High Performance Deformable Image Registration algorithms.for Manycore Processors develops highly data-parallel image registration algorithms.suitable for use on modern multi-core architectures, including graphics pro...
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ISBN:
(数字)9780124078802
ISBN:
(纸本)9780124077416
High Performance Deformable Image Registration algorithms.for Manycore Processors develops highly data-parallel image registration algorithms.suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Focusing on deformable registration, we show how to develop data-parallel versions of the registration algorithm suitable for execution on the GPU. Image registration is the process of aligning two or more images into a common coordinate frame and is a fundamental step to be able to compare or fuse data obtained from different sensor measurements. Extracting useful information from 2D/3D data is essential to realizing key technologies underlying our daily lives. Examples include autonomous vehicles and humanoid robots that can recognize and manipulate objects in cluttered environments using stereo vision and laser sensing and medical imaging to localize and diagnose tumors in internal organs using data captured by CT/MRI *** how to redesign widely used image registration algorithms.so as to best expose the underlying parallelism available in these algorithms.hows how to pose and implement the parallel versions of the algorithms.within the single instruction, multiple data (SIMD) model supported by GPUsProvides Programming "tricks" that can help readers develop other image processing algorithms. including registration algorithms.for the GPU
SmartShadow: Models and Methods for Pervasive Computing offers a new perspective on pervasive computing with SmartShadow, which is designed to model a user as a personality shadow and to model pervasive computing envi...
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ISBN:
(数字)9783642363818
ISBN:
(纸本)9783642363818;9783642363825
SmartShadow: Models and Methods for Pervasive Computing offers a new perspective on pervasive computing with SmartShadow, which is designed to model a user as a personality shadow and to model pervasive computing environments as user-centric dynamic virtual personal spaces. Just like human beings shadows in the physical world, it follows people wherever they go, providing them with pervasive services. The model, methods, and software infrastructure for SmartShadow are presented and an application for smart cars is also introduced. The book can serve as a valuable reference work for researchers and graduate students in the field of pervasive/ubiquitous computing. Zhaohui Wu is a Professor at Zhejiang University, Hangzhou, China. Gang Pan is a Professor at the same institute.
A cookbook of algorithms.for common image processing applications Thanks to advances in computer hardware and software, algorithms.have been developed that support sophisticated image processing without requiring an ...
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ISBN:
(数字)9781118019627
ISBN:
(纸本)9780470643853
A cookbook of algorithms.for common image processing applications Thanks to advances in computer hardware and software, algorithms.have been developed that support sophisticated image processing without requiring an extensive background in mathematics. This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids. It’s an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processing. algorithms.now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialists This bestselling book has been completely updated to include the latest algorithms. including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aids Saves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applications. algorithms.for Image Processing and computer Vision, 2nd Edition provides the tools to speed development of image processing applications.
computer vision systems attempt to understand a scene and its components from mostly visual information. The geometry exhibited by the real world, the influence of material properties on scattering of incident light, ...
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ISBN:
(数字)9781601983152
ISBN:
(纸本)9781601983145
computer vision systems attempt to understand a scene and its components from mostly visual information. The geometry exhibited by the real world, the influence of material properties on scattering of incident light, and the process of imaging introduce constraints and properties that are key to solving some of these tasks. In the presence of noisy observations and other uncertainties, the algorithms.make use of statistical methods for robust inference. In this paper, we highlight the role of geometric constraints in statistical estimation methods, and how the interplay of geometry and statistics leads to the choice and design of algorithms. In particular, we illustrate the role of imaging, illumination, and motion constraints in classical vision problems such as tracking, structure from motion, metrology, activity analysis and recognition, and appropriate statistical methods used in each of these problems.
An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematica...
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ISBN:
(数字)9780470503065
ISBN:
(纸本)9780470371923
An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.
While rapid progress has been made on upper bounds (algorithms., progress on lower bounds on the complexity of explicit problems has remained slow despite intense efforts over several decades. As is natural with typic...
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ISBN:
(数字)9781601982438
ISBN:
(纸本)9781601982421
While rapid progress has been made on upper bounds (algorithms., progress on lower bounds on the complexity of explicit problems has remained slow despite intense efforts over several decades. As is natural with typical impossibility results, lower bound questions are hard mathematical problems and hence are unlikely to be resolved by ad hoc attacks. Instead, techniques based on mathematical notions that capture computational complexity are necessary. Complexity Lower Bounds using Linear Algebra surveys several techniques for proving lower bounds in Boolean, algebraic, and communication complexity based on certain linear algebraic approaches. The common theme among these approaches is to study robustness measures of matrix rank that capture the complexity in a given model. Suitably strong lower bounds on such robustness functions of explicit matrices lead to important consequences in the corresponding circuit or communication models. Understanding the inherent computational complexity of problems is of fundamental importance in mathematics and theoretical computer science. Complexity Lower Bounds using Linear Algebra is an invaluable reference for anyone working in the field.
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