Download e-book for iPad: Advances in Intelligent IT by Y. Li, M. Looi, N. Zhong

By Y. Li, M. Looi, N. Zhong

Within the nice electronic period, we're witnessing many quick medical and technological advancements in human-centered, seamless computing environments, interfaces, units, and structures with functions starting from company and communique to leisure and studying. those advancements are jointly top characterised as lively Media know-how (AMT), a brand new sector of clever info know-how and machine technology that emphasizes the proactive, seamless roles of interfaces and platforms in addition to new media in all elements of electronic lifestyles. An AMT dependent desktop approach bargains providers that allow the speedy layout, implementation, deploying and aid of custom-made strategies. This publication brings jointly papers from researchers from diversified parts, reminiscent of internet intelligence, information mining, clever brokers, shrewdpermanent details use, networking and clever interface.

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-Biomedicine -Oncology -Artificial intelligence -Databases and knowledge structures -Maritime engineering -Nanotechnology -Geoengineering -All elements of physics -E-governance -E-commerce -The wisdom economic climate -Urban stories -Arms keep an eye on -Understanding and responding to terrorism -Medical informatics -Computer Sciences

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The domain knowledge construction is the key factor to build the tool. So far, we are depending on the experts to provide the domain information in order to transform users’ input through the Web-based interface. Requests from different domains require different sets of domain knowledge to support. The generalization design of the tool architecture to accommodate multi-domain is a potential area that can improve the request transformation. Another issue is the input format. In current solution, encoding components from real world cases are used as the input from users.

Maire / A Head-Tracker Based on the Lucas-Kanade Optical Flow Algorithm 29 minimum eigenvalue. The iterative implementation of the Lucas-Kanade optical flow computation provides sufficient local tracking accuracy. Figure 3. Feature extraction using modified Lucas-Kanade algorithm. The algorithm first identifies regions of interest in the image (subject to constraints) and tracks these regions in subsequent frames. Figure 4. Demonstrating the low-light tracking capability of the Lucas-Kanade algorithm 6.

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