Unsupervised spatio-temporal filtering of image sequences. A mean-shift specification
| Autor: | Dominik S. Meier, Simon Mure, Thomas Grenier, Hugues Benoit-Cattin, Charles R.G. Guttmann |
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| Přispěvatelé: | Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital [Boston] |
| Rok vydání: | 2015 |
| Předmět: |
business.industry
Feature vector Bandwidth (signal processing) Pattern recognition Synthetic data Constraint (information theory) Range (mathematics) Artificial Intelligence Feature (computer vision) Signal Processing Convergence (routing) Computer Vision and Pattern Recognition Mean-shift Artificial intelligence business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Software Mathematics |
| Zdroj: | ResearcherID Pattern Recognition Letters Pattern Recognition Letters, Elsevier, 2015, 68 (Part), pp.48-55. ⟨10.1016/j.patrec.2015.07.021⟩ |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2015.07.021 |
| Popis: | We propose an unsupervised spatio-temporal image filtering method based on mean-shift.We adapt the spatial and feature range domains to handle temporal evolution.A constraint is added on the sample's evolution to select temporal neighbors.Our method outperforms standard mean-shift by adequately considering time information.Effectiveness is demonstrated on both synthetic and real brain MRI time-series data. A new spatio-temporal filtering scheme based on the mean-shift procedure, which computes unsupervised spatio-temporal filtering for univariate feature evolution, is proposed in this paper. Our main contributions are on one hand the modification of the spatial/range domains to appropriately integrate the temporal feature into the mean-shift iterative form and on the other hand the addition of a trajectory constraint in the feature space with the use of the infinity norm. Therefore, only the samples living the same life in the feature space will converge. Major assets of the standard mean-shift framework such as convergence and bandwidth parameters adjustment are preserved. In this paper, we study the relative importance of the bandwidth parameters and the efficiency of the proposed method is assessed on synthetic data and compared to the standard mean-shift framework for spatio-temporal data filtering. The obtained results have allowed us to undertake a first study on real data, which has led to encouraging results in identifying spatio-temporal evolution of multiple sclerosis lesions appearing on T2-weighted MR images. |
| Databáze: | OpenAIRE |
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