4 Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar

4.1 Regularization Theory and Practice

Tikhonov, A.N.,
The Regularization of Ill-Posed Problems,
Dokl. Akad. Nauk.(SSR 153), No. 1, 1963, pp. 49-52. BibRef 6300

Arsenin, V.Y.,
Regularization Method,
USSR Computational Math(8), 1968. BibRef 6800

Good, I.J., Gaskins, R.A.,
Nonparametric Roughness Penalties for Preobability Densities,
Biometrika(58), 1971, pp. 255-277. BibRef 7100

Shahraray, B., and Anderson, D.J.,
Optimal Estiamtion of Contour Properties by Cross-Validated Regularization,
PAMI(11), No. 6, June 1989, pp. 600-610.
IEEE Abstract. IEEE Top Reference.
WWW Version. Analysis of parameters in regularization. BibRef 8906

Lee, D., and Pavlidis, T.,
One-Dimensional Regularization with Discontinuities,
PAMI(10), No. 6, November 1988, pp. 822-829.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 8811
Earlier: ICCV87(572-577). BibRef

Terzopoulos, D.[Demetri],
Regularization of Inverse Visual Problems Involving Discontinuities,
PAMI(8), No. 4, July 1986, pp. 413-424. A proposal of stabilizing functions for use in inverse vision problems. There are a lot of references, and this may really go with his relaxation papers. BibRef 8607

Terzopoulos, D.[Demetri],
Visual Modelling,
BMVC91(xx-yy).
PDF Version. 9109
BibRef

Terzopoulos, D.[Demetri],
Controlled-Smoothness Stabilizers fo the Regularization of Ill-Posed Visual Problems Involving Discontinuities,
DARPA84(225-229). BibRef 8400

Poggio, T., and Girosi, F.,
Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks,
Science(247), No. 4945, February 23, 1990. BibRef 9002

Girosi, F., Jones, M.J., Poggio, T.,
Regularization Theory and Neural Networks Architectures,
NeurComp(7), No. 2, March 1995, pp. 219-269. BibRef 9503

Poggio, T., and Girosi, F.,
Networks for Approximation and Learning,
PIEEE(78), No. 9, September 1990, pp. 1481-1497. BibRef 9009
Earlier:
A Theory of Networks for Approximation and Learning,
MIT AI-TR-1140, 1989. BibRef

Poggio, T.A., Torre, V., and Koch, C.,
Computational Vision and Regularization Theory,
Nature(317), 1985, pp. 314-319. BibRef 8500
Earlier: without A3:
Ill-Posed Problems and Regularization Analysis in Early Vision,
DARPA84(257-263). BibRef
And: MIT AI Memo-773, April 1984.
WWW Version. Computational Vision. A presentation of the basics of regularization and what it is intended to solve. BibRef

Taratorin, A.M., Sideman, S.,
Constrained regularized differentiation,
PAMI(16), No. 1, January 1994, pp. 88-92.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401
BibRef

Marroquin, J.L.[Jose L.], Velasco, F.A.[Fernando A.], Rivera, M.[Mariano], and Nakamura, M.[Miguel],
Gauss-Markov Measure Field Models for Low-Level Vision,
PAMI(23), No. 4, April 2001, pp. 337-348.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0104
Model using Bayesian Estimation Theory with prior MRF models. Applied to segmentation, texture directions, classification, quantization. BibRef

Marroquin, J.L., Mitter, S.K., and Poggio, T.A.,
Probabilistic Solution of Ill-Posed Problems in Computational Vision,
ASAJ(82), No. 397, March 1987, pp. 76-89. BibRef 8703
Earlier: DARPA85(293-309). BibRef
And: MIT AI Memo-97, March 1987. BibRef

Marroquin, J.L.,
Deterministic Bayesian Estimation of Markovian Random Fields with Applications to Computational Vision,
ICCV87(597-601). BibRef 8700

Marroquin, J.L.[Jose Luis],
Probabilistic Solution of Inverse Problems,
MIT AI-TR-860, September 1985. BibRef 8509 Ph.D.Thesis. 1985.
WWW Version. BibRef

