Keith Price Bibliography Active Contours and Snakes, Region Segmentation Issues

8.7.1.2 Active Contours and Snakes, Region Segmentation Issues

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

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

Sinclair, D.A., Blake, A., Smith, S., and Rothwell, C.A.,
Planar Region Detection and Motion Recovery,
IVC(11), No. 4, May 1993, pp. 229-234.
WWW Version. BibRef 9305
Earlier: BMVC92(xx-yy).
PDF Version. 9209
BibRef

Sinclair, D.A., Blake, A.,
Isoperimetric Normalization of Planar Curves,
PAMI(16), No. 8, August 1994, pp. 769-777.
IEEE Abstract.
IEEE DOI Link BibRef 9408

Sinclair, D.A., Blake, A.,
Quantitative Planar Region Detection,
IJCV(18), No. 1, April 1996, pp. 77-91.
Springer DOI Link 9605
BibRef

Ronfard, R.,
Region-Based Strategies for Active Contour Models,
IJCV(13), No. 2, October 1994, pp. 229-251.
Springer DOI Link Introduces heuristic rules to derive equations for a snake, using local statistics of the regions around the contour. BibRef 9410

Ivins, J.P.[Jim P.], Porrill, J.[John],
Active-Region Models for Segmenting Textures and Colors,
IVC(13), No. 5, June 1995, pp. 431-438.
WWW Version. BibRef 9506
And:
Active-Region Models for Segmenting Medical Images,
ICIP94(II: 227-231).
IEEE DOI Link 9411
BibRef
Earlier:
Statistical Snakes: Active Region Models,
BMVC94(xx-yy).
PDF Version. 9409
BibRef
And: A1, Only: Ph.D.Thesis, Univ. of Sheffield, 1996. See also Semiautomatic Tool for 3-D Medical Image Analysis Using Active Contour Models, A. BibRef

Ivins, J.P.[Jim P.], Porrill, J.[John],
Constrained Active Region Models for Fast Tracking in Color Image Sequences,
CVIU(72), No. 1, October 1998, pp. 54-71.
WWW Version. BibRef 9810

Zhu, S.C.[Song-Chun], Yuille, A.L.[Alan L.],
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation,
PAMI(18), No. 9, September 1996, pp. 884-900.
IEEE Abstract.
IEEE DOI Link BibRef 9609
Earlier: Add A2: Lee, T.S., ICCV95(416-423).
IEEE DOI Link
IEEE DOI Link BibRef
And:
Region Competition and its Analysis: A Unified Theory for Image Segmentation,
HarvardTR-95-07, 1995. Bayes Nets. Region Growing. Minimum Description Length. Color Models. These are the same if viewed in the right way. Problems with different size regions. BibRef

Tu, Z.W.[Zhuo-Wen], Chen, X.R.[Xiang-Rong], Yuille, A.L.[Alan L.], Zhu, S.C.[Song-Chun],
Image Parsing: Unifying Segmentation, Detection, and Recognition,
IJCV(63), No. 2, July 2005, pp. 113-140.
Springer DOI Link 0501
BibRef
And: CLOR06(545-576).
Springer DOI Link 0711
BibRef
Earlier: ICCV03(18-25).
IEEE DOI Link 0311
Award, Marr Prize. BibRef

Dubinskiy, A., Zhu, S.C.[Song Chun],
A multi-scale generative model for animate shapes and parts,
ICCV03(249-256).
IEEE DOI Link 0311
Model shapes and parts. BibRef

Chan, F.H.Y., Lam, F.K., Poon, P.W.F., Zhu, H., Chan, K.H.,
Object Boundary Location by Region and Contour Deformation,
VISP(143), No. 6, December 1996, pp. 353-360. 9702
BibRef

Milroy, M.J., Bradley, C., Vickers, G.W.,
Segmentation of a Wrap-Around Model Using an Active Contour,
CAD(29), No. 4, April 1997, pp. 299-320. 9703
BibRef

