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A Progressive Radiosity Algorithm Based on Piecewise Polynomial Intensity Distribution
 Computers & Graphics
, 1997
"... A progressive radiosity algorithm based on piecewise polynomial intensity distribution is presented in the paper. Unlike the conventional radiosity method, radiosity across each patch is assumed to vary in a polynomial distribution. A generalized radiosity equation for scenes with nondiffuse surface ..."
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Cited by 1 (0 self)
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A progressive radiosity algorithm based on piecewise polynomial intensity distribution is presented in the paper. Unlike the conventional radiosity method, radiosity across each patch is assumed to vary in a polynomial distribution. A generalized radiosity equation for scenes with nondiffuse
Computer Graphics in China A PROGRESSIVE RADIOSITY ALGORITHM BASED ON PIECEWISE POLYNOMIAL INTENSITY DISTRIBUTION
"... AbstractA progressive radiosity algorithm based on piecewise polynomial intensity distribution is presented in the paper. Unlike the conventional radiosity method, raciosity across each patch is assumed to vary in a polynomial distribution. A generalized radiosity equation for scenes with nondiffu ..."
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AbstractA progressive radiosity algorithm based on piecewise polynomial intensity distribution is presented in the paper. Unlike the conventional radiosity method, raciosity across each patch is assumed to vary in a polynomial distribution. A generalized radiosity equation for scenes with non
A rapid hierarchical radiosity algorithm
 Computer Graphics
, 1991
"... This paper presents a rapid hierarchical radiosity algorithm for illuminating scenes containing lar e polygonal patches. The afgorithm constructs a hierarchic“J representation of the form factor matrix by adaptively subdividing patches into su bpatches according to a usersupplied error bound. The a ..."
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Cited by 412 (11 self)
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This paper presents a rapid hierarchical radiosity algorithm for illuminating scenes containing lar e polygonal patches. The afgorithm constructs a hierarchic“J representation of the form factor matrix by adaptively subdividing patches into su bpatches according to a usersupplied error bound
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
Radiosity
, 1999
"... Contents 1 Introduction 3 1.1 Realistic Image Synthesis . . . . . . . . . . . . . . . . . . . . . . 3 1.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Radiosity and Finite Element Methods . . . . . . . . . . . . . . . 4 2 Rendering Concepts 5 2.1 Summary of Photomet ..."
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of Photometric and Rendering Terms . . . . . . . . . . 5 2.1.1 Radiometric Terms . . . . . . . . . . . . . . . . . . . . . . 5 2.1.2 The Bidirectional ReAEection Distribution Function . . . . 5 2.2 The Rendering Equation . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 The Radiosity Equation
Instancebased learning algorithms
 Machine Learning
, 1991
"... Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to ..."
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Cited by 1359 (18 self)
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to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instancebased learning, that generates classification predictions using only specific instances. Instancebased learning algorithms do not maintain a set of abstractions derived from specific instances
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
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Cited by 707 (18 self)
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The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new
Boosting a Weak Learning Algorithm By Majority
, 1995
"... We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas pr ..."
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Cited by 516 (15 self)
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We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 766 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations
 Journal of Computational Physics
, 1988
"... We devise new numerical algorithms, called PSC algorithms, for following fronts propagating with curvaturedependent speed. The speed may be an arbitrary function of curvature, and the front can also be passively advected by an underlying flow. These algorithms approximate the equations of motion, w ..."
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Cited by 1183 (64 self)
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in the moving fronts. The algorithms handle topological merging and breaking naturally, work in any number of space dimensions, and do not require that the moving surface be written as a function. The methods can be also used for more general HamiltonJacobitype problems. We demonstrate our algorithms
Results 1  10
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