Zobrazeno 1 - 10
of 226
pro vyhledávání: '"Bouthemy, Patrick"'
Autor:
Meunier, Etienne, Bouthemy, Patrick
Human beings have the ability to continuously analyze a video and immediately extract the motion components. We want to adopt this paradigm to provide a coherent and stable motion segmentation over the video sequence. In this perspective, we propose
Externí odkaz:
http://arxiv.org/abs/2310.01040
In this paper, we present a CNN-based fully unsupervised method for motion segmentation from optical flow. We assume that the input optical flow can be represented as a piecewise set of parametric motion models, typically, affine or quadratic motion
Externí odkaz:
http://arxiv.org/abs/2201.02074
Polynomial regression is a recurrent problem with a large number of applications. In computer vision it often appears in motion analysis. Whatever the application, standard methods for regression of polynomial models tend to deliver biased results wh
Externí odkaz:
http://arxiv.org/abs/1804.06504
This paper considers the problem of localizing actions in videos as a sequences of bounding boxes. The objective is to generate action proposals that are likely to include the action of interest, ideally achieving high recall with few proposals. Our
Externí odkaz:
http://arxiv.org/abs/1607.02003
Handling all together large displacements, motion details and occlusions remains an open issue for reliable computation of optical flow in a video sequence. We propose a two-step aggregation paradigm to address this problem. The idea is to supply loc
Externí odkaz:
http://arxiv.org/abs/1407.5759
Publikováno v:
In Pattern Recognition Letters 1 December 2019 128:298-305
Akademický článek
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Publikováno v:
In Computer Vision and Image Understanding April 2016 145:81-94
Akademický článek
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Publikováno v:
RFIAP 2022-Reconnaissance des Formes, Image, Apprentissage et Perception
RFIAP 2022-Reconnaissance des Formes, Image, Apprentissage et Perception, Jul 2022, Vannes, France. pp.1-10
RFIAP 2022-Reconnaissance des Formes, Image, Apprentissage et Perception, Jul 2022, Vannes, France. pp.1-10
This paper presents a CNN-based fully unsupervised method for motion segmentation from optical flow. We assume that the input optical flow can be represented as a piecewise set of parametric motion models, typically, affine or quadratic motion models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::dde7aa5b28bfb32fdcbbdd2b7ffa3dbf
https://hal.inria.fr/hal-03926935
https://hal.inria.fr/hal-03926935