8.7.1.2 Active Contours and Snakes, Region Segmentation Issues

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

Semantic Boundaries Dataset and Benchmark,
Online2011. Dataset, Segmentation.
HTML Version. or:
HTML Version. See also Semantic contours from inverse detectors. Related to: See also Berkeley Segmentation Dataset and Benchmark, The. See also PASCAL Visual Object Classes Challenge 2012, The. BibRef 1100

Sinclair, D.A., Blake, A.,
Isoperimetric Normalization of Planar Curves,
PAMI(16), No. 8, August 1994, pp. 769-777.
IEEE DOI See also Planar Region Detection and Motion Recovery. BibRef 9408

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

Ronfard, R.,
Region-Based Strategies for Active Contour Models,
IJCV(13), No. 2, October 1994, pp. 229-251.
Springer DOI 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 Link. BibRef 9506
And:
Active-Region Models for Segmenting Medical Images,
ICIP94(II: 227-231).
IEEE DOI 9411
BibRef
Earlier:
Statistical Snakes: Active Region Models,
BMVC94(xx-yy).
PDF File. 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.
DOI Link 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 DOI BibRef 9609
Earlier: Add A2: Lee, T.S., ICCV95(416-423).
IEEE DOI 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 0501
BibRef
And: CLOR06(545-576).
Springer DOI 0711
BibRef
Earlier: ICCV03(18-25).
IEEE DOI 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 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 DOI 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 Link. arbitrarily shaped contour is deformed stochastically until it approximates the contour of a target object BibRef 9806

Ip, H.H.S.[Horace H.S.], Shen, D.G.[Ding-Gang],
An Affine-Invariant Active Contour Model (AI-Snake) for Model-Based Segmentation,
IVC(16), No. 2, February 20 1998, pp. 135-146.
WWW Link. 9803
See also Hopfield Neural-Network for Adaptive Image Segmentation: An Active Surface Paradigm, A. 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 Link. 0301
BibRef
Earlier:
Pixel-level Snakes,
ICPR00(Vol I: 640-643).
IEEE DOI 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 Link. 9911
BibRef
Earlier:
A Robust Active Contour Model for Natural Scene Contour Extraction with Automatic Thresholding,
ECCV96(II:335-346).
Springer DOI Extract various objects from images. 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 DOI 0105
BibRef
And: Corrections: PAMI(23), No. 6, June 2001, pp. 686.
IEEE DOI 0106
BibRef
Earlier: A2, A1: CVPR99(II: 21-27).
IEEE DOI 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 Link. 0402
BibRef
Earlier:
Region Segmentation via Deformable Model-Guided Split and Merge,
ICCV01(I: 98-104).
IEEE DOI 0106
BibRef
And:
Shape-Guided Split and Merge of Image Regions,
VF01(367 ff.).
Springer DOI 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 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 Link. 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 Link. 0202
BibRef
Earlier: ICIP01(II: 797-800).
IEEE DOI 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 Link. 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 0202
BibRef
Earlier:
Loop-Free Snakes for Image Segmentation,
ICIP99(III:193-197).
IEEE DOI 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 Based on a global property, identify smooth closed contours. 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 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 1201
BibRef

Kappes, J.H.[Jörg Hendrik], Swoboda, P.[Paul], Savchynskyy, B.[Bogdan], Hazan, T.[Tamir], Schnörr, C.[Christoph],
Multicuts and Perturb and MAP for Probabilistic Graph Clustering,
JMIV(56), No. 2, October 2016, pp. 221-237.
Springer DOI 1609
BibRef
Earlier:
Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts,
SSVM15(231-242).
Springer DOI 1506
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 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 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 1107
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 1009
BibRef

Kappes, J.H.[Jörg H.], Schnörr, C.[Christoph],
MAP-Inference for Highly-Connected Graphs with DC-Programming,
DAGM08(xx-yy).
Springer DOI 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 0610
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 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 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 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 0803
BibRef
And: Correction: PAMI(30), No. 5, May 2008, pp. xx.
IEEE DOI 0803
BibRef
Earlier:
Magnetostatic Field for the Active Contour Model: A Study in Convergence,
BMVC06(I:127).
PDF File. 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 0609
BibRef

