Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles. This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution. Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as: Discriminative Random Fields (DRF) Strong Random Fields (SRF) Spatial-Temporal Models Total Variation Models Learning MRF for Classification (motivation + DRF) Relation to Graphic Models Graph Cuts Belief Propagation Features: Focuses on the application of Markov random fields to computer vision problems, such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain Presents various vision models in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation Uses a variety of examples to illustrate how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting Introduces readers to the basic concepts, important models and various special classes of MRFs on the regular image lattice and MRFs on relational graphs derived from images Examines the problems of parameter estimation and function optimization Includes an extensive list of references This broad-ranging and comprehensive volume is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It has been class-tested and is suitable as a textbook for advanced courses relating to these areas.

Markov Random Field Modeling in Image Analysis

There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.

Cooperative mobile robotics: antecedents and directions

Active perception (active vision specifically) is defined as a study of modeling and control strategies for perception. Local methods are distinguished from global models by their extent of application in space and time. The local models represent procedures and parameters such as optical distortions of the lens, focal lens, spatial resolution, bandpass filter, etc, The global models, on the other hand, characterize the overall performance and make predictions on how the individual modules interact. The control strategies are formulated as a search of such sequences of steps that would minimize a loss function while still seeking the most information. Examples are shown as the existence proof of the proposed theory on obtaining range from focus and stereo/vergence on 2-D segmentation of an image and 3-D shape parameterization. >

Active perception

A stereo confidence metric using single view imagery with comparison to five alternative approaches

In this article we consider the problem of task-directed information gathering. We first develop a decision-theo retic model of task-directed sensing in which sensors are modeled as noise-contaminated, uncertain measurement systems, and sensing tasks are inodeled by a transforma tion describing the type of information required by the task, a utility function describing sensitivity to error, and a cost function describing time or resource constraints on the system.This description allows us to develop a standard condi tional Bayes decision-making model where the value of information, or payoff, of an estimate is defined as the average utility (the expected value of some function of decision or estimation error) relative to the current proba bility distribution and the best estimate is that which max imizes payoff. The optimal sensor viewing strategy is that which maximizes the net payoff (decision value minus observation costs) of the final estimate. The advantage of this solution is generality—it does not...

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Computational methods for task-directed sensor data fusion and sensor planning

Presents a collaborative effort to design and implement a cooperative material handling system by a small team of human and robotic agents in an unstructured indoor environment. The authors' approach makes fundamental use of the human agents' expertise for aspects of task planning, task monitoring, and error recovery. The authors' system is neither fully autonomous nor fully teleoperated. It is designed to make effective use of the human's abilities within the present state of the art of autonomous systems. The authors' robotic agents refer to systems which are each equipped with at least one sensing modality and which possess some capability for self-orientation and/or mobility. The authors' robotic agents are not required to be homogeneous with respect to either capabilities or function. The authors' research stresses both paradigms and testbed experimentation. Theory issues include the requisite coordination principles and techniques which are fundamental to a cooperative multiagent system's basic functioning. The authors have constructed an experimental distributed multiagent-architecture testbed facility. The required modular components of this testbed are currently operational and have been tested individually. The authors' current research focuses on the agents' integration in a scenario for cooperative material handling.

/pdf/cooperative-material-handling-by-human-and-robotic-agents-3naggz7nft.pdf

Cooperative material handling by human and robotic agents: module development and system synthesis

We examine the existence and behavior of game-theoretic solutions for robust linear filters and predictors. Our basic uncertainty class includes m th-order time-varying discrete-time systems with uncertain dynamics, uncertain initial state covariance, and uncertain nonstationary input and observation noise covariance. Our results include recursive (Kalman filter/predictor) realizations for the resulting robust procedures. Our approach is based on saddle-point theory. We emphasize the notion of a least favorable prior distribution for the uncertain parameter values to obtain a worst case design technique. In this paper, we highlight the role such distributions with finite support play in these decision models. In particular, we demonstrate that, in these decision models, the least favorable prior distribution is always discrete.

Robust filtering and prediction for linear systems with uncertain dynamics: A game-theoretic approach

Dragging a tool across a textured object creates rich high-frequency vibrations that distinctly convey the physical interaction between the tool tip and the object surface. Varying one's scanning speed and normal force alters these vibrations, but it does not change the perceived identity of the tool or the surface. Previous research developed a promising data-driven approach to embedding this natural complexity in a haptic virtual environment: the approach centers on recording and modeling the tool contact accelerations that occur during real texture interactions at a limited set of force-speed combinations. This paper aims to optimize these prior methods of texture modeling and rendering to improve system performance and enable potentially higher levels of haptic realism. The key elements of our approach are drawn from time series analysis, speech processing, and discrete-time control. We represent each recorded texture vibration with a low-order auto-regressive moving-average (ARMA) model, and we optimize this set of models for a specific tool-surface pairing (plastic stylus and textured ABS plastic) using metrics that depend on spectral match, final prediction error, and model order. For rendering, we stably resample the texture models at the desired output rate, and we derive a new texture model at each time step using bilinear interpolation on the line spectral frequencies of the resampled models adjacent to the user's current force and speed. These refined processes enable our TexturePad system to generate a stable and spectrally accurate vibration waveform in real time, moving us closer to the goal of virtual textures that are indistinguishable from their real counterparts.

/pdf/refined-methods-for-creating-realistic-haptic-virtual-kbyiio4ohq.pdf

Max Mintz

Papers

A stereo confidence metric using single view imagery with comparison to five alternative approaches

Computational methods for task-directed sensor data fusion and sensor planning

Cooperative material handling by human and robotic agents: module development and system synthesis

Robust filtering and prediction for linear systems with uncertain dynamics: A game-theoretic approach

Refined methods for creating realistic haptic virtual textures from tool-mediated contact acceleration data