Bertero, M., Poggio, T.A., and Torre, V.,
Ill-Posed Problems in Early Vision,
PIEEE(76), No. 8, August 1988, pp. 869-889. BibRef 8808
Earlier: MIT AI Memo924, May 1987.
WWW Version. BibRef

Poggio, T.A.,
Early Vision: From Computational Structure to Algorithms and Parallel Hardware,
CVGIP(31), No. 2, August 1985, pp. 139-155.
WWW Version. See also Vision by Man and Machine. BibRef 8508

Verri, A.[Alessandro], Poggio, T.[Tomaso],
Regularization Theory and Shape Constraints,
MIT AI Memo-916, September 1986. BibRef 8609

Karayiannis, N.B., and Venetsanopoulos, A.N.,
Regularization Theory in Image Restoration: The Stabilizing Functional Approach,
ASSP(38), No. 7, July 1990, pp. 1155-1179. BibRef 9007

Unser, M., Aldroubi, A., and Eden, M.,
Recursive Regularization Filters: Design, Properties, and Applications,
PAMI(13), No. 3, March 1991, pp. 272-277.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9103

Thompson, A.M., Brown, J.C., Kay, J.W., and Titterington, D.M.,
A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization,
PAMI(13), No. 4, April 1991, pp. 326-339.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9104

Archer, G., Titterington, D.M.,
On Some Bayesian/Regularization Methods for Image Restoration,
IP(4), No. 7, July 1995, pp. 989-995.
IEEE DOI Link 0402
Restoration. See also Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery. BibRef

Tanaka, K., Titterington, D.M.,
Probabilistic image processing based on the Q-ising model by means of the mean field method and loopy belief propagation,
ICPR04(II: 40-43).
IEEE DOI Link 0409
BibRef

Kang, M.G., Katsaggelos, A.K.,
General Choice of the Regularization Functional in Regularized Image-Restoration,
IP(4), No. 5, May 1995, pp. 594-602.
IEEE DOI Link See also Simultaneous Multichannel Image Restoration and Estimation of the Regularization Parameters. BibRef 9505

Kang, M.G., Katsaggelos, A.K.,
Simultaneous Multichannel Image Restoration and Estimation of the Regularization Parameters,
IP(6), No. 5, May 1997, pp. 774-778.
IEEE DOI Link 9705
See also General Choice of the Regularization Functional in Regularized Image-Restoration. BibRef

Hong, M.C., Kang, M.G., and Katsaggelos, A.K.,
An Iterative Weighted Regularized Algorithm for Improving the Resolution of Video Sequences,
ICIP97(II: 474-477).
IEEE DOI Link BibRef 9700

Shulman, D.,
Regularization of Inverse Problems in Low-Level Vision While Preserving Discontinuities,
Ph.D.Thesis (CS), Univ. of Maryland, August 1990. How to deal with edges in a regularization function. BibRef 9008

Stevenson, R.L., Schmitz, B.E., Delp, E.J.,
Discontinuity Preserving Regularization of Inverse Visual Problems,
SMC(24), No. 3, March 1994, pp. 455-469. BibRef 9403

Stevenson, R.L.[Robert L.], and Delp, E.J.[Edward J.],
Fitting Curves with Discontinuities,
Robust90(xx). BibRef 9000

Reeves, S.J., and Higdon, A.C.,
Perceptual Evaluation of the Mean Square Error Choice of Regularization Parameter,
IP(4), No. 1, January 1995, pp. 107-110.
IEEE DOI Link Human evaluation of the results. BibRef 9501

Li, S.Z.,
On Discontinuity-Adaptive Smoothness Priors in Computer Vision,
PAMI(17), No. 6, June 1995, pp. 576-586.
IEEE Abstract. IEEE Top Reference.
WWW Version. Surface Reconstruction. Adaptive Smoothing. BibRef 9506

O'Sullivan, J.A.,
Roughness penalties on finite domains,
IP(4), No. 9, September 1995, pp. 1258-1268.
IEEE DOI Link 0402
Penalty functions in Regularization. BibRef

Lin, L.C., Kuo, C.C.J.,
On Theory and Regularization of Scale-Limited Extrapolation,
SP(54), No. 3, November 1996, pp. 225-237. 9701
BibRef

