Keith Price Bibliography Energy Minimization, Energy Maximization Computation, Function Solving, Optimizations

13.3.8.3 Energy Minimization, Energy Maximization Computation, Function Solving, Optimizations

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Energy Minimization. Optimization. See also Markov Random Field Models. Energy Minimization techniques include: Graph Cuts, Belief Propagation and Tree-Reweighted Message Passing. Solving functions, etc. For a Subset see: See also MRF Optimization, Energy Minimization.

Greig, D., Porteous, B., Seheult, A.,
Exact Maximum a Posterori Estimation for Binary Images,
RoyalStat(B-51), No. 2, 1989, pp. 271-279. Show min-cut/max-flow algorithms can be used to minimize energy functions in vision. BibRef 8900

Gurwitz, C., Overton, M.L.,
A globally convergent algorithm for minimizing over the rotation group of quadratic forms,
PAMI(11), No. 11, November 1989, pp. 1228-1232.
IEEE Abstract.
IEEE DOI Link 0401
BibRef

Salerno, E.[Emanuele],
Guest Editorial: Fast Energy-Minimization-Based Imaging and Vision Techniques,
RealTimeImg(7), No. 1, February 2001, pp. 1-2.
WWW Version. 0106
Special issue introduction. BibRef

Pérez, P.[Patrick], Chardin, A.[Annabelle], Laferté, J.M.[Jean-Marc],
Noniterative manipulation of discrete energy-based models for image analysis,
PR(33), No. 4, April 2000, pp. 573-586.
WWW Version. 0002
BibRef

Figueiredo, M.A.T.[Mario A.T.], Hancock, E.R.[Edwin R.], Pelillo, M.[Marcello], Zerubia, J.B.[Josiane B.],
Guest editors' Introduction to the special section on energy minimization methods in computer vision and pattern recognition,
PAMI(25), No. 11, November 2003, pp. 1361-1363.
IEEE Abstract. BibRef 0311
And: PAMI(26), No. 2, February 2004, pp. 145-146.
IEEE Abstract. 0311
Pose the task as the minimization of an energy measure, then a variety of optimization methods can be applied. Includes relaxation, regularization, active contours, Markov models. BibRef

Kolmogorov, V.,
Convergent Tree-Reweighted Message Passing for Energy Minimization,
PAMI(28), No. 10, October 2006, pp. 1568-1583.
IEEE DOI Link 0609
See also MAP Estimation via Agreement on (Hyper)Trees: Message-Passing and Linear-Programming Approaches. BibRef

Kolmogorov, V.,
Graph Based Algorithms for Scene Reconstruction from Two or More Views,
Ph.D.Thesis, Cornell University, September 2003.
HTML Version. BibRef 0309

Paragios, N.[Nikos], Zabih, R.[Ramin],
Discrete optimization in computer vision,
CVIU(112), No. 1, October 2008, pp. 1-2.
WWW Version. 0810
BibRef
And: Corrigendum: CVIU(113), No. 4, April 2009, pp. 588.
Elsevier DOI Link
WWW Version. 0903
Special section introduction. BibRef

Preusser, T.[Tobias], Scharr, H.[Hanno], Krajsek, K.[Kai], Kirby, R.M.[Robert M.],
Building Blocks for Computer Vision with Stochastic Partial Differential Equations,
IJCV(80), No. 3, December 2008, pp. xx-yy.
Springer DOI Link 0810
Get PDF in addition to the solution to the value at the pixel. BibRef

Rao, S.[Sudhir], de Medeiros Martins, A.[Allan], Principe, J.C.[Jose C.],
Mean shift: An information theoretic perspective,
PRL(30), No. 3, 1 February 2009, pp. 222-230.
Elsevier DOI Link
WWW Version. 0804
Mean shift; Information theoretic learning; Renyi's entropy Analysis of mean shift. Gaussian bluring MS and Gaussian MS. BibRef

Simila, T.[Timo], Tikka, J.[Jarkko],
Combined input variable selection and model complexity control for nonlinear regression,
PRL(30), No. 3, 1 February 2009, pp. 231-236.
Elsevier DOI Link
WWW Version. 0804
Regression; Function approximation; MLP; Multilayer perceptron; Input variable selection; Hidden node selection BibRef

Rodriguez, P.[Paul], Wohlberg, B.[Brendt],
Efficient Minimization Method for a Generalized Total Variation Functional,
IP(18), No. 2, February 2009, pp. 322-332.
IEEE DOI Link 0901
BibRef
And:
A generalized vector-valued total variation algorithm,
ICIP09(1309-1312).
IEEE DOI Link 0911
BibRef

Rodriguez, P.[Paul],
Multiplicative updates algorithm to minimize the generalized total variation functional with a non-negativity constraint,
ICIP10(2509-2512).
IEEE DOI Link 1009
BibRef

Bar, L.[Leah], Sapiro, G.[Guillermo],
Generalized Newton-Type Methods For Energy Formulations In Image Processing,
SIIMS(2), No. 2, 2009, pp. 508-531.
WWW Version.
WWW Version. BibRef 0900
Earlier:
Generalized Newton methods for energy formulations in image procesing,
ICIP08(809-812).
IEEE DOI Link 0810
Newton method; variational methods; trust-region; generalized inner product; geometric active contours; deblurring BibRef

