انجمن کامپیوتر ایران

In this paper, we present a novel hybrid algorithm for binarization of badly illuminated document images. This algorithm locally enhances the document image and makes the gray levels of text and background pixels separable. Afterward a simple global binarization algorithm binarizes the enhanced image. The enhancement process is a novel method that uses a separate transformation function to map the gray level of each pixel into a new domain. For each pixel, the transformation function is determined using its neighboring pixels gray level. The proposed binarization algorithm is robust for wide variety of degraded document images. Evaluation over a set of degraded document images illustrates the effectiveness of our proposed binarization algorithm.

In real world, optimization problems are usually dynamic in which local optima of the problem change. Hence, in these optimization problems goal is not only to find global optimum but also to track its changes. In this paper, we propose a variant of cellular PSO, a new hybrid model of particle swarm optimization and cellular automata, which addresses dynamic optimization. In the proposed model, population is split among cells of cellular automata embedded in the search space. Each cell of cellular automata can contain a specified number of particles in order to keep the diversity of swarm. Moreover, we utilize the exploration capability of quantum particles in order to find position of new local optima quickly. To do so, after a change in environment is detected, some of the particles in the cell change their role from standard particles to quantum for few iterations. Experimental results on moving peaks benchmark show that the proposed algorithm outperforms mQSO, a well-known multi swarm model for dynamic optimization, in many environments.

Though numerous approaches have been proposed for face recognition. In this paper we propose a novel face recognition approach based on adaptively weighted patch Local Statistic in Multi dimensional (LMDS) when only one exemplar image per person is available. In this approach, a face image is decomposed into a set of equal-sized patches in a nonoverlapping way. In order to obtain Local Multi Dimensional Statistic Features in each patch, we calculated mean and standard deviation of all pixels along some directions. An adaptively weighting scheme is used to assign proper weights to each LMDS features to adjust the contribution of each local area of a face in terms of the quantity of identity information that a patch contains. An extensive experimental investigation is conducted using AR face databases covering face recognition under controlled/ideal conditions and different facial expressions. The system performance is compared with the performance of four benchmark approaches. The encouraging experimental results demonstrate that our approach can be used for face recognition and patch-based local statistic features provides a novel way for face.

Reversible Cellular Automata is applicable in cryptographic functions. A reversible CA can be obtained by employing reversible rules. In this paper, we propose 65280 two-dimensional reversible CA rules by compounding one-dimensional reversible rules. We produce these rules by alternately applying onedimensional CA rule f1 to the rows and applying another rule f2 to the columns of the configuration matrix of twodimensional CA. In addition to describing these rules, we consider the required cryptographic properties such as completeness, strict avalanche criteria, non-linearity, and differential-profile flatness for our proposed approach. According to the obtained results, 9463 rules are specified as appropriate rules for the purpose of applying in cryptographic functions. Only 510 rules do not satisfy any of the mentioned cryptographic properties and the other remain rules satisfy some of the properties.