Charbonnier, P., Blanc-Feraud, L., Aubert, G., Barlaud, M.,
Deterministic Edge-Preserving Regularization in Computed Imaging,
IP(6), No. 2, February 1997, pp. 298-311.
IEEE DOI Link 9703
BibRef
Earlier:
Two deterministic half-quadratic regularization algorithms for computed imaging,
ICIP94(II: 168-172).
IEEE DOI Link 9411
BibRef

Koulibaly, P.M., Charbonnier, P., Blanc-Feraud, L., Laurette, I., Darcourt, J., Barlaud, M.,
Poisson statistic and half-quadratic regularization for emission tomography reconstruction algorithm,
ICIP96(II: 729-732).
IEEE DOI Link 9610
BibRef

Blanc-Feraud, L., Charbonnier, P., Aubert, G., Barlaud, M.,
Nonlinear image processing: modeling and fast algorithm for regularization with edge detection,
ICIP95(I: 474-477).
IEEE DOI Link 9510
BibRef

Aubert, G., Barlaud, M., Blanc-Feraud, L., Charbonnier, P.,
A deterministic algorithm for edge-preserving computed imaging using Legendre transform,
ICPR94(C:188-191).
IEEE DOI Link 9410
BibRef

Nikolova, M., Idier, J., Mohammad-Djafari, A.,
Inversion of Large-Support Ill-Posed Linear-Operators Using a Piecewise Gaussian MRF,
IP(7), No. 4, April 1998, pp. 571-585.
IEEE DOI Link 9804
BibRef

Radmoser, E.[Esther], Scherzer, O.[Otmar], Weickert, J.[Joachim],
Scale-Space Properties of Nonstationary Iterative Regularization Methods,
JVCIR(11), No. 2, June 2000, pp. 96-114. 0008
BibRef
Earlier:
Scale-Space Properties of Regularization Methods,
ScaleSpace99(211-222). BibRef

Gader, P.D.[Paul D.], Khabou, M.A.[Mohamed A.], Koldobsky, A.[Alexander],
Morphological regularization neural networks,
PR(33), No. 6, June 2000, pp. 935-944.
WWW Version. 0004
BibRef

Raudys, S.[Sarunas],
Scaled rotation regularization,
PR(33), No. 12, December 2000, pp. 1989-1998.
WWW Version. 0008
BibRef

de Micheli, E.[Enrico], Viano, G.A.[Giovanni Alberto],
Probabilistic regularization in inverse optical imaging,
JOSA-A(17), No. 11, November 2000, pp. 1942-1951. 0011
BibRef

de Micheli, E.[Enrico], Viano, G.A.[Giovanni Alberto],
Inverse optical imaging viewed as a backward channel communication problem,
JOSA-A(26), No. 6, June 2009, pp. 1393-1402.
WWW Version. 0906
BibRef

Chambolle, A., Lucier, B.J.,
Interpreting translation-invariant wavelet shrinkage as a new image smoothing scale space,
IP(10), No. 7, July 2001, pp. 993-1000.
IEEE DOI Link 0108
BibRef

Rivera, M.[Mariano], Marroquin, J.L.[Jose L.],
Efficient half-quadratic regularization with granularity control,
IVC(21), No. 4, April 2003, pp. 345-357.
WWW Version. 0301
BibRef

Hinterberger, W.[Walter], Hintermüller, M.[Michael], Kunisch, K.[Karl], von Oehsen, M.[Markus], Scherzer, O.[Otmar],
Tube Methods for BV Regularization,
JMIV(19), No. 3, November 2003, pp. 219-235.
WWW Version. 0310
Bounded variation regularization. BibRef

Scherzer, O.[Otmar],
Taut-String Algorithm and Regularization Programs with G-Norm Data Fit,
JMIV(23), No. 2, September 2005, pp. 135-143.
Springer DOI Link 0505
BibRef

Fuchs, M.[Matthias], Scherzer, O.[Otmar],
Regularized Reconstruction of Shapes with Statistical a priori Knowledge,
IJCV(79), No. 2, August 2008, pp. xx-yy.
Springer DOI Link 0711
BibRef