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

Zhang, H.Z.[Huai-Zhong], Xie, X.H.[Xiang-Hua],
Divergence of Gradient Convolution: Deformable Segmentation With Arbitrary Initializations,
IP(24), No. 11, November 2015, pp. 3902-3914.
IEEE DOI 1509
convex programming 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.
DOI Link 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 DOI 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.
PS File. 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 0510
BibRef
Earlier:
Nonparametric methods for image segmentation using information theory and curve evolution,
ICIP02(III: 797-800).
IEEE DOI 0210
BibRef
And: A1, A2, A4, A3, A5:
Incorporating complex statistical information in active contour-based image segmentation,
ICIP03(II: 655-658).
IEEE DOI 0312
BibRef

Fisher, III, J.W.[John W.], Principe, J.C.[Jose C.],
A Nonparametric Methodology for Information Theoretic Feature Extraction,
DARPA97(1077-1084). BibRef 9700

Chang, J.[Jason], Cabezas, R.[Randi], Fisher, III, J.W.[John W.],
Bayesian Nonparametric Intrinsic Image Decomposition,
ECCV14(IV: 704-719).
Springer DOI 1408
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 0905
BibRef

Soganli, A.[Abdurrahim], Uzunbas, M.G.[Mustafa Gökhan], Çetin, M.[Müjdat],
Combining learning-based intensity distributions with nonparametric shape priors for image segmentation,
SIViP(8), No. 4, May 2014, pp. 789-798.
Springer DOI 1404
BibRef

Erdil, E., Ghani, M.U., Rada, L., Argunsah, A.O., Unay, D., Tasdizen, T., Çetin, M.[Müjdat],
Nonparametric Joint Shape and Feature Priors for Image Segmentation,
IP(26), No. 11, November 2017, pp. 5312-5323.
IEEE DOI 1709
image segmentation, maximum likelihood estimation, nonparametric statistics, optimisation, probability, Parzen density estimator, class-dependent discriminative feature priors, image segmentation, maximum a posteriori estimation framework, multimodal shape density, nonparametric joint shape and feature priors, posterior density, probability density, shape-dependent discriminative feature priors, Estimation, Image segmentation, Level set, Principal component analysis, Shape, Training, BibRef

Erdil, E., Yildirim, S., Cetin, M., Tasdizen, T.,
MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors,
CVPR16(411-419)
IEEE DOI 1612
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 1101
BibRef

Feddern, C.[Christian], Weickert, J.[Joachim], Burgeth, B.[Bernhard], Welk, M.[Martin],
Curvature-Driven PDE Methods for Matrix-Valued Images,
IJCV(69), No. 1, August 2006, pp. 93-107.
Springer DOI 0606
BibRef
Earlier: A3, A4, A1, A2:
Morphological Operations on Matrix-Valued Images,
ECCV04(Vol IV: 155-167).
Springer DOI 0405
Applied to diffusion tensor magnetic resonance imaging. Detecting edges in tensor fields. Tensorial active contours. See also Tensor-driven Active Contour Model for Moving Object Segmentation, A. BibRef

Burgeth, B.[Bernhard], Bruhn, A.[Andres], Didas, S.[Stephan], Weickert, J.[Joachim], Welk, M.[Martin],
Morphology for matrix data: Ordering versus PDE-based approach,
IVC(25), No. 4, April 2007, pp. 496-511.
WWW Link. 0702
Mathematical morphology; Dilation; Erosion; Matrix-valued images; Diffusion tensor MRI; Loewner ordering; Nonlinear partial differential equation BibRef

Zagorchev, L.[Lyubomir], Goshtasby, A.[Ardeshir], Satter, M.[Martin],
R-snakes,
IVC(25), No. 6, 1 June 2007, pp. 945-959.
WWW Link. 0704
Image segmentation; Energy minimizing contours; R-snakes BibRef

Li, B.[Bing], Acton, S.T.[Scott T.],
Active Contour External Force Using Vector Field Convolution for Image Segmentation,
IP(16), No. 8, August 2007, pp. 2096-2106.
IEEE DOI 0709
BibRef
Earlier:
Vector Field Convolution for Image Segmentation using Snakes,
ICIP06(1637-1640).
IEEE DOI 0610
BibRef
Earlier:
Feature Weighted Active Contours for Image Segmentation,
Southwest06(188-192).
IEEE DOI 0603
BibRef