Bar, L.[Leah], Sapiro, G.[Guillermo],
Hierarchical invariant sparse modeling for image analysis,
ICIP11(2397-2400).
IEEE DOI Link 1201
BibRef

Wang, X.Y.[Xing-Yuan], Song, W.J.[Wen-Jing], Zou, L.X.[Li-Xian],
Julia Set of the Newton Method for Solving Some Complex Exponential Equation,
IJIG(9), No. 2, April 2009, pp. 153-169. 0905
BibRef

Hwang, J.K., Li, Y.P.,
Variable Step-Size LMS Algorithm With a Gradient-Based Weighted Average,
SPLetters(16), No. 12, December 2009, pp. 1043-1046.
IEEE DOI Link 0909
BibRef

Yu, D.[Dong], Deng, L.[Li], Acero, A.[Alex],
Using continuous features in the maximum entropy model,
PRL(30), No. 14, 15 October 2009, pp. 1295-1300,.
Elsevier DOI Link
WWW Version. 0909
Maximum entropy principle; Spline interpolation; Continuous feature; Maximum entropy model; Moment constraint; Distribution constraint BibRef

Leung, S.Y.[Shing-Yu], Liang, G.[Gang], Solna, K.[Knut], Zhao, H.[Hongkai],
Expectation-Maximization Algorithm With Local Adaptivity,
SIIMS(2), No. 3, 2009, pp. 834-857.
WWW Version.
WWW Version. expectation-maximization algorithm; Gaussian mixture model; posterior probability; local adaptivity; image segmentation BibRef 0900

Xu, S.G.[Song-Gang], Zhang, Y.X.[Yun-Xiang], Yong, J.H.[Jun-Hai],
A Fast Sweeping Method for Computing Geodesics on Triangular Manifolds,
PAMI(32), No. 2, February 2010, pp. 231-241.
IEEE DOI Link 1001
Computations for graphics applications. BibRef

Zeng, T., Ng, M.K.[Michael K.],
On the Total Variation Dictionary Model,
IP(19), No. 3, March 2010, pp. 821-825.
IEEE DOI Link 1003
BibRef

Pang, Y., Li, X.L., Yuan, Y.,
Robust Tensor Analysis With L1-Norm,
CirSysVideo(20), No. 2, February 2010, pp. 172-178.
IEEE DOI Link 1003
General computational technique. BibRef

Ghanem, B.[Bernard], Ahuja, N.[Narendra],
Dinkelbach NCUT: An Efficient Framework for Solving Normalized Cuts Problems with Priors and Convex Constraints,
IJCV(89), No. 1, August 2010, pp. xx-yy.
Springer DOI Link 1004
Dinkelbach NCUT. Normalized graph cut. BibRef

Alexeev, B., Ward, R.,
On the Complexity of Mumford-Shah-Type Regularization, Viewed as a Relaxed Sparsity Constraint,
IP(19), No. 10, October 2010, pp. 2787-2789.
IEEE DOI Link 1003
Inverse problems are NP-hard in general unlike Mumford-Shah functional, thus it can't solve them exactly. BibRef

Domingues, M.A.O.[Marco A.O.], de Souza, R.M.C.R.[Renata M.C.R.], Cysneiros, F.J.A.[Francisco Jose A.],
A robust method for linear regression of symbolic interval data,
PRL(31), No. 13, 1 October 2010, pp. 1991-1996.
Elsevier DOI Link
WWW Version. 1003
Symbolic interval-valued data; Symbolic data analysis; Symmetrical linear regression; Outliers BibRef

Mirebeau, J.M.[Jean-Marie], Cohen, A.[Albert],
Anisotropic Smoothness Classes: From Finite Element Approximation to Image Models,
JMIV(38), No. 1, September 2010, pp. 52-69.
WWW Version. 1011
For jump discontinuities along piecewise smooth curves. Use rather than total variation semi-norm. BibRef

Yin, W.T.[Wo-Tao],
Analysis and Generalizations of the Linearized Bregman Method,
SIIMS(3), No. 4, 2010, pp. 856-877.
WWW Version.
WWW Version. 1011
Bregman; linearized Bregman; compressed sensing; L_1-minimization; basis pursuit BibRef

Mandic, D.P., Jahanchahi, C., Took, C.C.,
A Quaternion Gradient Operator and Its Applications,
SPLetters(18), No. 1, January 2011, pp. 47-50.
IEEE DOI Link 1101
calculations on quaternions BibRef

Ranganathan, A.[Ananth], Yang, M.H.[Ming-Hsuan], Ho, J.,
Online Sparse Gaussian Process Regression and Its Applications,
IP(19), No. 2, February 2011, pp. 391-404.
IEEE DOI Link 1102
BibRef
Earlier: A1, A2, Only:
Online Sparse Matrix Gaussian Process Regression and Vision Applications,
ECCV08(I: 468-482).
Springer DOI Link 0810
BibRef

Yuan, H.Q.[Hua-Qiang], Ye, Y.D.[Yang-Dong],
Iterative sIB algorithm,
PRL(32), No. 4, 1 March 2011, pp. 606-614.
Elsevier DOI Link
WWW Version. 1102
Information Bottleneck; sIB algorithm; Mutation; Mutual information. sIB: sequential Information Bottleneck. Optimization technique. BibRef