Learning Spatio Temporal Invariances
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Learning Spatio Temporal Invariances
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Author : James V. Stone
language : en
Publisher:
Release Date : 1994
Learning Spatio Temporal Invariances written by James V. Stone and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Neural networks (Computer science) categories.
Abstract: "We present a neural network model for the unsupervised learning of high order visual invariances. The model is demonstrated on the problem of estimating sub-pixel stereo disparity from a temporal sequence of unprocessed image pairs. After learning on a given image sequence, the model's ability to detect sub-pixel disparity generalises, without additional learning, to image pairs from other sequences."
Deep Learning Of Invariant Spatio Temporal Features From Video
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Author :
language : en
Publisher:
Release Date : 2008
Deep Learning Of Invariant Spatio Temporal Features From Video written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
We present a novel hierarchical and distributed model for learning invariant spatio-temporal features from video. Our approach builds on previous deep learning methods and uses the Convolutional Restricted Boltzmann machine (CRBM) as a building block. Our model, called the Space-Time Deep Belief Network (ST-DBN), aggregates over both space and time in an alternating way so that higher layers capture more distant events in space and time. The model is learned in an unsupervised manner. The experiments show that it has good invariance properties, that it is well-suited for recognition tasks, and that it has reasonable generative properties that enable it to denoise video and produce spatio-temporal predictions.
Learning Perceptual Invariances
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Author : Stephen Eglen
language : en
Publisher:
Release Date : 1996
Learning Perceptual Invariances written by Stephen Eglen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Artificial intelligence categories.
Abstract: "A novel unsupervised learning method for extracting spatio-temporal invariances has been developed in (Stone, 1995). The learning method works by trying to jointly minimise the short term variance of a unit's output, whilst maximising the long term variance of the output. The learning method has been applied to extracting disparity from a temporal sequence of random dot stereograms. This paper reports on developing and applying the learning method to a spatial task, both in one and two dimensions. Random dot stereograms were used as input to a three layer feedforward network. After learning, output units in the network become selective for disparity. This confirms the usefulness of the learning method, and leads the way to creating a full spatio-temporal model, using both temporal and spatial information."
Unsupervised Learning
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Author : Geoffrey Hinton
language : en
Publisher: MIT Press
Release Date : 1999-05-24
Unsupervised Learning written by Geoffrey Hinton and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-05-24 with Medical categories.
Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.
Computer Vision Eccv 2008
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Author : David Hutchison
language : en
Publisher:
Release Date : 2008
Computer Vision Eccv 2008 written by David Hutchison and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computer graphics categories.
The four-volume set comprising LNCS volumes 5302/5303/5304/5305 constitutes the refereed proceedings of the 10th European Conference on Computer Vision, ECCV 2008, held in Marseille, France, in October 2008. The 243 revised papers presented were carefully reviewed and selected from a total of 871 papers submitted. The four books cover the entire range of current issues in computer vision. The papers are organized in topical sections on recognition, stereo, people and face recognition, object tracking, matching, learning and features, MRFs, segmentation, computational photography and active reconstruction.
Statistics For Spatio Temporal Data
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Author : Noel Cressie
language : en
Publisher: John Wiley & Sons
Release Date : 2015-11-02
Statistics For Spatio Temporal Data written by Noel Cressie and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-02 with Mathematics categories.