Grzeszczuk, R.P.[Robert P.], Levin, D.N.[David N.],
Brownian Strings: Segmenting Images with Stochastically Deformable Contours,
PAMI(19), No. 10, October 1997, pp. 1100-1114.
IEEE Abstract.
IEEE DOI Link 9710
Use simulated annealing so less dependant on initial contour. BibRef

Levin, D.N.[David N.], Grzeszczuk, R.P.[Robert P.],
Method and apparatus for segmenting images using stochastically deformable contours,
US_Patent5,768,413, Jun 16, 1998
WWW Version. arbitrarily shaped contour is deformed stochastically until it approximates the contour of a target object BibRef 9806

Ip, H.H.S., Shen, D.G.,
An Affine-Invariant Active Contour Model (AI-Snake) for Model-Based Segmentation,
IVC(16), No. 2, February 20 1998, pp. 135-146.
WWW Version. 9803
BibRef

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

Vilarino, D.L., Brea, V.M., Cabello, D., Pardo, J.M.,
Discrete-Time CNN for Image Segmentation by Active Contours,
PRL(19), No. 8, June 1998, pp. 721-734. 9808
BibRef

Pardo, X.M., Cabello, D.,
Biomedical active segmentation guided by edge saliency,
PRL(21), No. 6-7, June 2000, pp. 559-572. 0006
BibRef

Vilariño, D.L.[David L.], Cabello, D.[Diego], Pardo, X.M.[Xose M.], Brea, V.M.[Victof M.],
Cellular neural networks and active contours: A tool for image segmentation,
IVC(21), No. 2, February 2003, pp. 189-204.
WWW Version. 0301
BibRef
Earlier:
Pixel-level Snakes,
ICPR00(Vol I: 640-643).
IEEE DOI Link 0009
BibRef

Ngoi, K.P., Jia, J.C.,
An active contour model for colour region extraction in natural scenes,
IVC(17), No. 13, 1 November 1999, pp. 955-966.
WWW Version. 9911
BibRef
Earlier:
A Robust Active Contour Model for Natural Scene Contour Extraction with Automatic Thresholding,
ECCV96(II:335-346).
Springer DOI Link Extract various objects from images. BibRef

Chesnaud, C.[Christophe], Réfrégier, P.[Philippe], Boulet, V.[Vlady],
Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models,
PAMI(21), No. 11, November 1999, pp. 1145-1157.
IEEE Abstract.
IEEE DOI Link 9912
Region snakes, not edge snakes, base on statistics of the region inside and outside the snake. The examples seem to show extraction with very low contrasts. BibRef

Sclaroff, S.[Stan], Liu, L.F.[Li-Feng],
Deformable Shape Detection and Description via Model-Based Region Grouping,
PAMI(23), No. 5, May 2001, pp. 475-489.
IEEE Abstract.
IEEE DOI Link 0105
BibRef
And: Corrections: PAMI(23), No. 6, June 2001, pp. 686.
IEEE Abstract.
IEEE DOI Link 0106
BibRef
Earlier: A2, A1: CVPR99(II: 21-27).
IEEE Abstract.
IEEE DOI Link Partition the image using deformable shape templates. By using learned templates, shapes are segmented from background. BibRef

Liu, L.F.[Li-Feng], Sclaroff, S.[Stan],
Deformable model-guided region split and merge of image regions,
IVC(22), No. 4, 1 April 2004, pp. 343-354.
WWW Version. 0402
BibRef
Earlier:
Region Segmentation via Deformable Model-Guided Split and Merge,
ICCV01(I: 98-104).
IEEE DOI Link 0106
BibRef
And:
Shape-Guided Split and Merge of Image Regions,
VF01(367 ff.).
HTML Version. 0209
BibRef

