ABSTRACT It is important to produce 3D cloths for different body sizes from 2D patterns of any size. Moreover, classical patterns in old books and magazines are available only in hardcopy forms. Thus it is imperative to produce softcopies of those patterns in old books and magazines. Solutions for these two issues are provided in this research work. First automating the design of customized apparel products from pattern images is provided which can greatly improves the efficiency of cloth production in the apparel industry. Second generating different sizes patterns from a given pattern size. This greatly facilitate the generation of pattern design in the apparel industry.

ABSTRACT The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this article, we present an algorithm that provides outlier detection and data clustering simultaneously. The algorithmimprovesthe estimation of centroids of the generative distribution during the process of clustering and outlier discovery. The proposed algorithm consists of two stages. The first stage consists of improved genetic k-means algorithm (IGK) process, while the second stage iteratively removes the vectors which are far from their cluster centroids.

ABSTRACT The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this article, we present an algorithm that provides outlier detection and data clustering simultaneously. The algorithmimprovesthe estimation of centroids of the generative distribution during the process of clustering and outlier discovery. The proposed algorithm consists of two stages. The first stage consists of improved genetic k-means algorithm (IGK) process, while the second stage iteratively removes the vectors which are far from their cluster centroids.

ABSTRACT Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. However, it is known that the K-means algorithm may get stuck at suboptimal solutions, depending on the choice of the initial cluster centers. In this article, we propose a technique to handle large scale data, which can select initial clustering center purposefully using Genetic algorithms (GAs), reduce the sensitivity to isolated point, avoid dissevering big cluster, and overcome deflexion of data in some degree that caused by the disproportion in data partitioning owing to adoption of multi-sampling. We applied our method to some public datasets these show the advantages of the proposed approach for example Hepatitis C dataset that has been taken from the machine learning warehouse of University of California. Our aim is to evaluate hepatitis dataset. In order to evaluate this dataset we did some preprocessing operation, the reason to preprocessing is to summarize the data in the best and suitable way for our algorithm. Missing values of the instances are adjusted using local mean method.

ABSTRACT Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. However, it is known that the K-means algorithm may get stuck at suboptimal solutions, depending on the choice of the initial cluster centers. In this article, we propose a technique to handle large scale data, which can select initial clustering center purposefully using Genetic algorithms (GAs), reduce the sensitivity to isolated point, avoid dissevering big cluster, and overcome deflexion of data in some degree that caused by the disproportion in data partitioning owing to adoption of multi-sampling. We applied our method to some public datasets these show the advantages of the proposed approach for example Hepatitis C dataset that has been taken from the machine learning warehouse of University of California. Our aim is to evaluate hepatitis dataset. In order to evaluate this dataset we did some preprocessing operation, the reason to preprocessing is to summarize the data in the best and suitable way for our algorithm. Missing values of the instances are adjusted using local mean method.

Abstract.Through the use of a global geometric symmetry, detection ellipses are proposed in this paper. Based on the geometric symmetry, the proposed method first locates candidates of ellipses centers. In the meantime, according to these candidate centers, all feature points in an input image are grouped into several sub images. Then, for each sub image, by using geometric properties again, all ellipses are extracted. The method significantly reduces the time required to evaluate all possible parameters without using edge direction information. Experimental results are given to show the correctness and effectiveness of the proposed method.

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.