Winner of the 2013 DeGroot Prize. A state-of-the-art presentation of spatio-temporal processes, bridging classic ideas with modern hierarchical statistical modeling concepts and the latest computational methods Noel Cressie and Christopher K. Wikle, are also winners of the 2011 PROSE Award in the Mathematics category, for the book “Statistics for Spatio-Temporal Data” (2011), published by John Wiley and Sons. (The PROSE awards, for Professional and Scholarly Excellence, are given by the Association of American Publishers, the national trade association of the US book publishing industry.) Statistics for Spatio-Temporal Data has now been reprinted with small corrections to the text and the bibliography. The overall content and pagination of the new printing remains the same; the difference comes in the form of corrections to typographical errors, editing of incomplete and missing references, and some updated spatio-temporal interpretations. From understanding environmental processes and climate trends to developing new technologies for mapping public-health data and the spread of invasive-species, there is a high demand for statistical analyses of data that take spatial, temporal, and spatio-temporal information into account. Statistics for Spatio-Temporal Data presents a systematic approach to key quantitative techniques that incorporate the latest advances in statistical computing as well as hierarchical, particularly Bayesian, statistical modeling, with an emphasis on dynamical spatio-temporal models. Cressie and Wikle supply a unique presentation that incorporates ideas from the areas of time series and spatial statistics as well as stochastic processes. Beginning with separate treatments of temporal data and spatial data, the book combines these concepts to discuss spatio-temporal statistical methods for understanding complex processes. Topics of coverage include: Exploratory methods for spatio-temporal data, including visualization, spectral analysis, empirical orthogonal function analysis, and LISAs Spatio-temporal covariance functions, spatio-temporal kriging, and time series of spatial processes Development of hierarchical dynamical spatio-temporal models (DSTMs), with discussion of linear and nonlinear DSTMs and computational algorithms for their implementation Quantifying and exploring spatio-temporal variability in scientific applications, including case studies based on real-world environmental data Throughout the book, interesting applications demonstrate the relevance of the presented concepts. Vivid, full-color graphics emphasize the visual nature of the topic, and a related FTP site contains supplementary material. Statistics for Spatio-Temporal Data is an excellent book for a graduate-level course on spatio-temporal statistics. It is also a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.
Computer Vision Accv 2010
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Author : Ron Kimmel
language : en
Publisher: Springer
Release Date : 2011-02-28
Computer Vision Accv 2010 written by Ron Kimmel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-28 with Computers categories.
The four-volume set LNCS 6492-6495 constitutes the thoroughly refereed post-proceedings of the 10th Asian Conference on Computer Vision, ACCV 2009, held in Queenstown, New Zealand in November 2010. All together the four volumes present 206 revised papers selected from a total of 739 Submissions. All current issues in computer vision are addressed ranging from algorithms that attempt to automatically understand the content of images, optical methods coupled with computational techniques that enhance and improve images, and capturing and analyzing the world's geometry while preparing the higher level image and shape understanding. Novel geometry techniques, statistical learning methods, and modern algebraic procedures are dealt with as well.
Neural Information Processing
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Author : Minho Lee
language : en
Publisher: Springer
Release Date : 2013-10-29
Neural Information Processing written by Minho Lee and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-29 with Computers categories.
The three volume set LNCS 8226, LNCS 8227, and LNCS 8228 constitutes the proceedings of the 20th International Conference on Neural Information Processing, ICONIP 2013, held in Daegu, Korea, in November 2013. The 180 full and 75 poster papers presented together with 4 extended abstracts were carefully reviewed and selected from numerous submissions. These papers cover all major topics of theoretical research, empirical study and applications of neural information processing research. The specific topics covered are as follows: cognitive science and artificial intelligence; learning theory, algorithms and architectures; computational neuroscience and brain imaging; vision, speech and signal processing; control, robotics and hardware technologies and novel approaches and applications.
Face Image Analysis By Unsupervised Learning
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Author : Marian Stewart Bartlett
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Face Image Analysis By Unsupervised Learning written by Marian Stewart Bartlett and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.
On Spatio Temporal Data Modelling And Uncertainty Quantification Using Machine Learning And Information Theory
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Author : Fabian Guignard
language : en
Publisher: Springer Nature
Release Date : 2022-03-12
On Spatio Temporal Data Modelling And Uncertainty Quantification Using Machine Learning And Information Theory written by Fabian Guignard and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-12 with Science categories.
The gathering and storage of data indexed in space and time are experiencing unprecedented growth, demanding for advanced and adapted tools to analyse them. This thesis deals with the exploration and modelling of complex high-frequency and non-stationary spatio-temporal data. It proposes an efficient framework in modelling with machine learning algorithms spatio-temporal fields measured on irregular monitoring networks, accounting for high dimensional input space and large data sets. The uncertainty quantification is enabled by specifying this framework with the extreme learning machine, a particular type of artificial neural network for which analytical results, variance estimation and confidence intervals are developed. Particular attention is also paid to a highly versatile exploratory data analysis tool based on information theory, the Fisher-Shannon analysis, which can be used to assess the complexity of distributional properties of temporal, spatial and spatio-temporal data sets. Examples of the proposed methodologies are concentrated on data from environmental sciences, with an emphasis on wind speed modelling in complex mountainous terrain and the resulting renewable energy assessment. The contributions of this thesis can find a large number of applications in several research domains where exploration, understanding, clustering, interpolation and forecasting of complex phenomena are of utmost importance.