8.7.1.2.2 Variational Models, Snake Models, Active Contours

Chapter Contents (Back)
Deformable Curves. Snakes. Active Contours. Segmentation. Variational Models.

Vitti, A.[Alfonso],
The Mumford-Shah variational model for image segmentation: An overview of the theory, implementation and use,
PandRS(69), No. 1, April 2012, pp. 50-64.
Elsevier DOI 1202
Segmentation; Image processing; Variational model; Mathematics; GIS BibRef

Guichard, F.[Frederic], and Dal Maso, G., Morel, J.M.[Jean-Michel], and Solimini, S.,
A variational method in image segmentation: Existence and approximation results,
ActaMat(168), 1992, pp. 89-151. BibRef 9200

March, R., Dozio, M.,
A Variational Method for the Recovery of Smooth Boundaries,
IVC(15), No. 9, September 1997, pp. 705-712.
WWW Link. 9709
BibRef

Hewer, G.A., Kenney, C.S., Manjunath, B.S.,
Variational Image Segmentation Using Boundary Functions,
IP(7), No. 9, September 1998, pp. 1269-1282.
IEEE DOI
PDF File. 9809
BibRef
Earlier:
Image Segmentation via Functionals Based on Boundary Functions,
ICIP96(I: 813-816).
IEEE DOI
PDF File. BibRef

Hintermüller, M.[Michael], Ring, W.[Wolfgang],
An Inexact Newton-CG-Type Active Contour Approach for the Minimization of the Mumford-Shah Functional,
JMIV(20), No. 1-2, January-March 2004, pp. 19-42.
DOI Link 0403
( See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. ) BibRef

Hintermüller, M., Laurain, A.,
Multiphase Image Segmentation and Modulation Recovery Based on Shape and Topological Sensitivity,
JMIV(35), No. 1, September 2009, pp. xx-yy.
Springer DOI 0907
See also Multi-Scale Vectorial L-tau-TV Framework for Color Image Restoration, A. BibRef

Vanzella, W., Torre, V.,
A Versatile Segmentation Procedure,
SMC-B(36), No. 2, April 2006, pp. 366-378.
IEEE DOI 0604
Regularize with Mumford-Shah ( See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. ) BibRef

Tao, T.C.Y.[Trevor Chi-Yuen], Crisp, D.J.[David James], van der Hoek, J.[John],
Mathematical Analysis of An Extended Mumford-Shah Model for Image Segmentation,
JMIV(24), No. 3, May 2006, pp. 327-340.
Springer DOI 0605
See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Cremers, D.[Daniel], Tischhäuser, F.[Florian], Weickert, J.[Joachim], Schnörr, C.[Christoph],
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional,
IJCV(50), No. 3, December 2002, pp. 295-313.
DOI Link
PDF File. 0211
BibRef

Cremers, D.[Daniel], Kohlberger, T.[Timo], Schnörr, C.[Christoph],
Shape Statistics in Kernel Space for Variational Image Segmentation,
PR(36), No. 9, September 2003, pp. 1929-1943.
WWW Link. 0307
BibRef
Earlier:
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation,
ECCV02(II: 93 ff.).
Springer DOI
PS File. 0205
BibRef
And:
Nonlinear Shape Statistics via Kernel Spaces,
DAGM01(269-276). BibRef
And:
Diffusion-Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework,
LevelSet01(137-144).
PS File. 0106
See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. See also Statistical Shape Knowledge in Variational Motion Segmentation. BibRef

Cremers, D.[Daniel], Sochen, N.A.[Nir A.], Schnörr, C.[Christoph],
A Multiphase Dynamic Labeling Model for Variational Recognition-driven Image Segmentation,
IJCV(66), No. 1, January 2006, pp. 67-81.
Springer DOI 0601
BibRef
Earlier:
Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling,
ScaleSpace03(388-400).
Springer DOI 0310
BibRef

Cremers, D.[Daniel], Schmidt, F.R.[Frank R.], Barthel, F.[Frank],
Shape priors in variational image segmentation: Convexity, Lipschitz continuity and globally optimal solutions,
CVPR08(1-6).
IEEE DOI 0806
BibRef