Widynski, N.[Nicolas], Mignotte, M.[Max],
A Multi-Scale Particle Filter Framework for Contour Detection,
PAMI(36), No. 10, October 2014, pp. 1922-1935.
IEEE DOI 1410
BibRef
Earlier:
A Particle Filter Framework for Contour Detection,
ECCV12(I: 780-793).
Springer DOI 1210
Bayes methods BibRef

Destrempes, F.[Francois], Mignotte, M.[Max], Angers, J.F.[Jean-Francois],
Localization of Shapes Using Statistical Models and Stochastic Optimization,
PAMI(29), No. 9, September 2007, pp. 1603-1615.
IEEE DOI 0709
Color segmentation based on deformations. BibRef

Ferrari, V.[Vittorio], Fevrier, L., Jurie, F.[Frederic], Schmid, C.[Cordelia],
Groups of Adjacent Contour Segments for Object Detection,
PAMI(30), No. 1, January 2008, pp. 36-51.
IEEE DOI 0711
Scale invariant features from chains of straight line segments. BibRef

Ferrari, V.[Vittorio], Jurie, F.[Frederic], Schmid, C.[Cordelia],
From Images to Shape Models for Object Detection,
IJCV(87), No. 3, May 2010, pp. xx-yy.
Springer DOI 1003
BibRef
Earlier:
Accurate Object Detection with Deformable Shape Models Learnt from Images,
CVPR07(1-8).
IEEE DOI 0706
Object detector: learn models from the image. Shape Matching: find the boundaries. Training only needs bounding boxes, not boundaries. BibRef

Kalogeiton, V.[Vicky], Ferrari, V.[Vittorio], Schmid, C.[Cordelia],
Analysing Domain Shift Factors between Videos and Images for Object Detection,
PAMI(38), No. 11, November 2016, pp. 2327-2334.
IEEE DOI 1610
Issues of using still and video for model creation. Computer vision BibRef

Bergo, F.P.G.[Felipe P.G.], Falcăo, A.X.[Alexandre X.], de Miranda, P.A.V.[Paulo A.V.], Rocha, L.M.[Leonardo M.],
Automatic Image Segmentation by Tree Pruning,
JMIV(29), No. 2-3, November 2007, pp. 141-162.
Springer DOI 0712
BibRef

de Miranda, P.A.V.[Paulo A.V.], Falcăo, A.X.[Alexandre X.],
Links Between Image Segmentation Based on Optimum-Path Forest and Minimum Cut in Graph,
JMIV(35), No. 2, October 2009, pp. xx-yy.
Springer DOI 0907
See also Synergistic arc-weight estimation for interactive image segmentation using graphs. BibRef

Ciesielski, K.C.[Krzysztof Chris], Udupa, J.K.[Jayaram K.], Falcăo, A.X.[Alexandre X.], de Miranda, P.A.V.[Paulo A.V.],
Fuzzy Connectedness Image Segmentation in Graph Cut Formulation: A Linear-Time Algorithm and a Comparative Analysis,
JMIV(44), No. 3, November 2012, pp. 375-398.
WWW Link. 1209
BibRef
And: A1, A4, A2, A3:
Image segmentation by combining the strengths of Relative Fuzzy Connectedness and Graph Cut,
ICIP12(2005-2008).
IEEE DOI 1302
BibRef

Falcăo, A.X.[Alexandre X.], de Miranda, P.A.V.[Paulo A.V.], Rocha, A.[Anderson],
A Linear-Time Approach for Image Segmentation Using Graph-Cut Measures,
ACIVS06(138-149).
Springer DOI 0609
BibRef

de Miranda, P.A.V.[Paulo A.V.], Falcăo, A.X.[Alexandre X.], Spina, T.V.,
Riverbed: A Novel User-Steered Image Segmentation Method Based on Optimum Boundary Tracking,
IP(21), No. 6, June 2012, pp. 3042-3052.
IEEE DOI 1202
BibRef

