The fully automated estimation of the 6 degrees of freedom camera motion and the imaged 3D scenario using as the only input the pictures taken by the camera has been a long term aim in the computer vision community. The associated line of research has been known as Structure from Motion (SfM). An intense research effort during the latest decades has produced spectacular advances; the topic has reached a consistent state of maturity and most of its aspects are well known nowadays. 3D vision has immediate applications in many and diverse fields like robotics, videogames and augmented reality; and technological transfer is starting to be a reality. This book describes one of the first systems for sparse point-based 3D reconstruction and egomotion estimation from an image sequence; able to run in real-time at video frame rate and assuming quite weak prior knowledge about camera calibration, motion or scene. Its chapters unify the current perspectives of the robotics and computer vision communities on the 3D vision topic: As usual in robotics sensing, the explicit estimation and propagation of the uncertainty hold a central role in the sequential video processing and is shown to boost the efficiency and performance of the 3D estimation. On the other hand, some of the most relevant topics discussed in SfM by the computer vision scientists are addressed under this probabilistic filtering scheme; namely projective models, spurious rejection, model selection and self-calibration.

A large-scale system is composed of several interconnected subsystems. For such a system it is often desired to have some form of decentralization in the control structure, since it is typically not realistic to assume that all output measurements can be transmitted to every local control station. Problems of this kind can appear in electric power systems, communication networks, large space structures, robotic systems, economic systems, and traffic networks, to name only a few. Typical large-scale control systems have several local control stations which observe only local outputs and control only local inputs. All controllers are involved, however, in the control operation of the overall system. The focus of this book is on the efficient control of interconnected systems, and it presents systems analysis and controller synthesis techniques using a variety of methods. A systematic study of multi-input, multi-output systems is carried out and illustrative examples are given to clarify the ideas.

The fully automated estimation of the 6 degrees of freedom camera motion and the imaged 3D scenario using as the only input the pictures taken by the camera has been a long term aim in the computer vision community. The associated line of research has been known as Structure from Motion (SfM). An intense research effort during the latest decades has produced spectacular advances; the topic has reached a consistent state of maturity and most of its aspects are well known nowadays. 3D vision has immediate applications in many and diverse fields like robotics, videogames and augmented reality; and technological transfer is starting to be a reality. This book describes one of the first systems for sparse point-based 3D reconstruction and egomotion estimation from an image sequence; able to run in real-time at video frame rate and assuming quite weak prior knowledge about camera calibration, motion or scene. Its chapters unify the current perspectives of the robotics and computer vision communities on the 3D vision topic: As usual in robotics sensing, the explicit estimation and propagation of the uncertainty hold a central role in the sequential video processing and is shown to boost the efficiency and performance of the 3D estimation. On the other hand, some of the most relevant topics discussed in SfM by the computer vision scientists are addressed under this probabilistic filtering scheme; namely projective models, spurious rejection, model selection and self-calibration.

Mark Davison examines several legal models designed to protect databases, considering in particular the EU Directive, the history of its adoption and its transposition into national laws. He compares the Directive with a range of American legislative proposals, as well as the principles of misappropriation that underpin them. In addition, the book also contains a commentary on the appropriateness of the various models in the context of moves for an international agreement on the topic. This book will be of interest to academics and practitioners, including those involved with databases and other forms of new media.

The fourth edition of Australian Intellectual Property Law provides a detailed and comprehensive, yet concise and accessible discussion of intellectual property law in Australia. This edition has been thoroughly revised to cover the most recent developments in intellectual property law, including significant case law and discussion of the proposed and enacted amendments to the Copyright Act 1968 (Cth), the Patents Act 1990 (Cth) and the Plant Breeder's Rights Act 1994 (Cth). The text has been restructured, but continues to provide a complete discussion of the black-letter aspects of the law. Commencing with copyright, then followed by design law, confidential information, patents, plant breeder's rights, then finally trade marks. The work ends with a chapter on enforcing legal rights and civil remedies. Written by highly-respected intellectual property law researchers this text is an invaluable resource for both undergraduate and postgraduate students, academics and other professionals working with intellectual property.

Mark Davison examines several legal models designed to protect databases, and specifically, the E.U. Directive--the history of its adoption and its transposition into national laws. Davison compares the Directive with various American legislative proposals, as well as the principles of misappropriation that are behind them. In addition, the book contains a commentary on the appropriateness of the various models in the context of arguments for international agreement on the topic.

An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty-that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

In these turbulent post-September 11 times, failed states have received ever-greater levels of attention in the political and scholarly realms. Although development is seen as one of three main antidotes to combat state failure, the use of foreign aid to positively influence development is not without its controversies. This study, however, has clearly shown an empirical regularity between aid and failure by demonstrating that foreign aid only goes to states that fail and that all states that fail receive foreign aid. Further research into theories that account for this correlation should also help effectively maximize the leverage of aid dollars to prevent or minimize state failure episodes. One suggestion for additional research that might help sharpen the focus of foreign aid decisions is to examine the aid-failure correlation from 1955 to 1992 and the lack of correlation in the years afterward. Another potential research topic this study developed is to explore the nuances of the transition years in greater detail to determine what mechanisms caused those transitions.

This scarce antiquarian book is a selection from Kessinger Publishing's Legacy Reprint Series. Due to its age, it may contain imperfections such as marks, notations, marginalia and flawed pages. Because we believe this work is culturally important, we have made it available as part of our commitment to protecting, preserving, and promoting the world's literature. Kessinger Publishing is the place to find hundreds of thousands of rare and hard-to-find books with something of interest for everyone