انجمن کامپیوتر ایران

In this paper we propose a new integration method for multi-feature object tracking in a particle filter framework. We divide particles into separate clusters. All particles within a cluster measure a specific feature. The number of particles within a cluster is in proportion to the reliability of associated feature. We do a compensation stage which neutralizes the effect of particles weights mean within a cluster. Compensation stage balances the concentration of particles around local maximal. So, particles are distributed more effectively in the scene. Proposed method provides both effective hypothesis generation and effective evaluation of hypothesis. Experimental results over a set of real-world sequences demonstrate better performance of our method compared to the common methods of feature integration.

This paper introduces a revisited hybrid algorithm for function approximation. In this paper, a simple and fast learning algorithm is proposed, which automates structure and parameter identification simultaneously based on input-target samples. First, without need of clustering, the initial structure of the network with the specified number of rules is established, and then a training process based on the error of other training samples is applied to obtain a more precision model. After the network structure is identified, an optimization learning, based on the criteria error, is performed to optimize the obtained parameter set of the premise parts and the consequent parts. At the end, comprehensive comparisons are made with other approaches to demonstrate that the proposed algorithm is superior in term of compact structure, convergence speed, memory usage and learning efficiency.

This paper estimates and segments the moving objects based on center of mass model to decrease the search window and to provide a new algorithm, which achieves an accurate and rapid tracking. Furthermore, a novel method is proposed to update the template size adaptively by using estimation and segmentation of moving objects. The estimated results of moving target is transformed to wavelet domain and target tracking is performed in that domain. To improve the algorithm center of mass model is performed in wavelet domain. By using kalman predictor and thresholding method, a new approach is presented for object tracking failure and recovery.

Human can determine optimal behaviors which depend on the ability to make planned and adaptive decisions. In this paper, we have proposed a predictive structure based on neuropsychological evidences to model human decision making process by concentrating on the role of frontal brain regions which are responsible for predictive control of human behavior. We have considered a model-based reinforcement learning framework to implement the relations between these brain areas. Finally, we have designed an experimental test to compare the function of model with human behavior in a maze task. Our results reveal that there is more than reward and punishment in human behavior, and considering higher cognitive functions such as prediction will help to have more reliable models which could better describe human behavior.