Vanzella, W.[Walter], Pellegrino, F.A.[Felice Andrea], Torre, V.[Vincent],
Self-Adaptive Regularization,
PAMI(26), No. 6, June 2004, pp. 804-809.
IEEE Abstract. IEEE Top Reference. 0404
Adapting the parameters for Mumford-Shah See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. to optimize details. BibRef

Shkvarko, Y.V.,
Unifying Regularization and Bayesian Estimation Methods for Enhanced Imaging With Remotely Sensed Data-Part I: Theory,
GeoRS(42), No. 5, May 2004, pp. 923-931.
IEEE Abstract. IEEE Top Reference. 0407
BibRef

Shkvarko, Y.V.,
Unifying Regularization and Bayesian Estimation Methods for Enhanced Imaging With Remotely Sensed Data-Part II: Implementation and Performance Issues,
GeoRS(42), No. 5, May 2004, pp. 932-940.
IEEE Abstract. IEEE Top Reference. 0407
BibRef

Shkvarko, Y.V.[Yuriy V.], Vazquez-Bautista, R.[Rene], Villalon-Turrubiates, I.E.[Ivan E.],
Fusion of Bayesian Maximum Entropy Spectral Estimation and Variational Analysis Methods for Enhanced Radar Imaging,
ACIVS07(109-120).
Springer DOI Link 0708
BibRef

Shkvarko, Y.V.[Yuri V.], Netjukhailo, A.S.[Alexey S.],
Fusion of Bayesian estimation and MTF inversion techniques for improved array imaging in scattering media,
CAIP95(526-531).
Springer DOI Link 9509
BibRef

Shkvarko, Y.V., Leyva-Montiel, J.L., Villalon-Turrubiates, I.E.[Ivan E.],
Unifying the Experiment Design and Constrained Regularization Paradigms for Reconstructive Imaging with Remote Sensing Data,
ICIP06(3241-3244). 0610

IEEE DOI Link BibRef

Viéville, T.[Thierry],
An unbiased implementation of regularization mechanisms,
IVC(23), No. 11, 1 October 2005, pp. 981-998.
WWW Version. 0510
BibRef

Viéville, T.[Thierry],
Biologically plausible regularization mechanisms,
INRIARR-4625, Novembre 2002.
HTML Version. 0306
BibRef

Gutierrez, J., Ferri, F.J., Malo, J.,
Regularization Operators for Natural Images Based on Nonlinear Perception Models,
IP(15), No. 1, January 2006, pp. 189-200.
IEEE DOI Link 0601
BibRef

Allain, M., Idier, J., Goussard, Y.,
On Global and Local Convergence of Half-Quadratic Algorithms,
IP(15), No. 5, May 2006, pp. 1130-1142.
IEEE DOI Link 0605
BibRef
Earlier: ICIP02(II: 833-836).
IEEE Abstract. IEEE Top Reference. 0210
BibRef

Mignotte, M.[Max],
A Segmentation-Based Regularization Term for Image Deconvolution,
IP(15), No. 7, July 2006, pp. 1973-1984.
IEEE DOI Link 0606
BibRef
Earlier:
An Adaptive Segmentation-Based Regularization Term for Image Restoration,
ICIP05(I: 901-904).
IEEE DOI Link 0512
BibRef

Mignotte, M.[Max],
A non-local regularization strategy for image deconvolution,
PRL(29), No. 16, 1 December 2008, pp. 2206-2212.
WWW Version. 0811
Image deconvolution or restoration; Non-local regularization; Penalized likelihood; L-curve estimation BibRef

He, L.[Lin], Burger, M.[Martin], Osher, S.J.[Stanley J.],
Iterative Total Variation Regularization with Non-Quadratic Fidelity,
JMIV(26), No. 1-2, November 2006, pp. 167-184.
Springer DOI Link 0701
See also Variational Problems and Partial Differential Equations on Implicit Surfaces. BibRef

Goldstein, T.[Tom], Osher, S.J.[Stanley J.],
The Split Bregman Method For L1-Regularized Problems,
SIIMS(2), No. 2, 2009, pp. 323-343. constrained optimization; L1-regularization; compressed sensing; total variation denoising
WWW Version.
WWW Version. BibRef 0900

