This volume demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image reconstruction, image segmentation, 3D vision, and object labeling. It offers key findings and state-of-the-art research on both algorithms and applications.
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists—interested in theoretical and applied aspects of modeling, simulating, and building neural-like or intelligent systems.
Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation performed side by side in the brain. In New Directions in Statistical Signal Processing, leading researchers from both signal processing and neural computation present new work that aims to promote interaction between the two disciplines.
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications.
This book addresses a fundamental software engineering issue, applying formal techniques and rigorous analysis to a practical problem of great current interest: the incorporation of language-specific knowledge in interactive programming environments. It makes a basic contribution in this area by proposing an attribute-grammar framework for incremental semantic analysis and establishing its algorithmic foundations. The results are theoretically important while having immediate practical utility for implementing environment-generating systems.
An exploration of the techniques for analyzing the behavior of one- and two-dimensional iterative networks formed of discrete, or logical elements, showing that most questions about the behavior of iterative systems are recursively undecidable.
Although state variable concepts are a part of modern control theory, they have not been extensively applied in communication theory. The purpose of this book is to demonstrate how the concepts and methods of state variables can be used advantageously in analyzing a variety of communication theory problems.
Speed-independent circuits offer a potential solution to the timing problems of VLSI. In this book David Dill develops and implements a theory for practical automatic verification of these control circuits. He describes a formal model of circuit operation, defines the proper relationship between an implementation and its specification, and constructs a computer program that can check this relationship.
Neuromorphic engineers work to improve the performance of artificial systems through the development of chips and systems that process information collectively using primarily analog circuits. This book presents the central concepts required for the creative and successful design of analog VLSI circuits. The discussion is weighted toward novel circuits that emulate natural signal processing. Unlike most circuits in commercial or industrial applications, these circuits operate mainly in the subthreshold or weak inversion region.