Updated with new illustrations, presents comprehenive examination of Singapore's film landscape.

A timely collection of advanced, original material in the area of statistical methodology motivated by geometric problems, dedicated to the influential work of Kanti V. Mardia This volume celebrates Kanti V. Mardia's long and influential career in statistics. A common theme unifying much of Mardia’s work is the importance of geometry in statistics, and to highlight the areas emphasized in his research this book brings together 16 contributions from high-profile researchers in the field. Geometry Driven Statistics covers a wide range of application areas including directional data, shape analysis, spatial data, climate science, fingerprints, image analysis, computer vision and bioinformatics. The book will appeal to statisticians and others with an interest in data motivated by geometric considerations. Summarizing the state of the art, examining some new developments and presenting a vision for the future, Geometry Driven Statistics will enable the reader to broaden knowledge of important research areas in statistics and gain a new appreciation of the work and influence of Kanti V. Mardia.

"Provides detailed, comprehensive descriptions of electrostatic processes as well as their applications in areas such as rheology, atomization and spraying, industrial dust particle precipitation and filtering, biomedical engineering, gas treatments, atmospheric electricity, chemical reactors, and electronic devices. Summarizes electrostatic fundamentals and electrical phenomena in solids and fluids."

What is Contextual Image Classification A method of classification that is based on the contextual information contained in images is referred to as contextual image classification. This method falls under the category of pattern recognition in computer vision. A "contextual" approach is one that focuses on the relationship between the pixels that are in close proximity to one another, which is also referred to as the neighborhood. The classification of the photographs by the utilization of the contextual information is the objective of this approach. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Contextual image classification Chapter 2: Pattern recognition Chapter 3: Gaussian process Chapter 4: LPBoost Chapter 5: One-shot learning (computer vision) Chapter 6: Least-squares support vector machine Chapter 7: Fraunhofer diffraction equation Chapter 8: Symmetry in quantum mechanics Chapter 9: Bayesian hierarchical modeling Chapter 10: Paden-Kahan subproblems (II) Answering the public top questions about contextual image classification. (III) Real world examples for the usage of contextual image classification in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Contextual Image Classification.

The two-volume reference work Chemical Technology and the Environment provides readers with knowledge on contemporary issues in environmental pollution, prevention and control, as well as regulatory, health and safety issues as related to chemical technology. It introduces and expands the knowledge on emerging "green" materials and processes and "greener" energy technology, as well as more general concepts and methodology including sustainable development and chemistry and green chemistry. Based on Wiley's renowned, Kirk-Othmer Encyclopedia of Chemical Technology, this compact reference features the same breadth and quality of coverage and clarity of presentation found in the original.