27 results on '"Behzad Mirmahboub"'
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2. Median-Tree: An Efficient Counterpart of Tree-of-Shapes.
- Author
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Behzad Mirmahboub, Deise Santana Maia, François Merciol, and Sébastien Lefèvre
- Published
- 2021
- Full Text
- View/download PDF
3. Liver Segmentation in Abdominal CT Images Using Probabilistic Atlas and Adaptive 3D Region Growing.
- Author
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Shima Rafiei, Nader Karimi, Behzad Mirmahboub, Kayvan Najarian, Banafsheh Felfeliyan, Shadrokh Samavi, and S. M. Reza Soroushmehr
- Published
- 2019
- Full Text
- View/download PDF
4. Component trees for image sequences and streams.
- Author
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çaglayan Tuna, Behzad Mirmahboub, François Merciol, and Sébastien Lefèvre
- Published
- 2020
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5. Distance Penalization and Fusion for Person Re-identification.
- Author
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Behzad Mirmahboub, Mohamed Lamine Mekhalfi, and Vittorio Murino
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- 2017
- Full Text
- View/download PDF
6. Person re-identification by order-induced metric fusion.
- Author
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Behzad Mirmahboub, Mohamed Lamine Mekhalfi, and Vittorio Murino
- Published
- 2018
- Full Text
- View/download PDF
7. Person re-identification using sparse representation with manifold constraints.
- Author
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Behzad Mirmahboub, Hamed Kiani, Amran Bhuiyan, Alessandro Perina, Baochang Zhang 0001, Alessio Del Bue, and Vittorio Murino
- Published
- 2016
- Full Text
- View/download PDF
8. Image retargeting using depth assisted saliency map.
- Author
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F. Shafieyan, Nader Karimi, Behzad Mirmahboub, Shadrokh Samavi, and Shahram Shirani
- Published
- 2017
- Full Text
- View/download PDF
9. Person Re-identification Using Robust Brightness Transfer Functions Based on Multiple Detections.
- Author
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Amran Bhuiyan, Behzad Mirmahboub, Alessandro Perina, and Vittorio Murino
- Published
- 2015
- Full Text
- View/download PDF
10. Bone extraction in X-ray images by analysis of line fluctuations.
- Author
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Salome Kazeminia, Nader Karimi, Behzad Mirmahboub, S. Mohamad R. Soroushmehr, Shadrokh Samavi, and Kayvan Najarian
- Published
- 2015
- Full Text
- View/download PDF
11. Image seam carving using depth assisted saliency map.
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F. Shafieyan, Nader Karimi, Behzad Mirmahboub, Shadrokh Samavi, and Shahram Shirani
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- 2014
- Full Text
- View/download PDF
12. View-Invariant Fall Detection System Based on Silhouette Area and Orientation.
- Author
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Behzad Mirmahboub, Shadrokh Samavi, Nader Karimi, and Shahram Shirani
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- 2012
- Full Text
- View/download PDF
13. Automatic Monocular System for Human Fall Detection Based on Variations in Silhouette Area.
- Author
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Behzad Mirmahboub, Shadrokh Samavi, Nader Karimi, and Shahram Shirani
- Published
- 2013
- Full Text
- View/download PDF
14. Fast Pattern Spectra using Tree Representation of the Image for Patch Retrieval
- Author
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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)
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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.