Schoenemann, T.[Thomas], Cremers, D.[Daniel],
A Combinatorial Solution for Model-Based Image Segmentation and Real-Time Tracking,
PAMI(32), No. 7, July 2010, pp. 1153-1164.
IEEE DOI 1006
BibRef
Earlier:
Matching non-rigidly deformable shapes across images: A globally optimal solution,
CVPR08(1-6).
IEEE DOI 0806
BibRef
And:
Globally optimal shape-based tracking in real-time,
CVPR08(1-6).
IEEE DOI 0806
BibRef
Earlier:
Globally Optimal Image Segmentation with an Elastic Shape Prior,
ICCV07(1-6).
IEEE DOI 0710
BibRef
And:
Introducing Curvature into Globally Optimal Image Segmentation: Minimum Ratio Cycles on Product Graphs,
ICCV07(1-6).
IEEE DOI 0710
Combinatorial solution for optimal elastic match to image. Minimum cost cyclic path in 3D space. BibRef

Schoenemann, T.[Thomas], Schmidt, F.R., Cremers, D.[Daniel],
Image Segmentation with Elastic Shape Priors via Global Geodesics in Product Spaces,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Schoenemann, T.[Thomas], Masnou, S., Cremers, D.[Daniel],
The Elastic Ratio: Introducing Curvature Into Ratio-Based Image Segmentation,
IP(20), No. 9, September 2011, pp. 2565-2581.
IEEE DOI 1109
BibRef

Schmidt, F.R.[Frank R.], Cremers, D.[Daniel],
A Closed-Form Solution for Image Sequence Segmentation with Dynamical Shape Priors,
DAGM09(31-40).
Springer DOI 0909
BibRef

Lellmann, J.[Jan], Schnörr, C.[Christoph],
Continuous Multiclass Labeling Approaches and Algorithms,
SIIMS(4), No. 4 2011, pp. 1049.
DOI Link 1112
BibRef

Lellmann, J.[Jan], Lenzen, F.[Frank], Schnörr, C.[Christoph],
Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem,
JMIV(47), No. 3, November 2013, pp. 239-257.
WWW Link. 1309
BibRef
Earlier: EMMCVPR11(132-146).
Springer DOI 1107
BibRef

Lellmann, J.[Jan], Breitenreicher, D.[Dirk], Schnörr, C.[Christoph],
Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision,
ECCV10(II: 494-505).
Springer DOI 1009
BibRef

Lellmann, J.[Jan], Becker, F.[Florian], Schnörr, C.[Christoph],
Convex optimization for multi-class image labeling with a novel family of total variation based regularizers,
ICCV09(646-653).
IEEE DOI 0909
BibRef

Lellmann, J.[Jan], Kappes, J.H.[Jörg H.], Yuan, J.[Jing], Becker, F.[Florian], Schnörr, C.[Christoph],
Convex Multi-class Image Labeling by Simplex-Constrained Total Variation,
SSVM09(150-162).
Springer DOI 0906
BibRef

Bergtholdt, M.[Martin], Schnörr, C.[Christoph],
Shape Priors and Online Appearance Learning for Variational Segmentation and Object Recognition in Static Scenes,
DAGM05(342).
Springer DOI 0509
BibRef

Jackson, J.D.[Jeremy D.], Yezzi, A.J.[Anthony J.], Soatto, S.[Stefano],
Dynamic Shape and Appearance Modeling via Moving and Deforming Layers,
IJCV(79), No. 1, August 2008, pp. xx-yy.
Springer DOI 0804
BibRef
Earlier: EMMCVPR05(427-438).
Springer DOI 0601
BibRef
And:
Joint Priors for Variational Shape and Appearance Modeling,
MultiView07(1-7).
IEEE DOI 0706
See also Integral Invariants for Shape Matching. BibRef

Bresson, X.[Xavier], Vandergheynst, P.[Pierre], Thiran, J.P.[Jean-Philippe],
A Variational Model for Object Segmentation Using Boundary Information and Shape Prior Driven by the Mumford-Shah Functional,
IJCV(68), No. 2, June 2006, pp. 145-162.
Springer DOI 0606
BibRef
Earlier:
A priori information in image segmentation: energy functional based on shape statistical model and image information,
ICIP03(III: 425-428).
IEEE DOI 0312
See also Geodesic Active Fields: A Geometric Framework for Image Registration. See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Ye, J.[Jian], Wittman, T.[Todd], Bresson, X.[Xavier], Osher, S.J.[Stanley J.],
Segmentation for Hyperspectral Images with Priors,
ISVC10(II: 97-106).
Springer DOI 1011
BibRef

Bresson, X.[Xavier], Vandergheynst, P.[Pierre], Thiran, J.P.[Jean-Philippe],
Multiscale Active Contours,
IJCV(70), No. 3, December 2006, pp. 197-211.
Springer DOI 0608
BibRef
Earlier: ScaleSpace05(167-178).
Springer DOI 0505
BibRef

