This book describes the fundamental building-block of many new computer vision systems: dense and robust correspondence estimation. Dense correspondence estimation techniques are now successfully bein
This book presents novel learning techniques that enable mobile platforms with one or more robotic manipulators to autonomously adapt to new or changing situations.
The goal of this volume is to summarize the state-of-the-art in the utilization of computer vision techniques in the diagnosis of skin cancer. Malignant melanoma is one of the most rapidly increasing
The need for natural and effective Human-Computer Interaction (HCI) is increasingly important due to the prevalence of computers in human activities. Computer vision and pattern recognition continue t
200Ts Vision of Vision One of my formative childhood experiences was in 1968 stepping into the Uptown Theater on Connecticut Avenue in Washington, DC, one of the few movie theaters nationwide that pro
This book summarises the state of the art in computer vision-based driver and road monitoring, focussing on monocular vision technology in particular, with the aim to address challenges of driver assi
This book constitutes the thoroughly refereed prost-workshop proceedings of the International Workshop on Medical Computer Vision: Algorithms for Big Data, MCS 2015, held in Munich, Germany, in Octobe
This accessible textbook presents an introduction to computer vision algorithms for industrially-relevant applications of X-ray testing. Features: introduces the mathematical background for monocular
Information theory has proved to be effective for solving many computer vision & pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feat
This short book presents a framework for assessing the reliability and availability of visual quality control systems, placing particular emphasis on wavelet-based analysis. It presents experimental r
This book presents novel graph-theoretic methods for complex computer vision and pattern recognition tasks. It presents the application of graph theory to low-level processing of digital images, prese
Simulation based on mathematical models plays a major role in computer aided design of integrated circuits (ICs). Decreasing structure sizes, increasing packing densities and driving frequencies requi
This richly illustrated book introduces the reader to a newly developed theory in Computer Vision yielding elementary techniques to analyze digital images. These techniques are inspired by, and are a
WINNER OF THE 2001 DEGROOT PRIZE!Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculat
This book describes several mathematical models of the primary visual cortex, referring them to a vast ensemble of experimental data and putting forward an original geometrical model for its functiona
This monograph presents the challenges, vision and context to design smart learning objects (SLOs) through Computer Science (CS) education modelling and feature model transformations. It presents the
The goal of computer vision (CV) research is to provide computers with human-like perception capabilities so that they can sense the environment, understand the sensed data, take appropriate actions,
This book is an introduction to finite model theory which stresses the computer science origins of the area. In addition to presenting the main techniques for analyzing logics over finite models, the
Front-End Vision and Multi-Scale Image Analysis is a tutorial in multi-scale methods for computer vision and image processing. It builds on the cross fertilization between human visual perception and
Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the
This volume contains a total of thirteen papers covering a variety of AI topics ranging from computer vision and robotics to intelligent modeling, neural networks and fuzzy logic. There are two gen
This book covers aspects of human re-identification problems related to computer vision and machine learning. Working from a practical perspective, it introduces novel algorithms and designs for human
Motivated by a variational model concerning the depth of the objects in a picture and the problem of hidden and illusory contours, this book investigates one of the central problems of computer vision
Variational Regularization of 3D Data provides an introduction to variational methods for data modelling and its application in computer vision. In this book, the authors identify interpolation as an
This Springer Brief examines the combination of computer vision techniques and machine learning algorithms necessary for humanoid robots to develop “true consciousness.” It illustrates the critical fi
Mathematica is a computer program (software) for doing symbolic, numeric and graphical analysis of mathematical problems. In the hands of economists, financial analysts and other professionals in econ
The concept of 'shape' is at the heart of image processing and computer vision, yet researchers still have some way to go to replicate the human brain's ability to extrapolate meaning from the most ba
Most books on epidemiology have treated the subject from a statistical, mathematical or computer applicational point of view. However, experiments must be performed first to provide the data for model
This work presents a full generic approach to the detection and recognition of traffic signs. The approach is based on the latest computer vision methods for object detection, and on powerful methods
"An original motivation for algebraic geometry was to understand curves and surfaces in three dimensions. Recent theoretical and technological advances in areas such as robotics, computer vision, comp
A textbook for a graduate course in computer simulation for students with a basic background in differential equations, numerical analysis, and FORTRAN programming. It explains how to model environmen
