7.10.11 Statistical Image Models

Chapter Contents (Back)
Image Models.

Kretzmer, E.R.,
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Swerling, P.,
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Pawula, R.F.,
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Hunt, B.R., Cannon, T.M.,
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See also Comments on Nonstationary Assumptions for Gaussian Models of Images. BibRef 7612

Hunt, B.R.,
Nonstationary Statistical Image Models (and Their Application to Image Data compression),
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Comments on Nonstationary Assumptions for Gaussian Models of Images,
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McClure, D.E.[Donald E.],
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Kadota, T.T.,
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Kadota, T.T., Seery, J.B.,
Probability Distributions of Randomly Moving Objects on a Plane,
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Kashyap, R.L.,
Orientation of Anisotropic Random Fields and Images,
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Dunn, S.M., Keizer, R.L., Rosenfeld, A.,
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Jeulin, D.,
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Pikaz, A.[Arie], Averbuch, A.[Amir],
On the Relation between Second-Order Statistics, Connectivity Analysis, and Percolation Models in Digital Textures,
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Aykroyd, R.G.[Robert G.],
Bayesian Estimation for Homogeneous and Inhomogeneous Gaussian Random Fields,
PAMI(20), No. 5, May 1998, pp. 533-539.
IEEE DOI 9806
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Sun, C.M.[Chang-Ming],
Fast Algorithm for Local Statistics Calculation for N -Dimensional Images,
RealTimeImg(7), No. 6, December 2001, pp. 519-527.
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Eom, K.B.,
Long-correlation image models for textures with circular and elliptical correlation structures,
IP(10), No. 7, July 2001, pp. 1047-1055.
IEEE DOI 0108
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Srivastava, A., Lee, A.B., Simoncelli, E.P., Zhu, S.C.,
On Advances in Statistical Modeling of Natural Images,
JMIV(18), No. 1, January 2003, pp. 17-33.
DOI Link 0301
BibRef

Lee, A.B.[Ann B.], Pedersen, K.S.[Kim S.], Mumford, D.[David],
The Nonlinear Statistics of High-Contrast Patches in Natural Images,
IJCV(54), No. 1-3, August 2003, pp. 83-103.
DOI Link 0306
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Earlier:
The Complex statistics of high contrast patches in natural images,
SCTV01(xx-yy). 0106
BibRef

Steenstrup Pedersen, K.[Kim], Duits, R.[Remco], Nielsen, M.[Mads],
On a Kernels, Lévy Processes, and Natural Image Statistics,
ScaleSpace05(468-479).
Springer DOI 0505
BibRef

Deléchelle, É.[Éric], Nunes, J.C.[Jean-Claude], Lemoine, J.[Jacques],
Empirical mode decomposition synthesis of fractional processes in 1D- and 2D-space,
IVC(23), No. 9, 1 September 2005, pp. 799-806.
Elsevier DOI 0508
Gaussian, Brownian texture models. BibRef

Niang, O., Thioune, A., Gueirea, M.C.E., Deléchelle, É.[Éric], Lemoine, J.[Jacques],
Partial Differential Equation-Based Approach for Empirical Mode Decomposition: Application on Image Analysis,
IP(21), No. 9, September 2012, pp. 3991-4001.
IEEE DOI 1208
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Lillholm, M.[Martin], Nielsen, M.[Mads], Griffin, L.D.[Lewis D.],
Feature-Based Image Analysis,
IJCV(52), No. 2-3, May-June 2003, pp. 73-95.
DOI Link 0301

See also Superficial and deep structure in linear diffusion scale space: isophotes, critical points and separatrices. BibRef

Lillholm, M.[Martin], Griffin, L.D.[Lewis D.],
Statistics and category systems for the shape index descriptor of local 2nd order natural image structure,
IVC(27), No. 6, 4 May 2009, pp. 771-781.
Elsevier DOI 0904
BibRef
Earlier:
Novel image feature alphabets for object recognition,
ICPR08(1-4).
IEEE DOI 0812
Local image structure; Image features; Natural image statistics BibRef

Griffin, L.D.[Lewis D.], Lillholm, M.[Martin], Crosier, M.S.[Mike S.], van Sande, J.[Justus],
Basic Image Features (BIFs) Arising from Approximate Symmetry Type,
SSVM09(343-355).
Springer DOI 0906
BibRef

Griffin, L.D.[Lewis D.], Lillholm, M.[Martin],
Symmetry Sensitivities of Derivative-of-Gaussian Filters,
PAMI(32), No. 6, June 2010, pp. 1072-1083.
IEEE DOI 1004
Filters can be sensitive to a symmetry. BibRef

Nielsen, M., Lillholm, M.,
What do features tell about images?,
ScaleSpace01(xx-yy). 0106
BibRef