Germain, O.[Olivier], Réfrégier, P.[Philippe],
Statistical active grid for segmentation refinement,
PRL(22), No. 10, August 2001, pp. 1125-1132.
Elsevier DOI Link 0108
BibRef

Jang, J.H.[Jeong-Hun], Hong, K.S.[Ki-Sang],
Detection of curvilinear structures and reconstruction of their regions in gray-scale images,
PR(35), No. 4, April 2002, pp. 807-824.
WWW Version. 0201
See also Linear band detection based on the Euclidean distance transform and a new line segment extraction method. BibRef

Ji, L.[Lilian], Yan, H.[Hong],
Robust Topology-adaptive Snakes for Image Segmentation,
IVC(20), No. 2, February 2002, pp. 147-164.
WWW Version. 0202
BibRef
Earlier: ICIP01(II: 797-800).
IEEE Abstract. 0108
BibRef

Ji, L.[Lilian], Yan, H.[Hong],
Attractable snakes based on the greedy algorithm for contour extraction,
PR(35), No. 4, April 2002, pp. 791-806.
WWW Version. 0201
BibRef

Ji, L.[Lilian], Yan, H.[Hong],
Loop-free snakes for highly irregular object shapes,
PRL(23), No. 5, March 2002, pp. 579-591.
Elsevier DOI Link 0202
BibRef
Earlier:
Loop-Free Snakes for Image Segmentation,
ICIP99(III:193-197).
IEEE Abstract. BibRef

Jehan-Besson, S.[Stéphanie], Barlaud, M.[Michel], Aubert, G.[Gilles],
A 3-Step Algorithm Using Region-Based Active Contours for Video Objects Detection,
JASP(2002), No. 6, June 2002, pp. 572-581. 0208
BibRef
Earlier:
Video Objects Segmentation Using Eulerian Region-Based Active Contours,
ICCV01(I: 353-360).
IEEE DOI Link 0106
BibRef
And:
Region-based Active Contours for Video Object Segmentation with Camera Compensation,
ICIP01(II: 61-64).
IEEE Abstract. 0108
BibRef
Earlier:
Detection and Tracking of Moving Objects Using a New Level Set Based Method,
ICPR00(Vol III: 1100-1105).
IEEE DOI Link 0009
BibRef

Herbulot, A.[Ariane], Jehan-Besson, S.[Stéphanie], Duffner, S.[Stefan], Barlaud, M.[Michel], Aubert, G.[Gilles],
Segmentation of Vectorial Image Features Using Shape Gradients and Information Measures,
JMIV(25), No. 3, October 2006, pp. 365-386.
Springer DOI Link 0611
BibRef
Earlier: A1, A2, A4, A5, Only:
Shape gradient for multi-modal image segmentation using mutual information,
ICIP04(IV: 2729-2732).
IEEE DOI Link 0505
See also DREAM 2 S: Deformable Regions Driven by an Eulerian Accurate Minimization Method for Image and Video Segmentation. BibRef

Castaud, M., Barlaud, M.,
Video segmentation using active contours on a group of pictures,
ICIP02(II: 81-84).
IEEE Abstract. 0210
BibRef

Lecellier, F.[François], Fadili, J.[Jalal], Jehan-Besson, S.[Stéphanie], Aubert, G.[Gilles], Revenu, M.[Marinette], Saloux, E.[Eric],
Region-Based Active Contours with Exponential Family Observations,
JMIV(36), No. 1, January 2010, pp. xx-yy.
Springer DOI Link 1001
BibRef
Earlier: A1, A3, A2, A4, A5, A6:
Region-Based Active Contour with Noise and Shape Priors,
ICIP06(1649-1652).
IEEE DOI Link 0610
BibRef

Lecellier, F.[Francois], Jehan-Besson, S.[Stephanie], Fadili, J.[Jalal], Aubert, G.[Gilles], Revenu, M.[Marinette],
Optimization of Divergences within the Exponential Family for Image Segmentation,
SSVM09(137-149).
Springer DOI Link 0906
BibRef
Earlier: A1, A3, A2, A5, A4:
Region-based active contours and sparse representations for texture segmentation,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef