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- 2021
15. Image retargeting using depth assisted saliency map
- Author
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Behzad Mirmahboub, F. Shafieyan, Shahram Shirani, Nader Karimi, and Shadrokh Samavi
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Pixel ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,020207 software engineering ,02 engineering and technology ,Image (mathematics) ,Seam carving ,Depth map ,Computer Science::Computer Vision and Pattern Recognition ,Distortion ,Signal Processing ,Retargeting ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Computer Vision and Pattern Recognition ,Artificial intelligence ,Electrical and Electronic Engineering ,Visual artifact ,business ,Image resolution ,Software ,Mathematics - Abstract
Retargeting algorithms are used to transfer and display images on devices with various sizes and resolutions. All of these algorithms try to preserve the important parts of image against distortions while producing a retargeted image with visual quality comparable with the original one. The main challenge in different algorithms is to find a suitable energy function that properly estimates the importance of each pixel in image. Hence the energy map needs to be improved. In this paper we propose a novel energy function which combines the information from saliency map, depth map and gradient map. We also present an algorithm to adaptively assign proper weights to these three importance maps for each input image. Then we calculate a switching threshold based on energy map that determines when to apply seam carving or scaling. The idea is to use a combination of seam carving and scaling to preserve the structure of important parts of image against distortion when the image size decreases beyond a point. This method reduces shape deformations and visual artifacts in salient regions of images and produces better quality output images. The results of the proposed method show superior visual quality in produced images in comparison to the state-of-the-arts.
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- 2017
16. Person Re-Identification Using Pose-Driven Body Parts
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Abdennaceur Kachouri, Behzad Mirmahboub, Francois Bremond, Mohamed Amine Ben Farah, and Salwa Baabou
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Matching (statistics) ,business.industry ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,0102 computer and information sciences ,02 engineering and technology ,Texture (music) ,Behavior recognition ,01 natural sciences ,Re identification ,Discriminative model ,010201 computation theory & mathematics ,Similarity (psychology) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,business ,Whole body - Abstract
The topic of Person Re-Identification (Re-ID) is currently attracting much interest from researchers due to the various possible applications such as behavior recognition, person tracking and safety purposes at public places. General approach is to extract discriminative color and texture features from images and calculate their distances as a measure of similarity. Most of the work consider whole body to extract descriptors. However, human body maybe occluded or seen from different views that prevent correct matching between persons.
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- 2019
17. Liver Segmentation in Abdominal CT Images Using Probabilistic Atlas and Adaptive 3D Region Growing
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Kayvan Najarian, Shima Rafiei, Banafsheh Felfeliyan, Shadrokh Samavi, Behzad Mirmahboub, S. M. Reza Soroushmehr, and Nader Karimi
- Subjects
Computer science ,Abdominal ct ,02 engineering and technology ,computer.software_genre ,Liver segmentation ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Imaging, Three-Dimensional ,0302 clinical medicine ,Voxel ,Abdomen ,Image Processing, Computer-Assisted ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Diagnosis, Computer-Assisted ,Probabilistic atlas ,Atlas (topology) ,business.industry ,Pattern recognition ,Image segmentation ,Liver ,Region growing ,020201 artificial intelligence & image processing ,Artificial intelligence ,Tomography, X-Ray Computed ,business ,computer ,Algorithms - Abstract
Automatic liver segmentation plays a vital role in computer-aided diagnosis or treatment. Manual segmentation of organs is a tedious and challenging task and is prone to human errors. In this paper, we propose innovative pre-processing and adaptive 3D region growing methods with subject-specific conditions. To obtain strong edges and high contrast, we propose effective contrast enhancement algorithm then we use the atlas intensity distribution of most probable voxels in probability maps along with location before designing conditions for our 3D region growing method. We also incorporate the organ boundary to restrict the region growing. We compare our method with the label fusion of 13 organs on state-of-the-art Deeds registration method and achieved Dice score of 92.56%.