Xu, J., Osher, S.J.[Stanley J.],
Iterative Regularization and Nonlinear Inverse Scale Space Applied to Wavelet-Based Denoising,
IP(16), No. 2, February 2007, pp. 534-544.
IEEE DOI Link 0702
BibRef

Grasmair, M.[Markus],
The Equivalence of the Taut String Algorithm and BV-Regularization,
JMIV(27), No. 1, January 2007, pp. 59-66.
Springer DOI Link 0702
BibRef

Grasmair, M.[Markus],
Locally Adaptive Total Variation Regularization,
SSVM09(331-342).
Springer DOI Link 0906
BibRef

Lie, J.[Johan], Nordbotten, J.M.[Jan M.],
Inverse Scale Spaces for Nonlinear Regularization,
JMIV(27), No. 1, January 2007, pp. 41-50.
Springer DOI Link 0702
BibRef

Laligant, O., Truchetet, F., Meriaudeau, F.,
Regularization Preserving Localization of Close Edges,
SPLetters(14), No. 3, March 2007, pp. 185-188.
IEEE DOI Link 0703
BibRef

Le Hgarat-Mascle, S., Kallel, A., Descombes, X.,
Ant Colony Optimization for Image Regularization Based on a Nonstationary Markov Modeling,
IP(16), No. 3, March 2007, pp. 865-878.
IEEE DOI Link 0703
BibRef

Bioucas-Dias, J.M.[Jose M.], Figueiredo, M.A.T.[Mario A.T.],
A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration,
IP(16), No. 12, December 2007, pp. 2992-3004.
IEEE DOI Link 0711
BibRef
Earlier:
Two-Step Algorithms for Linear Inverse Problems with Non-Quadratic Regularization,
ICIP07(I: 105-108).
IEEE DOI Link 0709
BibRef

Bioucas-Dias, J.M.[Jose M.], Figueiredo, M.A.T.[Mario A.T.],
An iterative algorithm for linear inverse problems with compound regularizers,
ICIP08(685-688).
IEEE DOI Link 0810
BibRef

Steinke, F.[Florian], Scholkopf, B.[Bernhard],
Kernels, regularization and differential equations,
PR(41), No. 11, November 2008, pp. 3271-3286.
WWW Version. 0808
Positive definite kernel; Differential equation; Gaussian process; Reproducing kernel Hilbert space BibRef

Erdem, E.[Erkut], Tari, S.[Sibel],
Mumford-Shah Regularizer with Contextual Feedback,
JMIV(33), No. 1, January 2009, pp. xx-yy.
Springer DOI Link 0804
BibRef

Erdem, E.[Erkut], Sancar-Yilmaz, A.[Aysun], Tari, S.[Sibel],
Mumford-Shah Regularizer with Spatial Coherence,
SSVM07(545-555).
Springer DOI Link 0705
BibRef

Ban, S.J., Lee, C.W., Kim, S.W.,
Adaptive Regularization Parameter for Pseudo Affine Projection Algorithm,
SPLetters(16), No. 5, May 2009, pp. 382-385.
IEEE DOI Link 0903
BibRef

Beck, A.[Amir], Teboulle, M.[Marc],
A Fast Iterative Shrinkage-Thresholding Algorithm For Linear Inverse Problems,
SIIMS(2), No. 1, 2009, pp. 183-202. iterative shrinkage-thresholding algorithm; deconvolution; linear inverse problem; least squares and L_1 regularization problems; optimal gradient method; global rate of convergence; two-step iterative algorithms; image deblurring
WWW Version.
WWW Version. BibRef 0900

Allard, W.K.[William K.],
Total Variation Regularization For Image Denoising, III. Examples.,
SIIMS(2), No. 2, 2009, pp. 532-568. total variation; regularization; denoising
WWW Version.
WWW Version. 0905
BibRef

Hahn, J.Y.[Joo-Young], Lee, C.O.[Chang-Ock],
A Nonlinear Structure Tensor with the Diffusivity Matrix Composed of the Image Gradient,
JMIV(34), No. 2, June 2009, pp. xx-yy.
Springer DOI Link 0906
Nonlinear PDE for regularization. BibRef