Crosier, M.S.[Michael S.], Griffin, L.D.[Lewis D.],
Using Basic Image Features for Texture Classification,
IJCV(88), No. 3, July 2010, pp. xx-yy.
Springer DOI 1003
BibRef
Earlier:
Texture classification with a dictionary of basic image features,
CVPR08(1-7).
IEEE DOI 0806
BibRef

Griffin, L.D.[Lewis D.], Lillholm, M.[Martin],
Hypotheses for Image Features, Icons and Textons,
IJCV(70), No. 3, December 2006, pp. 213-230.
Springer DOI 0608
BibRef
Earlier:
Image Features and the 1-D, 2nd Order Gaussian Derivative Jet,
ScaleSpace05(26-37).
Springer DOI 0505
BibRef

Griffin, L.D.[Lewis D.], Lillholm, M.[Martin],
Mode Estimation Using Pessimistic Scale Space Tracking,
ScaleSpace03(266-280).
Springer DOI 0310
BibRef

Tagliati, E., Griffin, L.D.,
Features in scale-space: progress on the 2D 2nd order jet,
ScaleSpace01(xx-yy). 0106
BibRef

Griffin, L.D.[Lewis D.],
The Second Order Local-Image-Structure Solid,
PAMI(29), No. 8, August 2007, pp. 1355-1366.
IEEE DOI 0707
BibRef

Griffin, L.D.[Lewis D.],
The Atlas Structure of Images,
PAMI(41), No. 1, January 2019, pp. 234-245.
IEEE DOI 1812
Apertures, IP networks, Glass, Filtering theory, Kernel, Convolution, Image analysis, image representation, keypoints BibRef

Lillholm, M.[Martin], Griffin, L.D.[Lewis D.],
Maximum Likelihood Metameres for Local 2nd Order Image Structure of Natural Images,
SSVM07(394-405).
Springer DOI 0705
BibRef

Griffin, L.D.[Lewis D.],
Symmetries of 1-D Images,
JMIV(31), No. 2-3, July 2008, pp. 157-164.
WWW Link. 0711
BibRef

Griffin, L.D.[Lewis D.],
Symmetries of 2-D Images: Cases without Periodic Translations,
JMIV(34), No. 3, July 2009, pp. xx-yy.
Springer DOI 0906
BibRef

Marchant, R.[Ross], Jackway, P.T.[Paul T.],
A Sinusoidal Image Model Derived from the Circular Harmonic Vector,
JMIV(57), No. 2, February 2017, pp. 138-163.
WWW Link. 1702
BibRef
Earlier:
Feature Detection from the Maximal Response to a Spherical Quadrature Filter Set,
DICTA12(1-8).
IEEE DOI 1303
BibRef

Bu, X.Y.[Xing-Yuan], Wu, Y.W.[Yu-Wei], Gao, Z.[Zhi], Jia, Y.D.[Yun-De],
Deep convolutional network with locality and sparsity constraints for texture classification,
PR(91), 2019, pp. 34-46.
Elsevier DOI 1904
Deep convolutional feature, Sparse coding, Locality-aware, Texture classification BibRef


Samuel, K.G.G.[Kegan G.G.], Tappen, M.F.[Marshall F.],
Learning optimized MAP estimates in continuously-valued MRF models,
CVPR09(477-484).
IEEE DOI 0906
BibRef

Tappen, M.F.[Marshall F.], Samuel, K.G.G.[Kegan G. G.], Dean, C.V.[Craig V.], Lyle, D.M.[David M.],
The Logistic Random Field: A convenient graphical model for learning parameters for MRF-based labeling,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Liu, C.[Ce], Sharan, L.[Lavanya], Adelson, E.H.[Edward H.], Rosenholtz, R.[Ruth],
Exploring features in a Bayesian framework for material recognition,
CVPR10(239-246).
IEEE DOI 1006
glass, metal, wood, etc. from single image of surface. BibRef

Tappen, M.F.[Marshall F.], Liu, C.[Ce], Adelson, E.H.[Edward H.], Freeman, W.T.[William T.],
Learning Gaussian Conditional Random Fields for Low-Level Vision,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Zheng, H.W.[Hong-Wei], Hellwich, O.[Olaf],
Extended Mumford-Shah Regularization in Bayesian Estimation for Blind Image Deconvolution and Segmentation,
IWCIA06(144-158).
Springer DOI 0606

See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Xu, C.L.[Cun Lu], Chen, Y.Q.[Yan Qiu],
Statistical landscape features for texture classification,
ICPR04(I: 676-679).
IEEE DOI 0409
BibRef

Sullins, J.R.[John R.],
Distributed learning of texture classification,
ECCV90(347-358).
Springer DOI 9004
BibRef

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Dynamic Textures .


Last update:Mar 16, 2024 at 20:36:19