8.7.1.2 Snakes, Region Segmentation Issues

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

Ronfard, R.,
Region-Based Strategies for Active Contour Models,
IJCV(13), No. 2, October 1994, pp. 229-251.
Springer DOI Reference 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 Reference 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. 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., Yuille, A.,
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 Top Reference.
WWW Version. BibRef 9609
Earlier: Add A2: Lee, T.S., ICCV95(416-423).
IEEE DOI Reference
WWW Version. 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 Reference 0501 BibRef
And: CLOR06(545-576).
Springer DOI Reference 0711 BibRef
Earlier: ICCV03(18-25).
IEEE DOI Reference 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 Reference 0311Model 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 Top Reference.
WWW Version. 9710Use simulated annealing so less dependant on initial contour. BibRef

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 Reference 9809
PDF Version. BibRef
Earlier:
Image Segmentation via Functionals Based on Boundary Functions,
ICIP96(I: 813-816).
IEEE DOI Reference
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

Vilarińo, 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 Reference
HTML Version. 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 Reference 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 Top Reference.
WWW Version. 9912Region 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

Yuille, A.L., Coughlan, J.M.[James M.],
An A* perspective on deterministic optimization for deformable templates,
PR(33), No. 4, April 2000, pp. 603-616.
WWW Version. 0002 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 Top Reference.
WWW Version. 0105 BibRef
And: Corrections: PAMI(23), No. 6, June 2001, pp. 686.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0106 BibRef
Earlier: A2, A1: CVPR99(II: 21-27).
IEEE Abstract. IEEE Top Reference.
WWW Version. 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 Reference 0106 BibRef
And:
Shape-Guided Split and Merge of Image Regions,
VF01(367 ff.).
HTML Version. 0209 BibRef

Tsai, A.[Andy], Yezzi, Jr., A.J.[Anthony J.], Willsky, A.S.[Alan S.],
Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification,
IP(10), No. 8, August 2001, pp. 1169-1186.
IEEE DOI Reference 0108 BibRef

Yezzi, Jr., A.J.[Anthony J.], Tsai, A.[Andy], Willsky, A.S.[Alan S.],
A Fully Global Approach to Image Segmentation via Coupled Curve Evolution Equations,
JVCIR(13), No. 1/2, March/June 2002, pp. 195-216.
WWW Version. 0204 BibRef
Earlier: A2, A1, A3:
A Curve Evolution Approach to Smoothing and Segmentation Using the Mumford-Shah Functional,
CVPR00(I: 119-124).
IEEE Abstract. IEEE Top Reference.
WWW Version. 0005 See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Yezzi, Jr., A.J.[Anthony J.], Tsai, A.[Andy], Willsky, A.S.[Alan S.],
Binary and ternary flows for image segmentation,
ICIP99(II:1-5).
IEEE Abstract. IEEE Top Reference. BibRef 9900
And:
A Statistical Approach to Snakes for Bimodal and Trimodal Imagery,
ICCV99(898-903).
IEEE DOI Reference BibRef

Tsai, A.[Andy], Yezzi, Jr., A.J.[Anthony J.], Wells, III, W.M.[William M.], Tempany, C.[Clare], Tucker, D.[Dewey], Fan, A.[Ayres], Grimson, W.E.L.[W. Eric L.], Willsky, A.S.[Alan S.],
Model-Based Curve Evolution Technique for Image Segmentation,
CVPR01(I:463-468).
IEEE Abstract. IEEE Top Reference. 0110 Award, CVPR, Student HM. For known objects. BibRef

Kim, J.[Junno], Tsai, A., Cetin, M., Willsky, A.S.,
A curve evolution-based variational approach to simultaneous image restoration and segmentation,
ICIP02(I: 109-112).
IEEE Abstract. IEEE Top Reference. 0210 BibRef

Tsai, A.,
Curve Evolution, Boundary-value Stochastic Processes, the Mumford-Shah Problem, and Missing Data Applications,
ICIP00(Vol III: 588-591).
IEEE Abstract. IEEE Top Reference. 0008 See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Unal, G., Krim, H., Yezzi, A.J.,
Stochastic differential equations and geometric flows,
IP(11), No. 12, December 2002, pp. 1405-1416.
IEEE DOI Reference 0301Local stochastic interpretation of the basic curve smoothing equation, the geometric heat equation, this evolution amounts to a tangential diffusion movement of the particles along the contour. BibRef

Unal, G.[Gozde], Yezzi, A.J.[Anthony J.], Krim, H.[Hamid],
Information-Theoretic Active Polygons for Unsupervised Texture Segmentation,
IJCV(62), No. 3, May 2005, pp. 199-220.
Springer DOI Reference 0501 BibRef

Unal, G.[Gozde], Krim, H.[Hamid], Yezzi, A.J.[Anthony J.],
Fast Incorporation of Optical Flow Into Active Polygons,
IP(14), No. 6, June 2005, pp. 745-759.
IEEE DOI Reference 0505 BibRef
Earlier:
Active polygon for object tracking,
3DPVT02(696-699). 0206 BibRef

