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This paper presents a novel iterative Bayesian algorithm, Block Iterative Bayesian Algorithm (Block-IBA), for reconstructing block-sparse signals with unknown block structures. Unlike the other existing algorithms for block sparse signal recovery which assume the cluster structure of the non-zero elements of the unknown signal to be independent and identically distributed (i.i.d.), we use a more realistic...
Analysis of crowd behaviour in public places is an indispensable tool for video surveillance. Automated detection of anomalous crowd behaviour is a critical problem with the increase in human population. Anomalous events may include a person loitering about a place for unusual amounts of time; people running and causing panic; the size of a group of people growing over time etc. In this work, to detect...
Fuzzy clustering has been extensively used in brain magnetic resonance (MR) image segmentation. However, due to the existence of noise and intensity inhomogeneity, many segmentation algorithms suffer from limited accuracy. In this paper, we propose a fuzzy clustering algorithm via enhanced spatially constraint for brain MR image segmentation. A novel spatial factor is proposed by incorporating the...
Fuzzy clustering algorithms have been widely used in brain magnetic resonance (MR) image segmentation. However, due to the existence of noise and intensity inhomogeneity, many segmentation algorithms suffer from limited accuracy. In this paper, we propose a fuzzy clustering algorithm with robust spatially constraint for accurate and robust brain MR image segmentation. A novel spatial factor is proposed...
Hidden Markov models (HMMs) are widely employed in sequential data modeling both because they are capable of handling multivariate data of varying length, and because they capture the underlying hidden properties of time-series. Over the years, HMM-based clustering methods have been widely investigated and improved. However, their performance on noisy data and the effectiveness of similarity measure...
Many real world business processes are executed without explicit orchestration and hence do not generate structured execution logs. This is particularly true for the class of business processes which are executed in service delivery centers in emerging markets where rapid changes in processes and in the people executing the processes are common. In such environments, the process execution logs are...
Accurate segmentation of magnetic resonance images according to tissue type is widely studded by many researcher, Recently Markov Random Field (MRF) has been used in this area. However the original MRF is supervised. So we introduce a novel approach called Dirichlet Markov Random Field for Magnetic Resonance Image (MRI) brain tissue classification. The approach uses Dirchilet Process Mixture (DPM)...
The problem of identification of noise sources in the ocean is of prime importance because of its diverse practical applications. Hidden Markov Models provide an effective architecture for the classification of underwater noise sources. A technique for the estimation of State Transition Matrix for a twenty state Hidden Markov Model for the classification of noise sources in the ocean is presented...
We present a method to group trajectories of moving objects extracted from real-world surveillance videos. The trajectories are first mapped into a low dimensionality feature space generated through linear regression. Next the regression coefficients are clustered by a Gaussian mixture model initialized by K-means for improved efficiency. The model selection problem is solved with Bayesian information...
Sound is one of the few forms of energy that will propagate reliably underwater, and for this reason it is used by aquatic animals for navigation and communication. Man also uses sound for the same reasons underwater, and additionally generates noise as a byproduct of offshore activity. The problem of identification of noise sources in the ocean is of great importance because of its varied practical...
This paper proposes a novel hierarchical speaker identification method to save the speaker identification and training time, viz. First is to get a coarse decision by a fast scan all registered speakers using PCA classifier to found M possible target speakers; then is to get a final decision by the proposed Multi-Reduced Support Vector Machine (MRSVM). And the MRSVM has two reduction steps to reduce...
Recognizing links between offender patterns is one of the most crucial skills of an investigator. Early recognition of similar patterns can lead to focusing resources, improving clearance rates, and ultimately saving lives in terms of digital forensics. In this paper we propose a forensics methodology using Markov chain during a given time interval for tracking and predicting the degree of criminal...
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