory growth. Our method is especially suited to work for autonomous systems in realistic settings. We present experiments on two common scenarios in this domain: adaptation in presence of dynamic changes and transfer of knowledge between two different autonomous agents, focusing in both cases on the problem of visual place recognition applied to mobile robot topological localization. Experiments in both scenarios clearly show the power of our approach.

2010

Chargement