No abstract

No abstract

This paper, describes Concept of Big Data which is collection of large data set that cannot be proceed by traditional computational techniques. Therefore Hadoop technology designed to process Big Data. Hadoop is the platform in businesses for Big Data processing. Hadoop is an open source, Java-based programming framework which supports the processing and storage of extremely large data sets in a distributed computing environment. It helps Big Data analytics by overcoming the difficulties that are usually faced in handling Big Data. Hadoop can break down large computational problems into smaller tasks as smaller elements can be analyzed economically and quickly [1].Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for various kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks. All these parts are analyzed in parallel and the results of the analysis are regrouped to produce the final output.

No abstract

Hand gestures are an important modality for human computer interaction (HCI) [1]. Compared to many existing interfaces, hand gestures have the advantages of being easy to use, natural, and intuitive. Successful applications of hand gesture recognition include computer games control [2], human-robot interaction [3], and sign language recognition [4], to name a few. Vision-based recognition systems can give computers the capability of understanding and responding to hand gestures. The usability of such systems greatly depends on their ability to function reliably in common real-world environments, without requiring the user to wear special clothes or cumbersome devices such as colored markers or gloves [4]. The aim of this technique is the proposal of a real time vision system for its application within visual interaction environments through hand gesture recognition, using general-purpose hardware and low cost sensors, like a simple personal computer and an USB web cam, so any user could make use of it in his office or home. The basis of our approach is a fast segmentation process to obtain the moving hand from the whole image, which is able to deal with a large number of hand shapes against different backgrounds and lighting conditions, and a recognition process that identifies the hand posture from the temporal sequence of segmented hands. The use of a visual memory (Stored database) allows the system to handle variations within a gesture and speed up the recognition process through the storage of different variables related to each gesture. A hierarchical gesture recognition algorithm is introduced to recognize a large number of gestures. Three stages of the proposed algorithm are based on a new hand tracking technique to recognize the actual beginning of a gesture using a Kalman filtering process, hidden Markov models and graph matching. Processing time is important in working with large databases. Therefore, special cares are taken to deal with the large number of gestures.