Advances in intelligent information processing [electronic resource] : tools and applications / editors, B. Chanda, C.A. Murthy.

Hackensack, NJ : World Scientific, c2008.
1 online resource (316 p.)
Statistical Science and Interdisciplinary Research
Statistical science and interdisciplinary research, 1793-6195 ; v. 2

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Image processing.
Information storage and retrieval systems.
Electronic books.
The book deals with several key aspects of developing technologies in information processing systems. It explains various problems related to advanced image processing systems and describes some of the latest state-of-the-art techniques in solving them. Particularly, the recent advances in image and video processing are covered thoroughly with real-life applications. Some of the latest topics like rough fuzzy hybridization and knowledge reuse in computational intelligence are included adequately.
Foreword; Preface; Contents; 1. Non-parametric Mixture Model Based Evolution of Level Sets N. Joshi and M. Brady; 1.1 Introduction; 1.2 Need for Modelling Class Distributions Non-parametrically; 1.3 NP-windows Method for Non-parametric Estimation of PDFs; 1.4 NPMM-ICLS Framework; 1.5 Level Sets Method; 1.6 NPMM-ICLS Level Sets Method; 1.7 Results and Discussion; 1.8 Conclusions; Bibliography; 2. Pattern Generation Using Level Set Based Curve Evolution A. Chattopadhyay and D. P. Mukherjee; 2.1 Introduction; 2.2 Background; 2.2.1 Level set model of curve evolution
2.2.2 Reaction-diffusion model2.2.3 Shape optimization; 2.3 Proposed Methodology; 2.3.1 Reaction-diffusion influenced curve evolution; 2.3.2 Shape optimization based curve evolution; 2.4 Results; 2.4.1 Pattern disocclusion; 2.5 Conclusions; Bibliography; 3. Stable Contour Tracking Through Tangential Evolution V. Srikrishnan and S. Chaudhuri; 3.1 Active Contours: Introduction; 3.2 Curve Evolution; 3.3 Difficulties with Parametric Curves; 3.4 Existing Solutions; 3.5 Proposed Method; 3.5.1 Comparison with other works; 3.5.2 Choice of the ideal constant K; 3.5.3 Proof of conditional boundedness
3.6 Applications in Image Segmentation and Tracking3.7 Implementation Details; 3.8 Results; 3.9 Conclusions and FutureWork; Bibliography; 4. Information Theoretic Approaches for Next Best View Planning in Active Computer Vision C. Derichs, B. Deutsch, S. Wenhardt, H. Niemann and J. Denzler; 4.1 Introduction; 4.2 Information Theoretical Approaches for Next Best View Planning; 4.2.1 General state modeling and estimation; 4.2.2 Optimality criteria for active view planning; 4.3 Planning Tasks; 4.3.1 Active object recognition; State representation and information fusion Optimal action selection4.3.2 Active object tracking; State and observation representation; Optimal action selection; Visibility; Multi-step action selection; 4.3.3 Active object reconstruction; State and observation representation; Optimal action selection; 4.4 Experiments; 4.4.1 Evaluation for active object recognition; 4.4.2 Evaluation for active object tracking; 4.4.3 Evaluation for active object reconstruction; Reconstructing a calibration pattern; Reconstructing a mouse pad; 4.5 Summary; Bibliography
5. Evaluation of Linear Combination of Views for Object Recognition V. Zografos and B. F. Buxton5.1 Introduction; 5.2 Linear Combination of Views; 5.2.1 Image synthesis; 5.3 The Recognition System; 5.3.1 Template matching; 5.3.2 Optimisation; 5.4 Experimental Results; 5.4.1 Experiments on the CMU PIE database; 5.4.2 Experiments on the COIL-20 database; 5.5 Conclusion; Bibliography; 6. Using Object Models as Domain Knowledge in Perceptual Organization G. Harit, R. Bharatia and S. Chaudhury; 6.1 Introduction; 6.2 Perceptual Grouping in Video; 6.2.1 Video data clustering
6.2.2 The perceptual grouping model
"Platinum jubilee series."
Includes bibliographical references and index.
Chanda, B. (Bhabatosh)
Murthy, C. A.