Jehan-Besson, S.[Stephanie], Barlaud, M.[Michel], Aubert, G.[Gilles], Faugeras, O.D.,
Shape gradients for histogram segmentation using active contours,
ICCV03(408-415).
IEEE DOI Link 0311
BibRef

Gastaud, M., Barlaud, M., Aubert, G.,
Combining shape prior and statistical features for active contour segmentation,
CirSysVideo(14), No. 5, May 2004, pp. 726-734.
IEEE Abstract. 0407
BibRef
Earlier:
Tracking video objects using active contours,
Motion02(90-95).
IEEE Abstract. 0303
BibRef

Debreuve, É., Gastaud, M., Barlaud, M., Aubert, G.,
Using the Shape Gradient for Active Contour Segmentation: From the Continuous to the Discrete Formulation,
JMIV(28), No. 1, May 2007, pp. 47-66.
Springer DOI Link 0710
See also Outer-Layer Based Tracking using Entropy as a Similarity Measure. See also DREAM 2 S: Deformable Regions Driven by an Eulerian Accurate Minimization Method for Image and Video Segmentation. BibRef

Boltz, S.[Sylvain], Debreuve, É.[Éric], Barlaud, M.[Michel],
Joint Appearance and Deformable Shape for Nonparametric Segmentation,
HUMO07(180-195).
Springer DOI Link 0710
BibRef
And:
A High Dimensional Framework for Joint Color-Spatial Segmentation,
ICIP07(VI: 313-316).
IEEE DOI Link 0709
See also Motion and Appearance Nonparametric Joint Entropy for Video Segmentation. See also High-Dimensional Statistical Measure for Region-of-Interest Tracking. BibRef

Debreuve, E., Barlaud, M., Marmorat, J.P., Aubert, G.,
Active Contour Segmentation with a Parametric Shape Prior: Link with the Shape Gradient,
ICIP06(1653-1656).
IEEE DOI Link 0610
BibRef

Jehan-Besson, S., Gastaud, M., Barlaud, M., Aubert, G.,
Region-based active contours using geometrical and statistical features for image segmentation,
ICIP03(II: 643-646).
IEEE Abstract. 0312
BibRef

Mahamud, S.[Shyjan], Williams, L.R.[Lance R.], Thornber, K.K.[Karvel K.], Xu, K.L.[Kang-Lin],
Segmentation of multiple salient closed contours from real images,
PAMI(25), No. 4, April 2003, pp. 433-444.
IEEE Abstract. 0304
BibRef
Earlier: A1, A3, A2, Only:
Segmentation of Salient Closed Contours from Real Images,
ICCV99(891-897).
IEEE DOI Link Based on a global property, identify smooth closed contours. 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.
WWW Version.
PDF Version. 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 Version. 0307
BibRef
Earlier:
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation,
ECCV02(II: 93 ff.).
HTML Version.
Postscript Version. 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).
Postscript Version. 0106
See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Cremers, D.[Daniel],
Statistical Shape Knowledge in Variational Image Segmentation,
Ph.D.Thesis, Department of Mathematics and Computer Science, University of Mannheim, Germany, July 2002.
PDF Version. BibRef 0207

Cremers, D.[Daniel], Schnörr, C.[Christoph],
Statistical Shape Knowledge in Variational Motion Segmentation,
IVC(21), No. 1, January 2003, pp. 77-86.
WWW Version.
PDF Version. 0301
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 Link 0601
BibRef
Earlier:
Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling,
ScaleSpace03(388-400).
HTML Version. 0310
BibRef

