Why do things look blurry underwater? Why do people drive too fast in fog? How do you high-pass filter a cup of tea? What have mixer taps to do with colour vision? Basic Vision: An Introduction to Visual Perception demystifies the processes through which we see the world. Written by three authors with over 80 years of research and undergraduate teaching experience between them, it leads the reader step-by-step through the intricacies of visual processing, with full-colour illustrations on nearly every page. The writing style captures the excitement of recent research in neuroscience that has transformed our understanding of visual processing, but delivers it with a humour that keeps the reader enthused, rather than bemused. The book takes us through the various elements that come together as our perception of the world around us: the perception of size, colour, motion, and three-dimensional space. It illustrates the intricacy of the visual system, discussing its development during infancy, and revealing how the brain can get it wrong, either as a result of brain damage, through which the network of processes become compromised, or through illusion, where the brain compensates for mixed messages by seeing what it thinks should be there, rather than conveying the reality. The book also demonstrates the importance of contemporary techniques and methodology, and neuroscience-based techniques in particular, in driving forward our understanding of the visual system. Online Resource Centre The Online Resource Centre to accompany Basic Vision features: For registered adopters: Figures from the book available to download, to facilitate lecture preparation. Test bank of multiple choice questions – a readily available tool for either formative or summative assessment. A Journal Club, with questions to lead students through key research articles that relate to topics covered in the book. For students: Annotated web links, giving students ready access to these additional learning resources.
Thứ Hai, 20 tháng 4, 2015
Basic Vision
Thứ Năm, 12 tháng 3, 2015
Dataset Shift in Machine Learning
Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift.
Thứ Ba, 13 tháng 1, 2015
The Book of PF
OpenBSD’s stateful packet filter, PF, offers an amazing feature set and support across the major BSD platforms. Like most firewall software though, unlocking PF’s full potential takes a good teacher. Peter N.M. Hansteen’s PF website and conference tutorials have helped thousands of users build the networks they need using PF. The Book of PF is the product of Hansteen’s knowledge and experience, teaching good practices as well as bare facts and software options. Throughout the book, Hansteen emphasizes the importance of staying in control by having a written network specification, using macros to make rule sets more readable, and performing rigid testing when loading in new rules.
Thứ Năm, 1 tháng 1, 2015
Multirate and Wavelet Signal Processing, Volume 8
This innovative and in-depth book integrates the well-developed theory and practical applications of one dimensional and multidimensional multirate signal processing. Using a rigorous mathematical framework, it carefully examines the fundamentals of this rapidly growing field. Areas covered include: basic building blocks of multirate signal processing; fundamentals of multidimensional multirate signal processing; multirate filter banks; lossless lattice structures; introduction to wavelet signal processing.