ABSTRACT This paper a new proposes Algorithm to overcome the drawbacks of the generalized Hough transform, namely its computational complexity and storage requirement. For decreasing the Computation time, the algorithm performs the Hough transform by (1) decomposing an image into small blocks, (2) estimating line parameters through least-squares line fitting for each block, and (3) removing detected lines while performing the Hough transform for the remaining blocks. For reducing the memory storage, the proposed algorithm utilizes a data structure, list to represent accumulators. The method significantly reduces the computational complexity and storage required to evaluate all possible parameters without using a accumulator array. Experimental results are given to show the correctness and effectiveness of the proposed method.

The core of artificial intelligence and machine learning is to get computers to solve problems automatically. One of the great tools that attempt to achieve that goal is Genetic Programming (GP). As alternatives to GP, Scatter Programming (SP) is proposed in this paper. One of the main features of SP is to exploit local search in order to overcome some recently addressed drawbacks of GP, especially its highly disruption of its main operations; crossover and mutation. This work shows that SP has promising performance and results in solving machine learning problems.

The core of artificial intelligence and machine learning is to get computers to solve problems automatically. One of the great tools that attempt to achieve that goal is Genetic Programming (GP). As alternatives to GP, Scatter Programming (SP) is proposed in this paper. One of the main features of SP is to exploit local search in order to overcome some recently addressed drawbacks of GP, especially its highly disruption of its main operations; crossover and mutation. This work shows that SP has promising performance and results in solving machine learning problems.

Abstract Augmenting deformable surfaces like cloth and body in real video is a challenging task. This paper presents a system for cloth and body augmentation in a single-view video. The system allows users to change their cloth either by changing the color, the texture, or the whole cloth. It augments the user with virtual clothes. As a result, users can enjoy changing their cloth with any other cloth they want. As a prerequisite, the user needs to wear a special suit and enters through our motion capture system that captures the movements of the user. From the captured data, an animated 3D character model is created, which will serve as the new body. The model is rendered with the new cloth but without the head. We extract the real face of the user and place it on the virtual model. This system can be used in film production and advertisement.

Abstract Augmenting deformable surfaces like cloth and body in real video is a challenging task. This paper presents a system for cloth and body augmentation in a single-view video. The system allows users to change their cloth either by changing the color, the texture, or the whole cloth. It augments the user with virtual clothes. As a result, users can enjoy changing their cloth with any other cloth they want. As a prerequisite, the user needs to wear a special suit and enters through our motion capture system that captures the movements of the user. From the captured data, an animated 3D character model is created, which will serve as the new body. The model is rendered with the new cloth but without the head. We extract the real face of the user and place it on the virtual model. This system can be used in film production and advertisement.

Abstract Augmenting deformable surfaces like cloth and body in real video is a challenging task. This paper presents a system for cloth and body augmentation in a single-view video. The system allows users to change their cloth either by changing the color, the texture, or the whole cloth. It augments the user with virtual clothes. As a result, users can enjoy changing their cloth with any other cloth they want. As a prerequisite, the user needs to wear a special suit and enters through our motion capture system that captures the movements of the user. From the captured data, an animated 3D character model is created, which will serve as the new body. The model is rendered with the new cloth but without the head. We extract the real face of the user and place it on the virtual model. This system can be used in film production and advertisement.

The term "search engine” is traditionally used to refer to crawler based search engines, manually maintained directories, and hybrid search engines. However, current search engines do not fully satisfy the users' needs especially in terms of accuracy and specificity of the results. This thesis proposes an intelligent searchagent system on top of the Semantic Web. The Semantic web is an extension of the current web that will enable web resources to become machine understandable through the use of meta-data. Many languages have been developed for representing metadata in the semantic web such as DAML, OIL, and OWL. The process of adding metadata to the original data is called the annotation process. The proposed system consists of five main parts: the Annotator, the Ontology Parser, the Indexer, the Search Agent, and the Data Repository. Two kinds of search are implemented: keyword/concept based and structured search. The keyword/concept based search matches a user’s query terms to concepts while the structured search allows a user to choose the concept that s/he want to search for together with some attributes for this concept. The proposed system was evaluated using the precision measure. The evaluation is based on the comparison of the proposed system with the Lucene desktop application Evaluation results show that the proposed system’s two search models (keyword /concept based and structured search) achieve better results than Lucene..

Abstract. Steganography is one of the branches of information security field, it aims to hide information in unremarkable cover media so as not to arouse an eavesdropper’s suspicion. The secret message is hidden in such a way that no significant degradation can be detected in the quality of the original image. The aim of this paper is to introduce an efficient steganographic scheme to hide data over gray scale images. This scheme is based on the property of the human eye, which is more sensitive to the change in the smooth area than the edge area using pixel value difference, besides employing the LSB substitution technique as a fundamental stage. The experimental results show that the proposed method could successfully achieve the goals of the high embedding capacity and maintaining the visual quality, in addition, provides more secure data hiding using selective pixel positions determined by a secret image (i.e. key). Moreover, based on that, the secret message is replaced with dynamic LSBs, our scheme can effectively resist several image steganalysis techniques.

Abstract. The least significant bit (LSB) embedding method is one of the most commonly used techniques; it targets the LSB's of the host image to hide the data. This paper deals with three main steganography challenges (i.e. capacity, imperceptibility, and security). This is achieved by hybrid data hiding scheme incorporates LSB technique with a key- permutation method. The paper also proposes an optimal key permutation method using genetic algorithms for best key selection. Both normal and optimized methods are tested with standard images, varying both data size as well as key space. Final experimental results show decrement in computation time when increasing number of keys, at the same time system security improves.

Abstract. The big challenge in discovering association rules is to find the largest frequent itemsets. Sequential algorithms do not have analytical ability, especially in terms of run-time performance, for such very large databases. Therefore, we must rely on high performance parallel and distributed computing. We present a new parallel algorithm for frequent itemset mining, called HoriVertical algorithm. The algorithm passes the database only one time and starts a new stage with the finished itemsets while some other itemsets in the same stage have not been finished yet. Also, the new algorithm is based on partitioning the database vertically and horizontally. We present the result on the performance of our algorithm on various databases, and compare it against well-known algorithms.