Robustnesstochangesinilluminationconditionsaswellas viewing perspectives is an important requirement formany computer vision applications. One of the key fac-ors in enhancing the robustness of dynamic scene analy-sis that of accurate and reliable means for shadow de-ection. Shadowdetectioniscriticalforcorrectobjectde-ection in image sequences. Many algorithms have beenproposed in the literature that deal with shadows. How-ever,acomparativeevaluationoftheexistingapproachesisstill lacking. In this paper, the full range of problems un-derlyingtheshadowdetectionareidenti?edanddiscussed.Weclassifytheproposedsolutionstothisproblemusingaaxonomyoffourmainclasses, calleddeterministicmodeland non-model based and STATISTICAL parametric and non-parametric. Novelquantitative(detectionanddiscrimina-ionaccuracy)andqualitativemetrics(sceneandobjectin-dependence,?exibilitytoshadowsituationsandrobustnesso noise) are proposed to evaluate these classes of algo-rithms on a benchmark suite of indoor and outdoor videosequences.
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to STATISTICAL inference. 1.1 The Learning Problem and (STATISTICAL) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
This paper presents an interactive technique that
produces static hairstyles by generating individual hair strands
of the desired shape and color, subject to the presence of gravity
and collisions. A variety of hairstyles can be generated by
adjusting the wisp parameters, while the deformation is solved
efficiently, accounting for the effects of gravity and collisions.
Wisps are generated employing STATISTICAL approaches. As for
hair deformation, we propose a method which is based on
physical simulation concepts but is simplified to efficiently
solve the static shape of hair. On top of the STATISTICAL wisp
model and the deformation solver, a constraint-based styler
is proposed to model artificial features that oppose the natural
flow of hair under gravity and hair elasticity, such as a hairpin.
Our technique spans a wider range of human hairstyles than
previously proposed methods, and the styles generated by this
technique are fairly realistic.
《多傳感器數據融合手冊》
《Handbook of Multisensor Data Fusion》
作者: David L. Hall
定價: USD 199.95
出版社: CRC
出版年: 2001-06-20
簡介 · · · · · ·
Multisensor data fusion is an emerging technology with important applications in both the military and civilian sectors, such as target recognition, robotics, medical diagnostics, and "smart" buildings. It draws on techniques from wide-ranging disciplines, including artificial intelligence, pattern recognition, and STATISTICAL estimation. This handbook is an up-to-date, comprehensive resource for data fusion system designers and researchers. Top experts in the field lead readers from a basic introduction and survey of data fusion technology to advanced mathematics and theory and to some very practical advice for systems implementers.
AutoSummary uses Natural Language Processing to generate a contextually-relevant synopsis of plain text.
It uses STATISTICAL and rule-based methods for part-of-speech tagging, word sense disambiguation,
sentence deconstruction and semantic analysis.
Data mining (DM) is the extraction of hidden predictive information from large databases
(DBs). With the automatic discovery of knowledge implicit within DBs, DM uses
sophisticated STATISTICAL analysis and modeling techniques to uncover patterns and relationships
hidden in organizational DBs. Over the last 40 years, the tools and techniques to
process structured information have continued to evolve from DBs to data warehousing
(DW) to DM. DW applications have become business-critical. DM can extract even more
value out of these huge repositories of information.
From helping to assess the value of new medical treatments to evaluating the
factors that affect our opinions and behaviors, analysts today are finding
myriad uses for categorical data methods. In this book we introduce these
methods and the theory behind them.
STATISTICAL methods for categorical responses were late in gaining the level
of sophistication achieved early in the twentieth century by methods for
continuous responses. Despite influential work around 1900 by the British
statistician Karl Pearson, relatively little development of models for categorical
responses occurred until the 1960s. In this book we describe the early
fundamental work that still has importance today but place primary emphasis
on more recent modeling approaches. Before outlining
The kernel-ica package is a Matlab program that implements the Kernel
ICA algorithm for independent component analysis (ICA). The Kernel ICA
algorithm is based on the minimization of a contrast function based on
kernel ideas. A contrast function measures the STATISTICAL dependence
between components, thus when applied to estimated components and
minimized over possible demixing matrices, components that are as
independent as possible are found.
Recent advances in experimental methods have resulted in the generation
of enormous volumes of data across the life sciences. Hence clustering and
classification techniques that were once predominantly the domain of ecologists
are now being used more widely. This book provides an overview of these
important data analysis methods, from long-established STATISTICAL methods
to more recent machine learning techniques. It aims to provide a framework
that will enable the reader to recognise the assumptions and constraints that
are implicit in all such techniques. Important generic issues are discussed first
and then the major families of algorithms are described. Throughout the focus
is on explanation and understanding and readers are directed to other resources
that provide additional mathematical rigour when it is required. Examples
taken from across the whole of biology, including bioinformatics, are provided
throughout the book to illustrate the key concepts and each technique’s
potential.
This GUI can be used by entering nu at the MATLAB command prompt. The user can either select a function (f(x)) of their choice or a STATISTICAL distribution probability distribution function to plot over a user defined range. The function s integral can be evaluated over a user defined range by using: The composite trapezium, simpsons and gauss-legendre rules. This is useful for calculating accurate probabilities that one might see in STATISTICAL tables.
