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511
Estimation of relative camera positions for uncalibrated cameras
, 1992
"... Abstract. This paper considers, the determination of internal camera parameters from two views of a point set in three dimensions. A noniterative algorithm is given for determining the focal lengths of the two cameras, as well as their relative placement, assuming all other internal camera paramete ..."
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Cited by 325 (24 self)
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Abstract. This paper considers, the determination of internal camera parameters from two views of a point set in three dimensions. A noniterative algorithm is given for determining the focal lengths of the two cameras, as well as their relative placement, assuming all other internal camera parameters to be known. It is shown that this is all the information that may be deduced from a set of image correspondences. 1
Parametrization and smooth approximation of surface triangulations
 Computer Aided Geometric Design
, 1997
"... Abstract. A method based on graph theory is investigated for creating global parametrizations for surface triangulations for the purpose of smooth surface fitting. The parametrizations, which are planar triangulations, are the solutions of linear systems based on convex combinations. A particular pa ..."
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Cited by 306 (13 self)
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Abstract. A method based on graph theory is investigated for creating global parametrizations for surface triangulations for the purpose of smooth surface fitting. The parametrizations, which are planar triangulations, are the solutions of linear systems based on convex combinations. A particular parametrization, called shapepreserving, is found to lead to visually smooth surface approximations. A standard approach to fitting a smooth parametric curve c(t) through a given sequence of points xi = (xi,yi,zi) ∈ IR 3, i = 1,...,N is to first make a parametrization, a corresponding increasing sequence of parameter values ti. By finding smooth functions x,y,z: [t1,tN] → IR for which x(ti) = xi, y(ti) = yi, z(ti) = zi, an interpolatory curve
A Survey of Shape Analysis Techniques
 Pattern Recognition
, 1998
"... This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems. ..."
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Cited by 261 (2 self)
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This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems.
Optimal State Estimation
 Kalman, H Infinity, and Nonlinear Approaches, Wiley & Sons
, 2006
"... and rule base reduction of a fuzzy filter ..."
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Analog Computation via Neural Networks
 THEORETICAL COMPUTER SCIENCE
, 1994
"... We pursue a particular approach to analog computation, based on dynamical systems of the type used in neural networks research. Our systems have a fixed structure, invariant in time, corresponding to an unchanging number of "neurons". If allowed exponential time for computation, they turn ..."
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Cited by 98 (10 self)
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We pursue a particular approach to analog computation, based on dynamical systems of the type used in neural networks research. Our systems have a fixed structure, invariant in time, corresponding to an unchanging number of "neurons". If allowed exponential time for computation, they turn out to have unbounded power. However, under polynomialtime constraints there are limits on their capabilities, though being more powerful than Turing Machines. (A similar but more restricted model was shown to be polynomialtime equivalent to classical digital computation in the previous work [20].) Moreover, there is a precise correspondence between nets and standard nonuniform circuits with equivalent resources, and as a consequence one has lower bound constraints on what they can compute. This relationship is perhaps surprising since our analog devices do not change in any manner with input size. We note that these networks are not likely to solve polynomially NPhard problems, as the equality ...
A fortran90 based multiprecision system
 ACM Transactions on Mathematical Software
, 1995
"... The author has developed a new version of his Fortran multiprecision computation system that is based on the Fortran90 language. With this new approach, a translator program is not required — translation of Fortran code for multiprecision is accomplished by merely utilizing advanced features of For ..."
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Cited by 81 (17 self)
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The author has developed a new version of his Fortran multiprecision computation system that is based on the Fortran90 language. With this new approach, a translator program is not required — translation of Fortran code for multiprecision is accomplished by merely utilizing advanced features of Fortran90, such as derived data types and operator extensions. This approach results in more reliable translation and also permits programmers of multiprecision applications to utilize the full power of the Fortran90 language. Three multiprecision datatypes are supported in this system: multiprecision integer, real and complex. All the usual Fortran conventions for mixed mode operations are supported, and many of the Fortran intrinsics, such as SIN, EXP and MOD, are supported with multiprecision arguments. This paper also briefly describes an interesting application of this software, wherein new numbertheoretic identities have been discovered by means of multiprecision computations.