Sastry, C.S.,
Regularization of Incompletely, Irregularly and Randomly Sampled Data,
ICCVGIP08(158-162).
IEEE DOI Link 0812
BibRef

Lin, Y.[Youzuo], Wohlberg, B.[Brendt],
Application of the UPRE Method to Optimal Parameter Selection for Large Scale Regularization Problems,
Southwest08(89-92).
IEEE DOI Link 0803
BibRef

Chartrand, R.[Rick],
Nonconvex Regularization for Shape Preservation,
ICIP07(I: 293-296).
IEEE DOI Link 0709
BibRef

Chang, H.H.[Hsun-Hsien], Moura, J.M.F.[Jose M. F.],
Classification by Cheeger Constant Regularization,
ICIP07(II: 209-212).
IEEE DOI Link 0709
BibRef

Lin, Z.[Zhu], Islam, M.S.,
An Adaptive Edge-Preserving Variational Framework for Color Image Regularization,
ICIP05(I: 101-104).
IEEE DOI Link 0512
BibRef

Chan, R.H., Ho, C.W.[Chung-Wa], Leung, C.Y.[Chun-Yee], Nikolova, M.,
Minimization of Detail-preserving Regularization Functional by Newton's Method with Continuation,
ICIP05(I: 125-128).
IEEE DOI Link 0512
BibRef

Zhou, D.Y.[Deng-Yong], Schölkopf, B.[Bernhard],
Regularization on Discrete Spaces,
DAGM05(361).
Springer DOI Link 0509
BibRef

Florack, L.M.J.[Luc M.J.],
Codomain scale space and regularization for high angular resolution diffusion imaging,
Tensor08(1-6).
IEEE DOI Link 0806
BibRef

Florack, L.M.J., Duits, R.[Remco], Bierkens, J.,
Tikhonov regularization versus scale space: A new result,
ICIP04(I: 271-274).
IEEE DOI Link 0505
BibRef

Yang, C.J.[Chang-Jiang], Duraiswami, R., Davis, L.S.,
Near-optimal regularization parameters for applications in computer vision,
ICPR02(II: 569-573).
IEEE DOI Link 0211
BibRef

Nikolova, M., Ng, M.,
Comparison of the main forms of half-quadratic regularization,
ICIP02(I: 349-352).
IEEE Abstract. IEEE Top Reference. 0210
BibRef

Oraintara, S., Karl, W.C., Castanon, D.A., Nguyen, T.,
A Method for Choosing the Regularization Parameter in Generalized Tikhonov Regularized Linear Inverse Problems,
ICIP00(Vol I: 93-96).
IEEE Abstract. IEEE Top Reference. 0008
BibRef

Yang, Z.Y.[Zhi-Yong], Ma, S.D.[Song-De],
Beyond standard regularization theory,
CAIP97(289-296).
WWW Version. 9709
BibRef

Froehlinghaus, T., Buhmann, J.,
Regularizing Phase Based Stereo,
ICPR96(I: 451-455).
IEEE DOI Link 9608
(Rheinische Fr.-Wihelms-Univ., D) BibRef

Gunsel, B., Guzelis, C.,
Supervised learning of smoothing parameters in image restoration by regularization under cellular neural networks framework,
ICIP95(I: 470-473).
IEEE DOI Link 9510
BibRef

Howard, C.G., Bock, P.,
Using a hierarchical approach to avoid over-fitting in early vision,
ICPR94(A:826-829).
IEEE DOI Link 9410
BibRef

Boult, T.E.,
Optimal Algorithms: Tools for Mathematical Modeling,
Complexity(3), 1987, pp. 183-200. BibRef 8700
And:
Using Optimal Algorithms to Test Model Assumptions in Computer Vision,
DARPA87(921-926). BibRef

Boult, T.E.,
What is Regular in Regularization?,
ICCV87(457-462). A look at regularization and some alternatives. BibRef 8700

Szeliski, R.,
Regularization Uses Fractal Priors,
AAAI-87(749-754). BibRef 8700

Hummel, R., Moniot, R.,
Solving Ill-Conditioned Problems by Minimizing Equation Error,
ICCV87(527-533). BibRef 8700

Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Connectionist Approaches to Computer Vision .