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Author

Mohsen Soryani

Other affiliations: Heriot-Watt University
Bio: Mohsen Soryani is an academic researcher from Iran University of Science and Technology. The author has contributed to research in topics: Motion compensation & Video tracking. The author has an hindex of 14, co-authored 89 publications receiving 870 citations. Previous affiliations of Mohsen Soryani include Heriot-Watt University.


Papers
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Journal ArticleDOI
TL;DR: A new approach is introduced for driver hypovigilance (fatigue and distraction) detection based on the symptoms related to face and eye regions that is relatively efficient for estimating the driver fatigue and distraction.
Abstract: Driver face monitoring system is a real-time system that can detect driver fatigue and distraction using machine vision approaches. In this paper, a new approach is introduced for driver hypovigilance (fatigue and distraction) detection based on the symptoms related to face and eye regions. In this method, face template matching and horizontal projection of top-half segment of face image are used to extract hypovigilance symptoms from face and eye, respectively. Head rotation is a symptom to detect distraction that is extracted from face region. The extracted symptoms from eye region are (1) percentage of eye closure, (2) eyelid distance changes with respect to the normal eyelid distance, and (3) eye closure rate. The first and second symptoms related to eye region are used for fatigue detection; the last one is used for distraction detection. In the proposed system, a fuzzy expert system combines the symptoms to estimate level of driver hypo-vigilance. There are three main contributions in the introduced method: (1) simple and efficient head rotation detection based on face template matching, (2) adaptive symptom extraction from eye region without explicit eye detection, and (3) normalizing and personalizing the extracted symptoms using a short training phase. These three contributions lead to develop an adaptive driver eye/face monitoring. Experiments show that the proposed system is relatively efficient for estimating the driver fatigue and distraction.

120 citations

Journal ArticleDOI
TL;DR: A comprehensive review on driver face monitoring systems for fatigue and distraction detection and accident prevention is presented.
Abstract: Every year, many car accidents due to driver fatigue and distraction occur around the world and cause many casualties and injuries. Driver face monitoring systems is one of the main approaches for driver fatigue or distraction detection and accident prevention. Driver face monitoring systems capture the images from driver face and extract the symptoms of fatigue and distraction from eyes, mouth and head. These symptoms are usually percentage of eyelid closure over time (PERCLOS), eyelid distance, eye blink rate, blink speed, gaze direction, eye saccadic movement, yawning, head nodding and head orientation. The system estimates driver alertness based on extracted symptoms and alarms if needed. In this paper, after an introduction to driver face monitoring systems, the general structure of these systems is discussed. Then a comprehensive review on driver face monitoring systems for fatigue and distraction detection is presented.

92 citations

Journal ArticleDOI
31 Mar 2011
TL;DR: A new method for non-blind image watermarking that is robust against affine transformation and ordinary image manipulation is presented and higher performance of the proposed method in comparison with the DWT-SVD method is shown.
Abstract: In this paper, a new method for non-blind image watermarking that is robust against affine transformation and ordinary image manipulation is presented. The suggested method presents a watermarking scheme based on redundant discrete wavelet transform and Singular Value Decomposition. After applying RDWT to both cover and watermark images, we apply SVD to the LL subbands of them. We then modify singular values of the cover image using singular values of the visual watermark. The advantage of the proposed technique is its robustness against most common attacks. Analysis and experimental results show higher performance of the proposed method in comparison with the DWT-SVD method.

84 citations

Journal ArticleDOI
TL;DR: An efficient method for detection of masses in mammograms is implemented and according to FROC analysis, the mass detection algorithm outperforms other competing methods.
Abstract: Context: Mammography is the most effective procedure for an early detection of the breast abnormalities. Masses are a type of abnormality, which are very difficult to be visually detected on mammograms. Aims: In this paper an efficient method for detection of masses in mammograms is implemented. Settings and Design: The proposed mass detector consists of two major steps. In the first step, several suspicious regions are extracted from the mammograms using an adaptive thresholding technique. In the second step, false positives originating by the previous stage are reduced by a machine learning approach. Materials and Methods: All modules of the mass detector were assessed on mini-MIAS database. In addition, the algorithm was tested on INBreast database for more validation. Results: According to FROC analysis, our mass detection algorithm outperforms other competing methods. Conclusions: We should not just insist on sensitivity in the segmentation phase because if we forgot FP rate, and our goal was just higher sensitivity, then the learning algorithm would be biased more toward false positives and the sensitivity would decrease dramatically in the false positive reduction phase. Therefore, we should consider the mass detection problem as a cost sensitive problem because misclassification costs are not the same in this type of problems.

64 citations

Proceedings ArticleDOI
15 Jun 2011
TL;DR: The proposed method presents a watermarking scheme based on redundant discrete wavelet transform and Singular Value Decomposition (SVD) that is robust against affine transformation and ordinary image manipulation.
Abstract: In this paper, a new method for non-blind image watermarking presented that is robust against affine transformation and ordinary image manipulation. The proposed method presents a watermarking scheme based on redundant discrete wavelet transform (RDWT) and Singular Value Decomposition (SVD). After applying RDWT to cover image, we apply SVD to each subband. Then we modify singular values of the cover image using singular values of the visual watermark. The advantage of the proposed technique is its robustness against most common attacks. Analysis and experimental results show higher performance of the proposed method in comparison to the DWT-SVD method.

56 citations


Cited by
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Proceedings Article
01 Jan 1994
TL;DR: The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images.
Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

2,134 citations

Dissertation
01 Jan 2002

570 citations

Journal ArticleDOI
TL;DR: Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter.
Abstract: Intersection management is one of the most challenging problems within the transport system. Traffic light-based methods have been efficient but are not able to deal with the growing mobility and social challenges. On the other hand, the advancements of automation and communications have enabled cooperative intersection management, where road users, infrastructure, and traffic control centers are able to communicate and coordinate the traffic safely and efficiently. Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter. Cooperative methods, including time slots and space reservation, trajectory planning, and virtual traffic lights, are discussed in detail. Vehicle collision warning and avoidance methods are discussed to deal with uncertainties. Concerning vulnerable road users, pedestrian collision avoidance methods are discussed. In addition, an introduction to major projects related to cooperative intersection management is presented. A further discussion of the presented works is given with highlights of future research topics. This paper serves as a comprehensive survey of the field, aiming at stimulating new methods and accelerating the advancement of automated and cooperative intersections.

408 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a survey of early-stage traffic control technologies and discuss potential benefits that will be gained by using vehicle-to-vehicle (V2V) communications.
Abstract: During the last 60 years, incessant efforts have been made to improve the efficiency of traffic control systems to meet ever-increasing traffic demands. Some recent works attempt to enhance traffic efficiency via vehicle-to-vehicle communications. In this paper, we aim to give a survey of some research frontiers in this trend, identifying early-stage key technologies and discussing potential benefits that will be gained. Our survey focuses on the control side and aims to highlight that the design philosophy for traffic control systems is undergoing a transition from feedback character to feedforward character. Moreover, we discuss some contrasting preferences in the design of traffic control systems and their relations to vehicular communications. The first pair of contrasting preferences are model-based predictive control versus simulation-based predictive control. The second pair are global planning-based control versus local self-organization-based control. The third pair are control using rich information that may be highly redundant versus control using concise information that is necessary. Both the potentials and drawbacks of these control strategies are explained. We hope these comparisons can shed some interesting light on future traffic control studies.

278 citations

Journal ArticleDOI
TL;DR: The results demonstrate that the proposed integrated CAD system, through all stages of detection, segmentation, and classification, outperforms the latest conventional deep learning methodologies.

270 citations