Cremers, D.,
A variational framework for image segmentation combining motion estimation and shape regularization,
CVPR03(I: 53-58).
IEEE Abstract. 0307
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 Link 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 Link 1006
BibRef
Earlier:
Matching non-rigidly deformable shapes across images: A globally optimal solution,
CVPR08(1-6).
IEEE DOI Link 0806
BibRef
And:
Globally optimal shape-based tracking in real-time,
CVPR08(1-6).
IEEE DOI Link 0806
BibRef
Earlier:
Globally Optimal Image Segmentation with an Elastic Shape Prior,
ICCV07(1-6).
IEEE DOI Link 0710
BibRef
And:
Introducing Curvature into Globally Optimal Image Segmentation: Minimum Ratio Cycles on Product Graphs,
ICCV07(1-6).
IEEE DOI Link 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 Version. 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 Link 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 Link 0909
BibRef

Bergtholdt, M.[Martin], Kappes, J.H.[Jörg H.], Schmidt, S.[Stefan], Schnörr, C.[Christoph],
A Study of Parts-Based Object Class Detection Using Complete Graphs,
IJCV(87), No. 1-2, March 2010, pp. xx-yy.
Springer DOI Link 1001
Part based. BibRef

Andres, B.[Bjoern], Kappes, J.H.[Jorg H.], Beier, T.[Thorsten], Kothe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
Probabilistic image segmentation with closedness constraints,
ICCV11(2611-2618).
IEEE DOI Link 1201
BibRef

Kappes, J.H.[Jörg Hendrik], Speth, M.[Markus], Andres, B.[Björn], Reinelt, G.[Gerhard], Schnörr, C.[Christoph],
Globally Optimal Image Partitioning by Multicuts,
EMMCVPR11(31-44).
Springer DOI Link 1107
BibRef

Savchynskyy, B.[Bogdan], Kappes, J.H.[Jorg Hendrik], Schmidt, S.[Stefan], Schnorr, C.[Christoph],
A study of Nesterov's scheme for Lagrangian decomposition and MAP labeling,
CVPR11(1817-1823).
IEEE DOI Link 1106
BibRef

Schmidt, S.[Stefan], Savchynskyy, B.[Bogdan], Kappes, J.H.[Jörg Hendrik], Schnörr, C.[Christoph],
Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization,
EMMCVPR11(89-103).
Springer DOI Link 1107
BibRef

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

Lellmann, J.[Jan], Lenzen, F.[Frank], Schnörr, C.[Christoph],
Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem,
EMMCVPR11(132-146).
Springer DOI Link 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 Link 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 Link 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 Link 0906
BibRef

Kappes, J.H.[Jörg Hendrik], Schmidt, S.[Stefan], Schnörr, C.[Christoph],
MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation,
ECCV10(III: 735-747).
Springer DOI Link 1009
BibRef

Kappes, J.H.[Jörg H.], Schnörr, C.[Christoph],
MAP-Inference for Highly-Connected Graphs with DC-Programming,
DAGM08(xx-yy).
Springer DOI Link 0806
BibRef

Bergtholdt, M.[Martin], Kappes, J.H.[Jörg H.], Schnörr, C.[Christoph],
Learning of Graphical Models and Efficient Inference for Object Class Recognition,
DAGM06(273-283).
Springer DOI Link 0610
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 Link 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 Link 0804
BibRef
Earlier: EMMCVPR05(427-438).
Springer DOI Link 0601
BibRef
And:
Joint Priors for Variational Shape and Appearance Modeling,
MultiView07(1-7).
IEEE DOI Link 0706
See also Integral Invariants for Shape Matching. 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.
WWW Version. 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 Link 0907
See also Multi-Scale Vectorial L-tau-TV Framework for Color Image Restoration, A. BibRef

Xie, X.H.[Xiang-Hua], Mirmehdi, M.[Majid],
RAGS: Region-Aided Geometric Snake,
IP(13), No. 5, May 2004, pp. 640-652.
IEEE DOI Link 0404
BibRef
Earlier:
Geodesic Colour Active Contour Resistent to Weak Edges and Noise,
BMVC03(xx-yy).
HTML Version. 0409
BibRef