Unal, G., Krim, H.,
Feature-preserving Flows: A Stochastic Differential Equation's View,
ICIP00(Vol I: 896-899).
IEEE Abstract. IEEE Top Reference. 0008 BibRef

Unal, G., Nain, D., Ben-Arous, G., Shimk-In, N., Tannenbaum, A., Zeitouni, O.,
Algorithms for stochastic approximations of curvature flows,
ICIP03(II: 651-654).
IEEE Abstract. IEEE Top Reference. 0312 BibRef

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

Falcao, A.X.[Alexandre X.], Udupa, J.K.[Jayaram K.], Miyazawa, F.K.,
An ultra-fast user-steered image segmentation paradigm: Live wire on the fly,
MedImg(19), No. 1, January 2000, pp. 55-62.
IEEE Top Reference. 0110 BibRef

Nyúl, L.G.[László G.], Falcăo, A.X.[Alexandre X.], Udupa, J.K.[Jayaram K.],
Fuzzy-connected 3D image segmentation at interactive speeds,
GM(64), No. 5, September 2002, pp. Graphical Models, Volume 64, Issue 5, September 2002, 259-281.
WWW Version. 0309 BibRef

Falcao, A.X.[Alexandre X.], Udupa, J.K.[Jayaram K.], Samarasekera, S.[Supun], Sharma, S.[Shoba], Hirsch, B.E.[Bruce Elliot], de Alencar Lotufo, R.[Roberto],
User-Steered Image Segmentation Paradigms: Live Wire and Live Lane,
GMIP(60), No. 4, July 1998, pp. 233-260. BibRef 9807

Tang, M.[Ming], Ma, S.D.[Song-De],
General Scheme of Region Competition Based on Scale Space,
PAMI(23), No. 12, December 2001, pp. 1366-1378.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0112 BibRef
And: Corrections: PAMI(24), No. 7, July 2002, pp. 1007.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0207 BibRef
Earlier:
A Fast Algorithm of Multiresolution Elastic Matching,
SCIA97(xx-yy) 9705
HTML Version. Region segmentation. Cluster then group clusters. See also Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation. BibRef

Tang, M.[Ming], Xiao, J.[Jing], Ma, S.D.[Song-De],
Semantically Homogeneous Segmentation with Nonparametric Region Competition,
ICPR00(Vol I: 648-651).
IEEE DOI Reference
HTML Version. 0009 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

Jeon, B.K.[Byoung-Ki], Jung, Y.B.[Yun-Beom], Hong, K.S.[Ki-Sang],
Image Segmentation by Unsupervised Sparse Clustering,
PRL(27), No. 14, 15 October 2006, pp. 1650-1664.
WWW Version. 0609 BibRef
Earlier: WACV05(I: 2-7).
WWW Version. 0502Positiveness; Sparse clustering; Binary tree; Model selection; Intra- and inter-cluster measures Cluster into regions based on positiveness. BibRef

Kim, J.S.[Jong-Sung], Hong, K.S.[Ki-Sang],
A new graph cut-based multiple active contour algorithm without initial contours and seed points,
MVA(19), No. 3, May 2008, pp. 181-193.
Springer DOI Reference 0803 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. IEEE Top Reference. 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.
HTML Version. 0202 BibRef
Earlier:
Loop-Free Snakes for Image Segmentation,
ICIP99(III:193-197).
IEEE Abstract. IEEE Top Reference. 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 Reference 0106 BibRef
And:
Region-based Active Contours for Video Object Segmentation with Camera Compensation,
ICIP01(II: 61-64).
IEEE Abstract. IEEE Top Reference. 0108 BibRef
Earlier:
Detection and Tracking of Moving Objects Using a New Level Set Based Method,
ICPR00(Vol III: 1100-1105).
IEEE DOI Reference 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 Reference 0611 BibRef
Earlier: A1, A2, A4, A5, Only:
Shape gradient for multi-modal image segmentation using mutual information,
ICIP04(IV: 2729-2732).
IEEE DOI Reference 0505 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 Reference 0311 BibRef

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

Lecellier, F., Jehan-Besson, S., Fadili, J., Aubert, G., Revenu, M., Saloux, E.,
Region-Based Active Contour with Noise and Shape Priors,
ICIP06(1649-1652). 0610
IEEE DOI Reference 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. IEEE Top Reference. 0407 BibRef
Earlier:
Tracking video objects using active contours,
Motion02(90-95).
IEEE Abstract. IEEE Top Reference. 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 Reference 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 Reference 0710 BibRef
And:
A High Dimensional Framework for Joint Color-Spatial Segmentation,
ICIP07(VI: 313-316).
IEEE DOI Reference 0709 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). 0610
IEEE DOI Reference 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. IEEE Top Reference. 0312 BibRef

Kervrann, C.[Charles], Hoebeke, M.[Mark], Trubuil, A.[Alain],
Isophotes Selection and Reaction-Diffusion Model for Object Boundaries Estimation,
IJCV(50), No. 1, October 2002, pp. 63-94.
WWW Version. 0210 BibRef
Earlier:
A Level Line Selection Approach for Object Boundary Estimation,
ICCV99(963-968).
IEEE DOI Reference BibRef

