Sharif Digital Repository

In this paper, we propose a new method for automatic news event detection. An event is a specific happening in a particular time and place. We propose a new model in this paper to detect news events using a label based clustering approach. The model takes advantage of the fact that news events are news clusters with high internal similarity whose articles are about an event in a specific time and place. Since news articles about a particular event may appear in several consecutive days, we developed this model to be able to distinguish such events and merge the corresponding news articles. Although event detection is propounded as a stand alone news mining task, it has also applications in... 

Increasing use of commercial off-the-shelf (COTS) superscalar processors in industrial, embedded, and real-time systems necessitates the development of error detection mechanisms for such systems. This paper presents an error detection scheme called Committed Instructions Counting (CIC) to increase error detection in such systems. The scheme uses internal Performance Monitoring features and an external watchdog processor (WDP). The Performance Monitoring features enable counting the number of committed instructions in a program. The scheme is experimentally evaluated on a 32-bit Pentium® processor using software implemented fault injection (SWIFI). A total of 8181 errors were injected into... 

Sport video shot classification is a basic step in the sport video processing. For many purposes such as event detection and summarization, shot classification is needed for content filtering. In this paper, we present a new method for soccer video shot classification. At first, in-field and out-of-field frames are separated. In in-field frames three features based on number of connected components and shirt color percent in vertical and horizontal strips are extracted. The features are all new and showed excellent discrimination in the feature space. These features are given to SVM for classifying long, medium and close-up shots. One of the advantages of our method is that, close-ups can be... 

Identification of voltage and current disturbances is an important task in power system monitoring and protection. In this paper, a new algorithm for online characterization of a wide range of voltage disturbances based on two dimensional wavelet transformation is proposed. This algorithm is more complicated than algorithms based on one dimensional wavelet transformation, but it's more precise and is useful for steady state disturbances, transients with slow variations and transients with rapid changes. After each five cycles, a matrix is formed based on the last fourteen cycles, in a way that the voltage signal in one cycle forms one row of the matrix. Then, the resulted image is decomposed... 

Objective. Sleep apnea is a serious respiratory disorder, which is associated with increased risk factors for cardiovascular disease. Many studies in recent years have been focused on automatic detection of sleep apnea from polysomnography (PSG) recordings, however, detection of subtle respiratory events named Respiratory Event Related Arousals (RERAs) that do not meet the criteria for apnea or hypopnea is still challenging. The objective of this study was to develop automatic detection of sleep apnea based on Hidden Markov Models (HMMs) which are probabilistic models with the ability to learn different dynamics of the real time-series such as clinical recordings. Approach. In this study, a... 

In visual surveillance, detecting and localising abnormal events are of great interest. In this study, an unsupervised method is proposed to automatically discover abnormal events occurring in traffic videos. For learning typical motion patterns occurring in such videos, a group sparse topical coding (GSTC) framework and an improved version of it are applied to optical flow features extracted from video clips. Then a very simple and efficient algorithm is proposed for GSTC. It is shown that discovered motion patterns can be employed directly in detecting abnormal events. A variety of abnormality metrics based on the resulting sparse codes for detection of abnormality are investigated.... 

Motion patterns in traffic video can be directly exploited to generate high-level descriptions of video content, which can be used for rule mining and abnormal event detection. The most recent and successful unsupervised methods for complex traffic scene analysis are based on topic models. In this paper, a topic related sparse topical coding framework is proposed for more effectively discovering motion patterns in traffic videos. © 2001-2011 IEEE  

We propose an event based approach for locating keyframes in natural video through detection of locally correlated spectral targets. Temporal Decomposition (TD) is used to describe a set of spectral parameters of the video as a linear combination of a set of temporally overlapping event functions. This process provides preliminary information about keyframes, by selecting the frames located at event centroids as the keyframe candidates. No shot or shot cluster boundary detection is needed and keyframes are extracted directly from among event centroids that are much smaller in number than the number of frames. Generalized Gaussian Density (GGD) parameters, extracted from 2D wavelet transform... 

Analyzing motion patterns in traffic videos can directly generate some high-level descriptions of the video content which can be further employed in rule mining and abnormal event detection. The most recent and successful unsupervised approaches for complex traffic scene analysis are based on topic models. However, most existing topic models share some key characteristics which could limit their utility. In this paper, based on extracted optical flow features from video clips, we employ Sparse Topical Coding (STC) framework to automatically discover typical motion patterns in traffic scenes. For this purpose, we improve the STC to overcome one of the drawbacks of topic models with the aim of... 

This paper is focused on video event recognition based on frame level CNN descriptors. Using transfer learning, the image trained descriptors are applied to the video domain to make event recognition feasible in scenarios with limited computational resources. After fine-tuning of the existing Convolutional Neural Network (CNN) concept score extractors, pre-trained on ImageNet, the output descriptors of the different fully connected layers are employed as frame descriptors. The resulting descriptors are hierarchically post-processed and combined with novel and efficient pooling and normalization methods. As major contributions of this work to the video event recognition, we present a... 

This paper is focused on video event recognition based on frame level convolutional neural network (CNN) descriptors. Using transfer learning, the image trained descriptors are applied to the video domain to make event recognition feasible in scenarios with limited computational resources. After fine-tuning of the existing CNN concept score extractors, pretrained on ImageNet, the output descriptors of the different fully connected layers are employed as frame descriptors. The resulting descriptors are hierarchically postprocessed and combined with novel and efficient pooling and normalization methods. As major contributions of this paper to the video event recognition, we present a... 

This paper is focused on video event recognition based on frame level convolutional neural network (CNN) descriptors. Using transfer learning, the image trained descriptors are applied to the video domain to make event recognition feasible in scenarios with limited computational resources. After fine-tuning of the existing CNN concept score extractors, pretrained on ImageNet, the output descriptors of the different fully connected layers are employed as frame descriptors. The resulting descriptors are hierarchically postprocessed and combined with novel and efficient pooling and normalization methods. As major contributions of this paper to the video event recognition, we present a...