Majlesi Journal of Multimedia Processing
Volume:1 Issue: 2, Jun 2012

In this paper we present a novel method based on path normalization for classification in the traffic surveillance videos. Extracting the low level feature vectors in various sizes and recording all spatial-temporal information without fixed sampling rate are the main reason in this normalization. The normalized feature vectors are used for unsupervised learning and since most people of society have legal traffic behaviors system can extract the necessary knowledge automatically to detect illegal behavior. In the proposed structure, decision making for these behaviors is based on spatial-temporal features. The experimental results show high accuracy in trajectories classification using path normalization.

Features selection is one of the issues addressed in pattern recognition. By informed choice of effective features on increasing recognition rate among the overall features extracted from, the computational costs can be reduced and deducing unnecessary features are avoided. In this article, the Binary Particle Swarm Optimization (BPSO) algorithm and Binary Genetic Algorithm (BGA), both are population based algorithm series are used to find the best groups of fuzzy recognitions features of handwritten digits. Also In this article, real version of PSO (RPSO) has been used in different way to improve recognition rate. In this method, instead of choosing some of the features, one random weight has been assigned to each feature. Indeed, feature vector has been multiplied in weight vector to obtain a new feature vector. This Weight vector is obtained with RPSO. After several iterations, RPSO algorithm determines Weight set of features so that Classification accuracy increases. Fitness function in these algorithms is the number of fuzzy classifier errors and the aim is to make this value minimum. The obtained results confirmed that the population based algorithm with reducing the number of features and increasing the Rate of recognition have proper performance.

The Universal Mobile Telecommunication System (UMTS) security architecture is acknowledged as an example of the principle of ‘good enough’ security, because of the right balance among cost-effectiveness, security and usability, but UMTS has weaknesses that can lead to security incidents in the multimedia applications. In this paper, we study the security architecture of UMTS network and the structure of Generic Authentication Architecture (GAA) as a security service. Then the vulnerabilities of both UMTS security architecture as well as GAA are investigated to present the Denial of Service (DoS) attack scenarios. These vulnerabilities can be used by an attacker to trig DoS attack scenarios. The attack scenarios in UMTS and GAA are based on signaling attacks. Finally, DoS scenarios are applied to a web shopping plan which has used GAA.

The system presented here uses a minimum of prior knowledge to retrieve trajectories and abnormal behaviour. This system is based on the premise that most members of society choose to comply with traffic laws. The system input is only two parameters, namely (1) the maximum number of junctions available and (2) the rate of infractions in the city. In an unsupervised manner, the new system is capable of learning all legal behaviour and, on its own, extracting the knowledge required for detection of illegal behaviour. The query model used in this system is based on keywords and user sketches. Keywords are labels applied automatically by the system to express the activity models. The proposed system also optimizes user sketches before implementation. Practical implementations have demonstrated the high efficiency of this system in learning legal behaviour and detecting illegal practices.

Straight lines can fit to a system with several methods which present properties of the system. For a multi criteria system using these methods like least mean square and mean incision, cannot get the appropriate answer. In this article, we present a new method that begins with the calculation of the system error. Then, if the calculated error is much more than a specific value, therefore the system supposed to be a multi criteria system. Here in this case, we perform a classification between data of the system by using minimum distance techniques. Finally, each part of the classified data fits with the straight lines in order to reduction of the error. In this paper, final results describe by several examples of its superior application in edge detection in the image processing.

Time series forecasting has been applied to different applications. Artificial neural networks (ANNs) have widely usage in developing time series forecasting models, too. In this paper, an evolutionary neural network model is proposed to improve the performance of ANNs in time series forecasting. In this way, the genetic algorithm (GA) is used to determine the optimum structure and parameters of ANN, such as the number of hidden nodes, the slope of nodes’ activation function, the values of learning rate and momentum coefficient in hidden and output layers, and the number of input features. To evaluate the effectiveness of the proposed model, foreign exchange rate (FOREX) prediction, as a benchmark application, is performed in this paper. Empirical results show that by using suitable operators for selection and crossover in GA, the mean squared error (MSE) of the proposed evolutionary-connectionist hybrid model reaches 0.0010, which is a better performance compared to some other algorithms.

Image segmentation is an effect method in image processing which input is digital images and output is our favorite feature extracted. Segmentation involves partitioning an image into groups of pixels which are homogeneous with respect to some criterion. Different groups must not intersect each other and adjacent groups must be heterogeneous. The rate of accuracy of this technique, is depend to identify of our special region in image. In many proposed algorithm, difference in intensity and choose proportionate threshold, play a pivotal role for segmentation. In this paper, classification is based sudden change of intensity and dividing similarities, can directly affect other post processing, such as image analysis and feature extraction. The main advantage of our proposed algorithm is sampling in analysis in image. This technique can be used in many industrial, weather, medical and agriculture applications.