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- 2019
18. Person re-identification by order-induced metric fusion
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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
19. Distance penalization and fusion for person re-identification
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Mohamed Lamine Mekhalfi, Behzad Mirmahboub, and Vittorio Murino
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Computer science ,business.industry ,Pipeline (computing) ,Feature extraction ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,computer.software_genre ,Sensor fusion ,Distance measures ,Feature (computer vision) ,Histogram ,Metric (mathematics) ,0202 electrical engineering, electronic engineering, information engineering ,Benchmark (computing) ,020201 artificial intelligence & image processing ,Data mining ,Artificial intelligence ,business ,computer - Abstract
This paper presents a novel person re-identification framework based on data fusion. The pipeline of the proposed method is composed of two stages. First, a metric learning paradigm is applied on a bunch of distinct feature extractors to produce an ensemble of estimated distance measures, which are subsequently penalized according to their confidence in evidencing the correct matches from the false ones, and averaged as to draw a final decision. Second, the close persons from the gallery are selected based on the previously fused distance estimates, and utilized to build a dictionary as to reconstruct a given probe pattern. Evaluated on benchmark datasets, the proposed framework advances the state-of-the-art by interesting margins. In particular, Rank1 gains amounting to about 12%, 1%, 6%, and 12%, were scored on VIPeR, CAVIAR4REID, iLIDS, and 3DPeS, respectively.
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- 2017
20. Person re-identification using sparse representation with manifold constraints
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Baochang Zhang, Hamed Kiani, Amran Bhuiyan, Alessandro Perina, Alessio Del Bue, Behzad Mirmahboub, and Vittorio Murino
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business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,020207 software engineering ,Pattern recognition ,02 engineering and technology ,Pedestrian ,Sparse approximation ,Re identification ,Manifold ,Redundancy (information theory) ,Discriminative model ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,Neural coding ,business ,Information redundancy ,Mathematics - Abstract
Human re-identification is still a challenging task due to the human pose and illumination variations. Nowadays, surveillance cameras with high frame rate are capable of capturing several consecutive frames from each person. Multi-shot images provide richer information of the target person compared to a single-shot image. They, however, produce a high cost of information redundancy which may degrade the performance of re-identification systems. In this paper, we propose a novel framework that combines sparse coding and manifold constraints to extract discriminative information from multi-shot images of one pedestrian for person re-identification across a set of non-overlapped surveillance cameras. The evaluation over two standard multi-shot datasets shows very competitive accuracy of our framework against the state-of-the-art.
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- 2016
21. Bone extraction in X-ray images by analysis of line fluctuations
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Shadrokh Samavi, Kayvan Najarian, S. Kazeminia, Behzad Mirmahboub, Nader Karimi, and S.M.R. Soroushmehr
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Active contour model ,Morphological gradient ,Computer science ,business.industry ,Region growing ,Line (geometry) ,Segmentation ,Computer vision ,Artificial intelligence ,business ,Edge detection - Abstract
Segmentation of X-ray bone images is of concern in many medical applications such as detection of osteoporosis and bone fractures. Segmentation of such images is a challenging process. Varying brightness throughout the image makes it difficult to separate bones from background and soft tissue. Costume made as well as standard segmentation methods, such as active contour and region growing, have been applied to bone X-ray images. Although each method could perform well for some images, due to variety of bone structures and lighting conditions none of these methods can be considered as complete. In this paper we present a new bone segmentation method in which an image goes through preprocessing steps such as noise cancellation and edge detection. Analysis of intensity fluctuations in all rows of the image results in more accurate segmentation of bone regions. Visual evaluation show that the proposed algorithm segments bones better than conventional and some recent bone segmentation approaches.
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- 2015
22. Person Re-identification Using Robust Brightness Transfer Functions Based on Multiple Detections
- Author
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Alessandro Perina, Vittorio Murino, Amran Bhuiyan, and Behzad Mirmahboub
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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.