Bogdanova, I., Bresson, X.[Xavier], Thiran, J.P.[Jean-Philippe], Vandergheynst, P.[Pierre],
Scale Space Analysis and Active Contours for Omnidirectional Images,
IP(16), No. 7, July 2007, pp. 1888-1901.
IEEE DOI 0707
BibRef

Bresson, X.[Xavier], Esedoglu, S.[Selim], Vandergheynst, P.[Pierre], Thiran, J.P.[Jean-Philippe], Osher, S.J.[Stanley J.],
Fast Global Minimization of the Active Contour/Snake Model,
JMIV(28), No. 2, June 2007, pp. 151-167.
Springer DOI 0710
BibRef

Sawatzky, A.[Alex], Tenbrinck, D.[Daniel], Jiang, X.Y.[Xiao-Yi], Burger, M.[Martin],
A Variational Framework for Region-Based Segmentation Incorporating Physical Noise Models,
JMIV(47), No. 3, November 2013, pp. 179-209.
Springer DOI 1309
BibRef

Tenbrinck, D.[Daniel], Jiang, X.Y.[Xiao-Yi],
Image segmentation with arbitrary noise models by solving minimal surface problems,
PR(48), No. 11, 2015, pp. 3293-3309.
Elsevier DOI 1506
BibRef
Earlier:
Discriminant Analysis Based Level Set Segmentation for Ultrasound Imaging,
CAIP13(II:144-151).
Springer DOI 1311
BibRef
And: A2, A1:
Region Based Contour Detection by Dynamic Programming,
CAIP13(II:152-159).
Springer DOI 1311
Segmentation BibRef

Zhang, X.Q.[Xiao-Qun], Burger, M.[Martin], Bresson, X.[Xavier], Osher, S.J.[Stanley J.],
Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction,
SIIMS(3), No. 3, 2010, pp. 253-276.
DOI Link nonlocal regularization; Bregman iteration; primal dual method BibRef 1000

Houhou, N.[Nawal], Thiran, J.P.[Jean-Philippe], Bresson, X.[Xavier],
Fast texture segmentation model based on the shape operator and active contour,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Houhou, N.[Nawal], Lemkaddem, A.[Alia], Duay, V.[Valerie], Allal, A.[Abdelkarim], Thiran, J.P.[Jean-Philippe],
Shape prior based on statistical map for active contour segmentation,
ICIP08(2284-2287).
IEEE DOI 0810
BibRef

Duay, V., Houhou, N., Thiran, J.P.,
Atlas-Based Segmentation of Medical Images Locally Constrained by Level Sets,
ICIP05(II: 1286-1289).
IEEE DOI 0512
BibRef

Bresson, X.[Xavier], Chan, T.F.[Tony F.],
Active Contours Based on Chambolle's Mean Curvature Motion,
ICIP07(I: 33-36).
IEEE DOI 0709
BibRef

Pan, Y.S.[Yong-Sheng], Birdwell, J.D.[J. Douglas], Djouadi, S.M.[Seddik M.],
Preferential Image Segmentation Using Trees of Shapes,
IP(18), No. 4, April 2009, pp. 854-866.
IEEE DOI 0903
BibRef
Earlier:
Bottom-Up Hierarchical Image Segmentation Using Region Competition and the Mumford-Shah Functional,
ICPR06(II: 117-121).
IEEE DOI 0609
BibRef

Grady, L.[Leo], Alvino, C.[Christopher],
The Piecewise Smooth Mumford-Shah Functional on an Arbitrary Graph,
IP(18), No. 11, November 2009, pp. 2547-2561.
IEEE DOI 0911
BibRef
Earlier:
Reformulating and Optimizing the Mumford-Shah Functional on a Graph: A Faster, Lower Energy Solution,
ECCV08(I: 248-261).
Springer DOI 0810
See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Grady, L.[Leo], Singh, V.[Vivek], Kohlberger, T.[Timo], Alvino, C.[Christopher], Bahlmann, C.[Claus],
Automatic Segmentation of Unknown Objects, with Application to Baggage Security,
ECCV12(II: 430-444).
Springer DOI 1210
BibRef

Garamendi, J.F., Gaspar, F.J., Malpica, N., Schiavi, E.,
Box Relaxation Schemes in Staggered Discretizations for the Dual Formulation of Total Variation Minimization,
IP(22), No. 5, May 2013, pp. 2030-2043.
IEEE DOI 1304
See also Automatic Segmentation of the Liver in CT Using Level Sets Without Edges. BibRef

