Author
Hamid Reza Pourreza
Other affiliations: Amirkabir University of Technology, Islamic Azad University
Bio: Hamid Reza Pourreza is an academic researcher from Ferdowsi University of Mashhad. The author has contributed to research in topics: Feature extraction & Camera resectioning. The author has an hindex of 23, co-authored 136 publications receiving 1889 citations. Previous affiliations of Hamid Reza Pourreza include Amirkabir University of Technology & Islamic Azad University.
Papers published on a yearly basis
Papers
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17 Dec 2008
TL;DR: A novel method to detect various posture-based events in a typical elderly monitoring application in a home surveillance scenario by combination of best-fit approximated ellipse around the human body, projection histograms of the segmented silhouette and temporal changes of head position is proposed.
Abstract: Video surveillance is an omnipresent topic when it comes to enhancing security and safety in the intelligent home environments. In this paper, we propose a novel method to detect various posture-based events in a typical elderly monitoring application in a home surveillance scenario. These events include normal daily life activities, abnormal behaviors and unusual events. Due to the fact that falling and its physical-psychological consequences in the elderly are a major health hazard, we monitor human activities with a particular interest to the problem of fall detection. Combination of best-fit approximated ellipse around the human body, projection histograms of the segmented silhouette and temporal changes of head position, would provide a useful cue for detection of different behaviors. Extracted feature vectors are fed to a MLP neural network for precise classification of motions and determination of fall event. Reliable recognition rate of experimental results underlines satisfactory performance of our system.
217 citations
12 Feb 2008
TL;DR: Experimental results show that fuzzy approach for background modeling and background subtraction is relatively more accurate than classical approach and this method is suggested for fuzzy background modeling.
Abstract: Summary Running average method and its modified version are two simple and fast methods for background modeling. In this paper, some weaknesses of running average method and standard background subtraction are mentioned. Then, a fuzzy approach for background modeling and background subtraction is proposed. For fuzzy background modeling, fuzzy running average is suggested. Background modeling and background subtraction algorithms are very commonly used in vehicle detection systems. To demonstrate the advantages of fuzzy running average and fuzzy background subtraction, these methods and their standard versions are compared in vehicle detection application. Experimental results show that fuzzy approach is relatively more accurate than classical approach.
130 citations
TL;DR: The proposed MCA algorithm can achieve improved detection in abnormal retinal images and decrease false positive vessels in pathological regions compared to other methods and the robustness of the method in the presence of noise is shown via experimental result.
124 citations
TL;DR: In this paper, several textural feature groups of seeds images were examined to evaluate their efficacy in identification of nine common Iranian wheat seed varieties on the whole, 1080 gray scale images of bulk wheat seeds (120 images of each variety) were acquired at a stable illumination condition (florescent ring light).
94 citations
TL;DR: A novel structural and automated method is presented for artery/vein classification of blood vessels in retinal images and potentially allows for further investigation of labels of thinner arteries and veins which might be found by tracing them back to the major vessels.
93 citations
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TL;DR: A comprehensive survey of different systems for fall detection and their underlying algorithms is given, divided into three main categories: wearable device based, ambience device based and vision based.
777 citations
Book•
01 Jan 1982
TL;DR: "Graefe's Archive" is a distinguished international journal that presents original clinical reports and clinically relevant experimental studies and provides rapid dissemination of clinical and clinically related experimental information.
Abstract: "Graefe's Archive" is a distinguished international journal that presents original clinical reports and clinically relevant experimental studies. Founded in 1854 by Albrecht von Graefe to serve as a source of useful clinical information and a stimulus for discussion, the journal has published articles by leading ophthalmologists and vision research scientists for more than a century. With peer review by an international Editorial Board and prompt English-language publication, "Graefe's Archive" provides rapid dissemination of clinical and clinically related experimental information.
750 citations
TL;DR: This work analyzes the wireless signal propagation model considering human activities influence and proposes a novel and truly unobtrusive detection method based on the advanced wireless technologies, which it is called as WiFall, which withdraws the need for hardware modification, environmental setup and worn or taken devices.
Abstract: Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.
686 citations
TL;DR: The purpose of this paper is to provide a complete survey of the traditional and recent approaches to background modeling for foreground detection, and categorize the different approaches in terms of the mathematical models used.
664 citations