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2,607
Bundle Adjustment  A Modern Synthesis
 VISION ALGORITHMS: THEORY AND PRACTICE, LNCS
, 2000
"... This paper is a survey of the theory and methods of photogrammetric bundle adjustment, aimed at potential implementors in the computer vision community. Bundle adjustment is the problem of refining a visual reconstruction to produce jointly optimal structure and viewing parameter estimates. Topics c ..."
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Cited by 562 (13 self)
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covered include: the choice of cost function and robustness; numerical optimization including sparse Newton methods, linearly convergent approximations, updating and recursive methods; gauge (datum) invariance; and quality control. The theory is developed for general robust cost functions rather than
Distortion invariant object recognition in the dynamic link architecture
 IEEE TRANSACTIONS ON COMPUTERS
, 1993
"... We present an object recognition system based on the Dynamic Link Architecture, which is an extension to classical Artificial Neural Networks. The Dynamic Link Architecture exploits correlations in the finescale temporal structure of cellular signals in order to group neurons dynamically into hig ..."
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Cited by 637 (80 self)
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matching cost function. Our implementation on a transputer network successfully achieves recognition of human faces and office objects from gray level camera images. The performance of the program is evaluated by a statistical analysis of recognition results from a portrait gallery comprising images of 87
Scalable molecular dynamics with NAMD.
 J Comput Chem
, 2005
"... Abstract: NAMD is a parallel molecular dynamics code designed for highperformance simulation of large biomolecular systems. NAMD scales to hundreds of processors on highend parallel platforms, as well as tens of processors on lowcost commodity clusters, and also runs on individual desktop and la ..."
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Cited by 849 (63 self)
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and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion
Multiple kernel learning, conic duality, and the SMO algorithm
 In Proceedings of the 21st International Conference on Machine Learning (ICML
, 2004
"... While classical kernelbased classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimiz ..."
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Cited by 445 (31 self)
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; moreover, the sequential minimal optimization (SMO) techniques that are essential in largescale implementations of the SVM cannot be applied because the cost function is nondifferentiable. We propose a novel dual formulation of the QCQP as a secondorder cone programming problem, and show how to exploit
New tight frames of curvelets and optimal representations of objects with piecewise C² singularities
 COMM. ON PURE AND APPL. MATH
, 2002
"... This paper introduces new tight frames of curvelets to address the problem of finding optimally sparse representations of objects with discontinuities along C2 edges. Conceptually, the curvelet transform is a multiscale pyramid with many directions and positions at each length scale, and needleshap ..."
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Cited by 428 (21 self)
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the wavelet decomposition of the object. For instance, the nterm partial reconstruction f C n obtained by selecting the n largest terms in the curvelet series obeys ‖f − f C n ‖ 2 L2 ≤ C · n−2 · (log n) 3, n → ∞. This rate of convergence holds uniformly over a class of functions which are C 2 except
Sparse signal reconstruction from limited data using FOCUSS: A reweighted minimum norm algorithm
 IEEE TRANS. SIGNAL PROCESSING
, 1997
"... We present a nonparametric algorithm for finding localized energy solutions from limited data. The problem we address is underdetermined, and no prior knowledge of the shape of the region on which the solution is nonzero is assumed. Termed the FOcal Underdetermined System Solver (FOCUSS), the algor ..."
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Cited by 368 (22 self)
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), the algorithm has two integral parts: a lowresolution initial estimate of the real signal and the iteration process that refines the initial estimate to the final localized energy solution. The iterations are based on weighted norm minimization of the dependent variable with the weights being a function
Contracting AutoEncoders
"... Introduction. We propose a novel approach for training deterministic autoencoders. We show that by adding a well chosen penalty term to the classical reconstruction cost function, we can achieve results that equal or surpass those attained by other regularized autoencoders as well ..."
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Introduction. We propose a novel approach for training deterministic autoencoders. We show that by adding a well chosen penalty term to the classical reconstruction cost function, we can achieve results that equal or surpass those attained by other regularized autoencoders as well
Trajectory Sampling for Direct Traffic Observation
, 2001
"... Traffic measurement is a critical component for the control and engineering of communication networks. We argue that traffic measurement should make it possible to obtain the spatial flow of traffic through the domain, i.e., the paths followed by packets between any ingress and egress point of the d ..."
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Cited by 248 (30 self)
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) the measurement reporting traffic is modest and can be controlled precisely. The key idea of the method is to sample packets based on a hash function computed over the packet content. Using the same hash function will yield the same sample set of packets in the entire domain, and enables us to reconstruct packet
The geometry of optimal transportation
 Acta Math
, 1996
"... A classical problem of transporting mass due to Monge and Kantorovich is solved. Given measures µ and ν on R d, we find the measurepreserving map y(x) between them with minimal cost — where cost is measured against h(x − y) withhstrictly convex, or a strictly concave function of x − y. This map i ..."
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Cited by 240 (33 self)
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A classical problem of transporting mass due to Monge and Kantorovich is solved. Given measures µ and ν on R d, we find the measurepreserving map y(x) between them with minimal cost — where cost is measured against h(x − y) withhstrictly convex, or a strictly concave function of x − y. This map
A Theory of Networks for Approximation and Learning
 Laboratory, Massachusetts Institute of Technology
, 1989
"... Learning an inputoutput mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensional function, that is solving the problem of hypersurface reconstruction. From this point of view, t ..."
Abstract

Cited by 235 (24 self)
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Learning an inputoutput mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensional function, that is solving the problem of hypersurface reconstruction. From this point of view
Results 1  10
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2,607