Garamendi, J.F., Malpica, N., Schiavi, E.,
A Fast Anisotropic Mumford-Shah Functional Based Segmentation,
IbPRIA09(322-329).
Springer DOI 0906
See also Automatic Segmentation of the Liver in CT Using Level Sets Without Edges. See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Feng, J., Cao, Z., Pi, Y.,
Multiphase SAR Image Segmentation With G^0-Statistical-Model-Based Active Contours,
GeoRS(51), No. 7, 2013, pp. 4190-4199.
IEEE DOI 1307
Active contours; Image edge detection; total variation (TV) regularization BibRef

Zhu, H., Zheng, J., Cai, J., Thalmann, N.M.,
Object-Level Image Segmentation Using Low Level Cues,
IP(22), No. 10, 2013, pp. 4019-4027.
IEEE DOI 1309
Image segmentation into the small number of real objects. BibRef

Lui, D., Scharfenberger, C., Fergani, K., Wong, A., Clausi, D.A.,
Enhanced Decoupled Active Contour Using Structural and Textural Variation Energy Functionals,
IP(23), No. 2, February 2014, pp. 855-869.
IEEE DOI 1402
edge detection BibRef

Fergani, K., Lui, D., Scharfenberger, C., Wong, A., Clausi, D.A.,
Hybrid structural and texture distinctiveness vector field convolution for region segmentation,
CVIU(125), No. 1, 2014, pp. 85-96.
Elsevier DOI 1406
Texture distinctiveness BibRef

Xu, J.[Jianlou], Feng, X.C.[Xiang-Chu], Hao, Y.[Yan], Han, Y.[Yu],
Image decomposition using adaptive second-order total generalized variation,
SIViP(8), No. 1, January 2014, pp. 39-47.
Springer DOI 1402
Geometric objects and texture or noise. BibRef

Baus, F., Nikolova, M., Steidl, G.,
Fully Smoothed L_1-TV Models: Bounds for the Minimizers and Parameter Choice,
JMIV(48), No. 2, February 2014, pp. 295-307.
WWW Link. 1402
BibRef

Louchet, C., Moisan, L.,
Posterior Expectation of the Total Variation Model: Properties and Experiments,
SIIMS(6), No. 4, 2013, pp. 2640-2684.
DOI Link 1402
BibRef

Hintermüller, M., Langer, A.,
Subspace Correction Methods for a Class of Nonsmooth and Nonadditive Convex Variational Problems with Mixed L1/L2 Data-Fidelity in Image Processing,
SIIMS(6), No. 4, 2013, pp. 2134-2173.
DOI Link 1402
BibRef

Zhang, K., Liu, Q., Song, H., Li, X.,
A Variational Approach to Simultaneous Image Segmentation and Bias Correction,
Cyber(45), No. 8, August 2015, pp. 1426-1437.
IEEE DOI 1506
segmentation of images with intensity inhomogeneity. Gaussian distribution BibRef


Rada, L., Chen, K.[Ke], Ghanbari, B.,
A restarted iterative homotopy analysis method for three-dimensional image segmentation,
IPTA12(178-185)
IEEE DOI 1503
approximation theory BibRef

Chen, K.[Ke],
Selective variational image segmentation combined with registration: Models and algorithms,
IPTA12(5-8)
IEEE DOI 1503
constraint handling BibRef

Last, C.[Carsten], Winkelbach, S.[Simon], Wahl, F.M.[Friedrich M.],
Global-to-local shape priors for variational image segmentation,
ICIP14(6056-6060)
IEEE DOI 1502
Active contours BibRef

Gorelick, L.[Lena], Veksler, O.[Olga], Boykov, Y.Y.[Yuri Y.], Nieuwenhuis, C.[Claudia],
Convexity Shape Prior for Binary Segmentation,
PAMI(39), No. 2, February 2017, pp. 258-271.
IEEE DOI 1702
BibRef
Earlier:
Convexity Shape Prior for Segmentation,
ECCV14(V: 675-690).
Springer DOI 1408
Computational modeling BibRef

Bergbauer, J., Nieuwenhuis, C.[Claudia], Souiai, M., Cremers, D.[Daniel],
Proximity Priors for Variational Semantic Segmentation and Recognition,
GMSU13(15-21)
IEEE DOI 1403
See also Midrange Geometric Interactions for Semantic Segmentation. convex programming BibRef

Pock, T.[Thomas], Cremers, D.[Daniel], Bischof, H.[Horst], Chambolle, A.[Antonin],
An algorithm for minimizing the Mumford-Shah functional,
ICCV09(1133-1140).
IEEE DOI 0909
See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Active Contours and Snakes, Segmentations, Flow, Gradient Flow .


Last update:Jul 15, 2017 at 20:56:55