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Your search keyword '"Behzad Mirmahboub"' showing total 5 results
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5 results on '"Behzad Mirmahboub"'

1. Fast Pattern Spectra using Tree Representation of the Image for Patch Retrieval

Author

Jérôme Moré, Behzad Mirmahboub, David Youssefi, François Merciol, Sébastien Lefèvre, Alain Giros, Observation de l’environnement par imagerie complexe (OBELIX), SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre National d'Études Spatiales [Toulouse] (CNES), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)

Subjects
Selection (relational algebra), Computer science, Computation, 0211 other engineering and technologies, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, large satellite images, Content-based image retrieval, Image (mathematics), Tree representation, pattern spectra, Histogram, 0202 electrical engineering, electronic engineering, information engineering, 021101 geological & geomatics engineering, business.industry, Pattern recognition, Object detection, tree representation, patch retrieval, Tree (data structure), Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, content based image retrieval, Artificial intelligence, business
Abstract

International audience; We extend the notion of content based image retrieval to patch retrieval where the goal is to find the similar patches to a query patch in a large image. Naive searching for similar patches by sequentially computing and comparing descriptors of sliding windows takes a lot of time in a large image. We propose a novel method to compute descriptors for all sliding windows independent from number of patches. We rely on tree representation of the image and exploit the histogram nature of pattern spectra to compute all the required descriptors in parallel. Computation time of the proposed method depends only on the number of tree nodes and is free from query selection. Experimental results show the effectiveness of the proposed method to reduce the computation time and its potential for object detection in large images.

Published
2021

2. Component trees for image sequences and streams

Author

Caglayan Tuna, François Merciol, Behzad Mirmahboub, Sébastien Lefèvre, Environment observation with complex imagery (OBELIX), Université de Bretagne Sud (UBS)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec, SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
Hierarchy, Earth observation, Computer science, business.industry, Pattern recognition, 02 engineering and technology, 01 natural sciences, Grayscale, Image (mathematics), Tree (data structure), Artificial Intelligence, Component (UML), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 0103 physical sciences, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Satellite Image Time Series, Computer Vision and Pattern Recognition, Artificial intelligence, 010306 general physics, business, Time complexity, Software
Abstract

International audience; Morphological hierarchies now form a well-established framework for (still) image modeling and processing. However , their extension to time-related data remains largely unexplored. In this paper, we address such a topic and show how to analyze image sequences with tree-based representations. To do so, we distinguish between three kinds of models, namely spatial, temporal and spatial-temporal hierarchies. For each of them, we review different strategies to build the hierarchy from an image sequence. We also propose some algorithms to update such trees when new images are appended to the series and we compared the time complexity with tree building from scratch. We illustrate our findings with the max and min-tree structures built on grayscale data provided by Satellite Image Time Series that are gathering a growing interest in Earth Observation. Besides, we provide a comparative study for different hierarchies with classification experiments.

Published
2020

3. Person re-identification by order-induced metric fusion

Author

Vittorio Murino, Mohamed Lamine Mekhalfi, and Behzad Mirmahboub

Subjects
0209 industrial biotechnology, Computer science, Cognitive Neuroscience, 02 engineering and technology, Machine learning, computer.software_genre, Video analysis, Measure (mathematics), Induced metric, 020901 industrial engineering & automation, Person re-identification, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Information fusion, Person re-identification, Score weighting, Video analysis, Fusion, business.industry, Score weighting, Computer Science Applications, Weighting, Ranking, Order (business), Benchmark (computing), 020201 artificial intelligence & image processing, Pairwise comparison, Artificial intelligence, Information fusion, business, computer
Abstract

This paper presents a novel two-pronged framework for person re-identification. Its idea articulates over the fact that distinct descriptors manifest different ranking scores for the same probe pattern. Thus, if conveniently fused, the descriptors in hand are ought to compensate each other, leading to significant improvements. In this respect, this paper proposes a learning-free weighting method that penalizes and averages the re-identification estimates (e.g., distances) pointed out by different descriptors according to their confidence in evidencing the correct match, to a given probe person, among a given gallery. We particularly show that tangible improvements can be attained with respect to utilizing each descriptor individually. Moreover, we consider a confidence measure mechanism that treats the mutual pairwise distances within the gallery, in order to raise the scores obtained at the fusion stage, and we show that interesting improvements can be achieved. We evaluate the proposed framework on four benchmark datasets and advance late works by large margins.

Published
2018

4. Person Re-identification Using Robust Brightness Transfer Functions Based on Multiple Detections

Author

Alessandro Perina, Vittorio Murino, Amran Bhuiyan, and Behzad Mirmahboub

Subjects
Brightness, business.industry, Computer science, Video surveillance, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Multiple frame, Brightness transfer function, Re-identification, Transfer function, Re identification, Transfer (computing), Computer vision, Artificial intelligence, business
Abstract

Re-identification systems aim at recognizing the same individuals in multiple cameras and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of Minimum Multiple Cumulative Brightness Transfer Functions to model this appearance variations. It is multiple frame-based learning approach which leverages consecutive detections of each individual to transfer the appearance, rather than learning brightness transfer function from pairs of images. We tested our approach on standard multi-camera surveillance datasets showing consistent and significant improvements over existing methods on two different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method.

Published
2015

5. Automatic monocular system for human fall detection based on variations in silhouette area

Author

Nader Karimi, Shadrokh Samavi, Shahram Shirani, and Behzad Mirmahboub

Subjects
education.field_of_study, Engineering, Monocular, Support Vector Machine, Contextual image classification, business.industry, Feature extraction, Population, Biomedical Engineering, Video Recording, Poison control, Monitoring, Ambulatory, Reproducibility of Results, Silhouette, Support vector machine, Feature (computer vision), Image Processing, Computer-Assisted, Humans, Computer vision, Accidental Falls, Computer Simulation, Artificial intelligence, education, business
Abstract

Population of old generation is growing in most countries. Many of these seniors are living alone at home. Falling is among the most dangerous events that often happen and may need immediate medical care. Automatic fall detection systems could help old people and patients to live independently. Vision-based systems have advantage over wearable devices. These visual systems extract some features from video sequences and classify fall and normal activities. These features usually depend on camera's view direction. Using several cameras to solve this problem increases the complexity of the final system. In this paper, we propose to use variations in silhouette area that are obtained from only one camera. We use a simple background separation method to find the silhouette. We show that the proposed feature is view invariant. Extracted feature is fed into a support vector machine for classification. Simulation of the proposed method using a publicly available dataset shows promising results.

Published
2012

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