انجمن کامپیوتر ایران

A hybrid feature and decision level information fusion architecture is proposed for human emotion recognition from facial expression and speech prosody. An active buffer stores the most recent information extracted from face and speech. This buffer allows fusion of asynchronous information through keeping track of individual modality updates. The contents of the buffer will be fused at feature level; if their respective update times are close to each other. Based on the classifiers’ reliability, a decision level fusion block combines results of the unimodal speech and face based systems and the feature level fusion based classifier. Experimental results on a database of 12 people show that the proposed fusion architecture performs better than unimodal classification, pure feature level fusion and decision level fusion.

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 presents a new segmentation method for color images. It relies on soft and hard segmentation processes. In the soft segmentation process, a cellular learning automata analyzes the input image and closes together the pixels that are enclosed in each region to generate a soft segmented image. Adjacency and texture information are encountered in the soft segmentation stage. Soft segmented image is then fed to the hard segmentation process to generate the final segmentation result. As the proposed method is based on CLA it can adapt to its environment after some iterations. This adaptive behavior leads to a semi content-based segmentation process that performs well even in presence of noise. Experimental results show the effectiveness of the proposed segmentation method.

Object tracking is one of the major subjects in machine vision and plays a main role in detection of major events in indoor soccer matches. In this paper, a novel approach for tracking the ball and players is proposed. In this method, the ground lines are segmented and eliminated using a fast and effective method. Then, the remaining non-field pixels are considered and labeled as players and the ball. A fast level set contour is used to track players and the ball. The proposed method can track players and the ball in presence of occlusion. Experiments show that the proposed method is robust to occlusion and different field colors.

In this paper, we introduce a refinement approach to develop probabilistic programs formally. To achieve this goal, we first present a way to specify probabilistic programs in a Z-based notation. We then use an existing method of translating Z into a refinement calculus to transform our Z-style specifications of probabilistic programs into specification statements of the refinement calculus. We finally add new laws to the refinement calculus helping us to refine the resulting specification statements into probabilistic choice constructs of a probabilistic imperative language. In this way, we will provide a completely formal way for developing probabilistic programs.