Hybrid Bayesian Networks for Reasoning about Complex Systems
, 2002
"... Many realworld systems are naturally modeled as hybrid stochastic processes, i.e., stochastic processes that contain both discrete and continuous variables. Examples include speech recognition, target tracking, and monitoring of physical systems. The task is usually to perform probabilistic inferen ..."
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Cited by 71 (0 self)
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Many realworld systems are naturally modeled as hybrid stochastic processes, i.e., stochastic processes that contain both discrete and continuous variables. Examples include speech recognition, target tracking, and monitoring of physical systems. The task is usually to perform probabilistic inference, i.e., infer the hidden state of the system given some noisy observations. For example, we can ask what is the probability that a certain word was pronounced given the readings of our microphone, what is the probability that a submarine is trying to surface given our sonar data, and what is the probability of a valve being open given our pressure and flow readings. Bayesian networks are
A new conjugate gradient method with guaranteed descent and an efficient line search
 SIAM J. OPTIM
, 2005
"... A new nonlinear conjugate gradient method and an associated implementation, based on an inexact line search, are proposed and analyzed. With exact line search, our method reduces to a nonlinear version of the Hestenes–Stiefel conjugate gradient scheme. For any (inexact) line search, our scheme sat ..."
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Cited by 68 (6 self)
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A new nonlinear conjugate gradient method and an associated implementation, based on an inexact line search, are proposed and analyzed. With exact line search, our method reduces to a nonlinear version of the Hestenes–Stiefel conjugate gradient scheme. For any (inexact) line search, our scheme satisfies the descent condition gT k dk ≤ − 7 8 ‖gk‖2. Moreover, a global convergence result is established when the line search fulfills the Wolfe conditions. A new line search scheme is developed that is efficient and highly accurate. Efficiency is achieved by exploiting properties of linear interpolants in a neighborhood of a local minimizer. High accuracy is achieved by using a convergence criterion, which we call the “approximate Wolfe ” conditions, obtained by replacing the sufficient decrease criterion in the Wolfe conditions with an approximation that can be evaluated with greater precision in a neighborhood of a local minimum than the usual sufficient decrease criterion. Numerical comparisons are given with both LBFGS and conjugate gradient methods using the unconstrained optimization problems in the CUTE library.
Experimental Evaluation of Euler Sums
, 1993
"... In response to a letter from Goldbach, Euler considered sums of the form 1 X k=1 ` 1 + 1 2 m + \Delta \Delta \Delta + 1 k m ' (k + 1) \Gamman for positive integers m and n. Euler was able to give explicit values for certain of these sums in terms of the Riemann zeta function. In a r ..."
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Cited by 65 (11 self)
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In response to a letter from Goldbach, Euler considered sums of the form 1 X k=1 ` 1 + 1 2 m + \Delta \Delta \Delta + 1 k m ' (k + 1) \Gamman for positive integers m and n. Euler was able to give explicit values for certain of these sums in terms of the Riemann zeta function. In a recent companion paper, Euler's results were extended to a significantly larger class of sums of this type, including sums with alternating signs. This research was facilitated by numerical computations using an algorithm that can determine, with high confidence, whether or not a particular numerical value can be expressed as a rational linear combination of several given constants. The present paper presents the numerical techniques used in these computations and lists many of the experimental results that have been obtained.
Automatic Joint Parameter Estimation from Magnetic Motion Capture Data
, 2000
"... This paper describes a technique for using magnetic motion capture data to determine the joint parameters of an articulated hierarchy. This technique makes it possible to determine limb lengths, joint locations, and sensor placement for a human subject without external measurements. Instead, the joi ..."
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Cited by 64 (1 self)
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This paper describes a technique for using magnetic motion capture data to determine the joint parameters of an articulated hierarchy. This technique makes it possible to determine limb lengths, joint locations, and sensor placement for a human subject without external measurements. Instead, the joint parameters are inferred with high accuracy from the motion data acquired during the capture session. The parameters are computed by performing a linear least squares fit of a rotary joint model to the input data. A hierarchical structure for the articulated model can also be determined in situations where the topology of the model is not known. Once the system topology and joint parameters have been recovered, the resulting model can be used to perform forward and inverse kinematic procedures. We present the results of using the algorithm on human motion capture data, as well as validation results obtained with data from a simulation and a wooden linkage of known dimensions.