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Full-Text Articles in Physical Sciences and Mathematics
A Short Length Window-Based Method For Islanding Detection In Distributed Generation, Mollah Alam, Kashem Muttaqi, Abdesselam Bouzerdoum
A Short Length Window-Based Method For Islanding Detection In Distributed Generation, Mollah Alam, Kashem Muttaqi, Abdesselam Bouzerdoum
Professor Salim Bouzerdoum
Distributed generation (DG) has recently drawn the interest to meet the increased load demand with minimum investment. But cohesive operation of these DG sources, in a grid-connected environment, gives rise to several issues during abnormal conditions of the utility system. This paper addresses the detection method of one such crucial event which is “islanding”. A short length window based Mahalanobis Distance method has been proposed in this paper to detect islanding. A trade-off between computational time and accuracy has been maintained to make it reliable and acceptable. In this method, network parameters such as rate of change of frequency (ROCOF), …
A Car Detection System Based On Hierarchical Visual Features, Fok Hing Chi Tivive, Abdesselam Bouzerdoum
A Car Detection System Based On Hierarchical Visual Features, Fok Hing Chi Tivive, Abdesselam Bouzerdoum
Professor Salim Bouzerdoum
In this paper, we address the problem of detecting and localizing cars in still images. The proposed car detection system is based on a hierarchical feature detector in which the processing units are shunting inhibitory neurons. To reduce the training time and complexity of the network, the shunting inhibitory neurons in the first layer are implemented as directional nonlinear filters, whereas the neurons in the second layer have trainable parameters. A multi-resolution processing scheme is implemented so as to detect cars of different sizes, and to reduce the number of false positives during the detection stage, an adaptive thresholding strategy …
Markov Random Fields For Abnormal Behavior Detection On Highways, Son Lam Phung, Philippe L. Bouttefroy, Abdesselam Bouzerdoum, Azeddine Beghdadi
Markov Random Fields For Abnormal Behavior Detection On Highways, Son Lam Phung, Philippe L. Bouttefroy, Abdesselam Bouzerdoum, Azeddine Beghdadi
Professor Salim Bouzerdoum
This paper introduces a new paradigm for abnormal behavior detection relying on the integration of contextual information in Markov random fields. Contrary to traditional methods, the proposed technique models the local density of object feature vector, therefore leading to simple and elegant criterion for behavior classification. We develop a Gaussian Markov random field mixture catering for multi-modal density and integrating the neighborhood behavior into a local estimate. The convergence of the random field is ensured by online learning through a stochastic clustering algorithm. The system is tested on an extensive dataset (over 2800 vehicles) for behavior modeling. The experimental results …
Automatic Left Ventricle Detection In Echocardiographic Images For Deformable Contour Initialization, Cher Hau Seng, Ramazan Demirli, Moeness G. Amin, Jason L. Seachrist, Abdesselam Bouzerdoum
Automatic Left Ventricle Detection In Echocardiographic Images For Deformable Contour Initialization, Cher Hau Seng, Ramazan Demirli, Moeness G. Amin, Jason L. Seachrist, Abdesselam Bouzerdoum
Professor Salim Bouzerdoum
The accurate left ventricular boundary detection in echocardiographic images allow cardiologists to study and assess cardiomyopathy in patients. Due to the tedious and time consuming manner of manually tracing the borders, deformable models are generally used for left ventricle segmentations. However, most deformable models require a good initialization, which is usually outlined manually by the user. In this paper, we propose an automated left ventricle detection method for two-dimensional echocardiographic images that could serve as an initialization for deformable models. The proposed approach consists of pre-processing and post-processing stages, coupled with the watershed segmentation. The pre-processing stage enhances the overall …
Reduced Training Of Convolutional Neural Networks For Pedestrian Detection, Giang Hoang Nguyen, Son Lam Phung, Abdesselam Bouzerdoum
Reduced Training Of Convolutional Neural Networks For Pedestrian Detection, Giang Hoang Nguyen, Son Lam Phung, Abdesselam Bouzerdoum
Professor Salim Bouzerdoum
Pedestrian detection is a vision task with many practical applications in video surveillance, road safety, autonomous driving and military. However, it is much more difficult compared to the detection of other visual objects, because of the tremendous variations in the inner region as well as the outer shape of the pedestrian pattern. In this paper, we propose a pedestrian detection approach that uses convolutional neural network (CNN) to differentiate pedestrian and non-pedestrian patterns. Among several advantages, the CNN integrates feature extraction and classification into one single, fully adaptive structure. It can extract two-dimensional features at increasing scales, and it is …