Mahamud, S.[Shyjan], Williams, L.R.[Lance R.], Thornber, K.K.[Karvel K.], Xu, K.[Kanglin],
Segmentation of multiple salient closed contours from real images,
PAMI(25), No. 4, April 2003, pp. 433-444.
IEEE Abstract. IEEE Top Reference. 0304Based on a global property, identify smooth closed contours. BibRef

Mahamud, S., Thornber, K.K., Williams, L.R.,
Segmentation of Salient Closed Contours from Real Images,
ICCV99(891-897).
IEEE DOI Reference BibRef 9900

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. 0205
Postscript Version. 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], 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 Reference 0806 BibRef

Schoenemann, T.[Thomas], Cremers, D.[Daniel],
Globally optimal shape-based tracking in real-time,
CVPR08(1-6).
IEEE DOI Reference 0806 BibRef

Schoenemann, T.[Thomas], Cremers, D.[Daniel],
Matching non-rigidly deformable shapes across images: A globally optimal solution,
CVPR08(1-6).
IEEE DOI Reference 0806 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], Cremers, D.[Daniel],
Globally Optimal Image Segmentation with an Elastic Shape Prior,
ICCV07(1-6).
IEEE DOI Reference 0710 BibRef

Schoenemann, T.[Thomas], Cremers, D.[Daniel],
Introducing Curvature into Globally Optimal Image Segmentation: Minimum Ratio Cycles on Product Graphs,
ICCV07(1-6).
IEEE DOI Reference 0710 BibRef

Heiler, M.[Matthias], Schnörr, C.[Christoph],
Natural Image Statistics for Natural Image Segmentation,
IJCV(63), No. 1, June 2005, pp. 5-19.
Springer DOI Reference 0501 BibRef
Earlier: ICCV03(1259-1266).
IEEE DOI Reference 0311 Award, Marr Prize. BibRef

Keuchel, J.[Jens], Schnorr, C.[Chrostoph], Schellewald, C.[Christian], Cremers, D.[Daniel],
Binary partitioning, perceptual grouping, and restoration with semidefinite programming,
PAMI(25), No. 11, November 2003, pp. 1364-1379.
IEEE Abstract. IEEE Top Reference. 0311 BibRef
Earlier:
Unsupervised Image Partitioning with Semidefinite Programming,
DAGM02(141 ff.).
HTML Version. 0303Figure-ground. Segment into coherent parts. BibRef

Heiler, M.[Matthias], Keuchel, J.[Jens], Schnörr, C.[Christoph],
Semidefinite Clustering for Image Segmentation with A-priori Knowledge,
DAGM05(309).
Springer DOI Reference 0509 BibRef
Earlier: A2, A1, A3:
Hierarchical Image Segmentation Based on Semidefinite Programming,
DAGM04(120-128).
WWW Version. 0505 BibRef

Keuchel, J.[Jens], Küttel, D.[Daniel],
Efficient Combination of Probabilistic Sampling Approximations for Robust Image Segmentation,
DAGM06(41-50).
Springer DOI Reference 0610 BibRef

Keuchel, J.[Jens],
Multiclass Image Labeling with Semidefinite Programming,
ECCV06(II: 454-467).
Springer DOI Reference 0608 BibRef

Kappes, J.H.[Jörg H.], Schnörr, C.[Christoph],
MAP-Inference for Highly-Connected Graphs with DC-Programming,
DAGM08(xx-yy).
Springer DOI Reference 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 Reference 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 Reference 0509 BibRef

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 Reference 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. IEEE Top Reference. 0307 BibRef

Manay, S.[Siddharth], Cremers, D.[Daniel], Hong, B.W.[Byung-Woo], Yezzi, A.J.[Anthony J.], Soatto, S.[Stefano],
Integral Invariants for Shape Matching,
PAMI(28), No. 10, October 2006, pp. 1602-1618.
IEEE DOI Reference 0609Integral invariants as opposed to differential invariants. BibRef

Manay, S.[Siddharth], Cremers, D.[Daniel], Yezzi, A.J.[Anthony J.], Soatto, S.[Stefano],
One-Shot Integral Invariant Shape Priors for Variational Segmentation,
EMMCVPR05(414-426).
Springer DOI Reference 0601 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 Reference 0804 BibRef
Earlier: EMMCVPR05(427-438).
Springer DOI Reference 0601 BibRef
And:
Joint Priors for Variational Shape and Appearance Modeling,
MultiView07(1-7).
IEEE DOI Reference 0706 BibRef

Cremers, D., Soatto, S.,
Variational space-time motion segmentation,
ICCV03(886-893).
IEEE DOI Reference 0311 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 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 Reference 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],
Implicit Active Model using Radial Basis Function Interpolated Level Sets,
BMVC07(xx-yy).
PDF Version. 0709 BibRef

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

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Han, D.F.[Dong-Feng], Li, W.H.[Wen-Hui], Lu, X.S.[Xiao-Suo], Li, L.[Lin], Wang, Y.[Yi],
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