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- 2015
23. Image seam carving using depth assisted saliency map
- Author
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Behzad Mirmahboub, Nader Karimi, F. Shafieyan, Shahram Shirani, and Shadrokh Samavi
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Pixel ,Computer science ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Visual appearance ,Seam carving ,Depth map ,Distortion ,Retargeting ,Computer vision ,Saliency map ,Artificial intelligence ,Visual artifact ,business ,Image resolution - Abstract
Retargeting algorithms are needed to transfer an image from a device to another with different size and resolution. The goal is to preserve the best visual quality for important objects of the original image. In order to reduce image size, pixels should be removed from less important parts of the image. Therefore, we need an energy function to select less important pixels in seam carving. Various energy functions have been proposed in previous works to minimize the distortion in salient objects. In this paper we combine three different importance maps to form a new energy map. We first use both gradient and depth maps to highlight the values in the saliency map, eventually generates the final energy map. Experimental results using the proposed energy map show better visual appearance in comparison to previous algorithms even at high resizing percentage. The visual artifacts that cause shape deformation in salient objects and deteriorates geometrical consistency of the scene are considerably reduced in our proposed algorithm.
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- 2014
24. Multi focus image fusion using categorization of energy levels
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Iman Roosta, Nader Karimi, Shadrokh Samavi, and Behzad Mirmahboub
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Image fusion ,business.industry ,Computer science ,Image quality ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image processing ,Pattern recognition ,Digital image ,Image texture ,Digital image processing ,Computer vision ,Artificial intelligence ,business ,Image restoration ,Feature detection (computer vision) - Abstract
Multifocus image fusion plays an important role in image processing and machine vision applications. In frequent occasions, captured images are not focus throughout the image because the optical lenses that are commonly used for producing image have limited depth of field. Therefore only the objects that are near the focal range of the camera are clear while other parts are blurred. One solution is to capture several images with different focal ranges and combine them to produce an image that is focused everywhere. To identify focused regions, current implementations of the mentioned solution use spatial or transform-domain. These methods usually suffer from artifacts such as blockiness or ringing. In this paper we have defined an energy term and categorized a region's energy into low, medium, and high levels. Then based on the level of energy each pixel is defined as either focused or not. The output fused image is constructed from focused pixels of the two source images. Experimental results reveal the superiority of our method compared to comparable algorithms.
- Published
- 2014
25. Outdoor fire detection based on color and motion characteristics
- Author
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Mansour Nejati, Maedeh Jamali, Shadrokh Samavi, and Behzad Mirmahboub
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Warning system ,Pixel ,Contextual image classification ,Fire detection ,business.industry ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Process (computing) ,YCbCr ,Motion detection ,Support vector machine ,Computer vision ,Artificial intelligence ,business - Abstract
With due attention to industry deployment and extension of urban zones, early warning systems have critical role in giving emergency response to unexpected events. Video-base fire detection is a low cost and effective method for this purpose. Most of available fire detection methods only use color information in detection process that is inaccurate. This paper intends to increase the accuracy of fire detection in video sequences using motion detection and combination of two classifiers. Movement of pixels and their color in the YCbCr space are considered for detection. Using this combined method, false alarms due to movements of ordinary objects with fire-like color, are greatly reduced in comparison with other color based fire detection systems.
- Published
- 2013
26. Automatic monocular system for human fall detection based on variations in silhouette area
- Author
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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
27. View-Invariant Fall Detection System Based on Silhouette Area and Orientation
- Author
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Shadrokh Samavi, Nader Karimi, Behzad Mirmahboub, and Shahram Shirani
- Subjects
Kernel (image processing) ,Contextual image classification ,business.industry ,Computer science ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Word error rate ,Pattern recognition ,Computer vision ,Artificial intelligence ,business ,Object detection ,Silhouette - Abstract
Population of old generation that live alone is growing in most countries. Surveillance systems help them stay home and reduce the burden on the healthcare system. Automatic visual surveillance systems have advantages over wearable devices. They extract features from video sequences and use them for event classification. But these features are dependent on the position of cameras relative to the person. Therefore they need multi-camera for more accuracy that increases cost and complexity. In this paper we propose using silhouette area combined with inclination angle as robust features that can be measured using only one camera with an arbitrary direction. Through rigorous simulations on a publicly available dataset the error rate of the system is found to be less than 1%.
- Published
- 2012
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