Xie, X.H.[Xiang-Hua], Mirmehdi, M.[Majid],
Initialisation-Free Active Contour Segmentation,
ICPR10(2318-2321).
IEEE DOI Link 1008
BibRef

Xie, X.H.,
Active Contouring Based on Gradient Vector Interaction and Constrained Level Set Diffusion,
IP(19), No. 1, January 2010, pp. 154-164.
IEEE DOI Link 1001
BibRef

Xie, X.H.[Xiang-Hua], Mirmehdi, M.[Majid],
MAC: Magnetostatic Active Contour Model,
PAMI(30), No. 4, April 2008, pp. 632-646.
IEEE DOI Link 0803
BibRef
And: Correction: PAMI(30), No. 5, May 2008, pp. xx.
IEEE DOI Link 0803
BibRef
Earlier:
Magnetostatic Field for the Active Contour Model: A Study in Convergence,
BMVC06(I:127).
PDF Version. 0609
BibRef

Yang, R.H.[Rong-Hua], Mirmehdi, M.[Majid], Xie, X.H.[Xiang-Hua],
A Charged Active Contour Based on Electrostatics,
ACIVS06(173-184).
Springer DOI Link 0609
BibRef

Yang, R.H.[Rong-Hua], Mirmehdi, M.[Majid],
A Charged Geometric Model for Active Contours,
ICPR06(II: 183-186).
IEEE DOI Link 0609
BibRef

Hanek, R.[Robert], Beetz, M.[Michael],
The Contracting Curve Density Algorithm: Fitting Parametric Curve Models to Images Using Local Self-Adapting Separation Criteria,
IJCV(59), No. 3, September-October 2004, pp. 233-258.
WWW Version. 0405
Locally adaptive criteria in fitting. BibRef

Hanek, R.[Robert],
The Contracting Curve Density Algorithm and its Application to Model-based Image Segmentation,
CVPR01(I:797-804).
IEEE Abstract. 0110
BibRef

Agnus, V.[Vincent],
Segmentation d'images par cooperation contours-regions,
M.S.Thesis, 1997, Strasbourg, France. Study an approach based on combining edge and regions techniques.
Postscript Version. BibRef 9700

Kim, J.[Junmo], Fisher, III, J.W., Yezzi, A.J., Cetin, M., Willsky, A.S.,
A nonparametric statistical method for image segmentation using information theory and curve evolution,
IP(14), No. 10, October 2005, pp. 1486-1502.
IEEE DOI Link 0510
BibRef
Earlier:
Nonparametric methods for image segmentation using information theory and curve evolution,
ICIP02(III: 797-800).
IEEE Abstract. 0210
BibRef
And: A1, A2, A4, A3, A5:
Incorporating complex statistical information in active contour-based image segmentation,
ICIP03(II: 655-658).
IEEE Abstract. 0312
BibRef

Kim, J.[Junmo], Çetin, M.[Müjdat], Willsky, A.S.[Alan S.],
Nonparametric shape priors for active contour-based image segmentation,
SP(87), No. 12, December, 2007, pp. 3021-3044.
Elsevier DOI Link 0905
BibRef

Uzunbas, M.G., Soldea, O., Unay, D., Cetin, M., Unal, G., Erçil, A., Ekin, A.,
Coupled Nonparametric Shape and Moment-Based Intershape Pose Priors for Multiple Basal Ganglia Structure Segmentation,
MedImg(29), No. 12, December 2010, pp. 1959-1978.
IEEE DOI Link 1101
BibRef

Vanzella, W., Torre, V.,
A Versatile Segmentation Procedure,
SMC-B(36), No. 2, April 2006, pp. 366-378.
IEEE DOI Link 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.
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Bresson, X.[Xavier], Vandergheynst, P.[Pierre], Thiran, J.P.[Jean-Philippe],
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