Firas A. Jassim
Management Information Systems Department, Irbid National University, Jordan.
Abstract— Image inpainting is the art of predicting damaged regions of an image. The manual way of image inpainting is a time consuming. Therefore, there must be an automatic digital method for image inpainting that recovers the image from the damaged regions. In this paper, a novel statistical image inpainting algorithm based on Kriging interpolation technique was proposed. Kriging technique automatically fills the damaged region in an image using the information available from its surrounding regions in such away that it uses the spatial correlation structure of points inside the kk block. Kriging has the ability to face the challenge of keeping the structure and texture information as the size of damaged region heighten. Experimental results showed that, Kriging has a high PSNR value when recovering a variety of test images from scratches and text as damaged regions.
Keywords-image inpainting; image masking; Kriging; text removal; scratch removal.
Nidhi Gupta, USICT, GGSIPU, India.
R.L.Ujjwal, USICT, GGSIPU, India.
Abstract— Clustering is process of grouping data objects into distinct clusters so that data in the same cluster are similar. The most popular clustering algorithm used is the K-means algorithm, which is a partitioning algorithm. Unsupervised techniques like clustering may be used for fault prediction in software modules. This paper describes the standard k-means algorithm and analyzes the shortcomings of standard k-means algorithm. This paper proposes an incremental clustering algorithm. Experimental results show that the proposed algorithm produces clusters in less computation time.
Keywords – Clustering; Incremental Clustering; K-means; Unsupervised; Partitioning; Data Objects.
Taisir Eldos, Aws Kanan, Abdullah Aljumah
Department Of Computer Engineering, College of Computer Engineering and Sciences, Salman in Abdulaziz University, Saudi Arabia.
Abstract— Printed Circuit Board (PCB) manufacturing depends on the holes drilling time, which is a function of the number of holes and the order in which they are drilled. A typical PCB may have hundreds of holes and optimizing the time to complete the drilling plays a role in the production rate. At an early stage of the manufacturing process, a numerically controlled drill has to move its bit over the holes one by one and must complete the job in minimal time. The order by which the holes are visited is of great significance in this case. Solving the TSP leads to minimizing the time to drill the holes on a PCB. Finding an optimal solution to the TSP may be prohibitively large as the number of possibilities to evaluate in an exact search is (n-1)!/2 for n-hole PCB. There exist too many algorithms to solve the TSP in an engineering sense; semi-optimal solution, with good quality and cost tradeoff. Starting with Greedy Algorithm which delivers a fast solution at the risk of being low in quality, to the evolutionary algorithms like Genetic algorithms, Simulated Annealing Algorithms, Ant Colony, Swarm Particle Optimization, and others which promise better solutions at the price of more search time. We propose an Ant Colony Optimization (ACO) algorithm with problem-specific heuristics like making use of the dispersed locales, to guide the search for the next move. Hence, making smarter balance between the exploration and exploitation leading to better quality for the same cost or less cost for the same quality. This will also offer a better way of problem partitioning which leads to better parallelization when more processing power is to be used to deliver the solution even faster.
Keywords – Ant Colony; Optimization Algorithm; Printed Circuits Board Drilling;Traveling Salesman.