Condori, M.A.T.[Marcos A.T.], Mansilla, L.A.C.[Lucy A. C.], de Miranda, P.A.V.[Paulo A.V.],
Bandeirantes: A Graph-Based Approach for Curve Tracing and Boundary Tracking,
ISMM17(95-106).
Springer DOI 1706
BibRef

de Miranda, P.A.V.[Paulo A. V.], Mansilla, L.A.C.[Lucy A. C.],
Oriented Image Foresting Transform Segmentation by Seed Competition,
IP(23), No. 1, January 2014, pp. 389-398.
IEEE DOI 1402
BibRef
Earlier: A2, A1:
Image Segmentation by Oriented Image Foresting Transform with Geodesic Star Convexity,
CAIP13(572-579).
Springer DOI 1308
graph theory BibRef

de Moraes Braz, C.[Caio], Miranda, P.A.V.[Paulo A.V.],
Image segmentation by image foresting transform with geodesic band constraints,
ICIP14(4333-4337)
IEEE DOI 1502
Computer vision BibRef

Bejar, H.H.C.[Hans H.C.], Cappabianco, F.A.M.[Fábio A.M.], Miranda, P.A.V.[Paulo A.V.],
Efficient Image Segmentation in Graphs with Localized Curvilinear Features,
CIAP17(I:718-728).
Springer DOI 1711
BibRef

Mansilla, L.A.C., Miranda, P.A.V.[Paulo A.V.], Cappabianco, F.A.M.[Fábio A.M.],
Oriented image foresting transform segmentation with connectivity constraints,
ICIP16(2554-2558)
IEEE DOI 1610
Approximation algorithms BibRef

Mansilla, L.A.C.[Lucy A.C.], Jackowski, M.P.[Marcel P.], Miranda, P.A.V.[Paulo A.V.],
Image foresting transform with geodesic star convexity for interactive image segmentation,
ICIP13(4054-4058)
IEEE DOI 1402
fuzzy connectedness BibRef

Le Guyader, C.[Carole], Vese, L.A.[Luminita A.],
Self-Repelling Snakes for Topology-Preserving Segmentation Models,
IP(17), No. 5, May 2008, pp. 767-779.
IEEE DOI 0804
BibRef

Le Guyader, C.[Carole], Apprato, D., Gout, C.,
On the Construction of Topology-Preserving Deformation Fields,
IP(21), No. 4, April 2012, pp. 1587-1599.
IEEE DOI 1204
BibRef

Le Guyader, C.[Carole], Vese, L.A.[Luminita A.],
A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother,
CVIU(115), No. 12, December 2011, pp. 1689-1709.
Elsevier DOI 1111
BibRef
Earlier: SSVM09(600-611).
Springer DOI 0906
Image segmentation; Image registration; Nonlinear elasticity; Ogden materials; Saint Venant–Kirchhoff materials; Calculus of variations; Augmented Lagrangian BibRef

Guillot, L.[Laurence], Le Guyader, C.[Carole],
Extrapolation of Vector Fields Using the Infinity Laplacian and with Applications to Image Segmentation,
SSVM09(87-99).
Springer DOI 0906
BibRef

Ozeré, S.[Solčne], Gout, C.[Christian], Le Guyader, C.[Carole],
Joint Segmentation/Registration Model by Shape Alignment via Weighted Total Variation Minimization and Nonlinear Elasticity,
SIIMS(8), No. 3, 2015, pp. 1981-2020.
DOI Link 1511
BibRef
Earlier: A1, A3, Only:
Nonlocal Joint Segmentation Registration Model,
SSVM15(348-359).
Springer DOI 1506
BibRef

Debroux, N.[Noémie], Ozeré, S.[Solčne], Le Guyader, C.[Carole],
A Non-local Topology-Preserving Segmentation-Guided Registration Model,
JMIV(59), No. 3, November 2017, pp. 432-455.
WWW Link. 1710
BibRef

Debroux, N.[Noémie], Le Guyader, C.[Carole],
A Unified Hyperelastic Joint Segmentation/Registration Model Based on Weighted Total Variation and Nonlocal Shape Descriptors,
SSVM17(614-625).
Springer DOI 1706
BibRef

Kokkinos, I.[Iasonas], Evangelopoulos, G.[Georgios], Maragos, P.[Petros],
Texture Analysis and Segmentation Using Modulation Features, Generative Models, and Weighted Curve Evolution,
PAMI(31), No. 1, January 2009, pp. 142-157.
IEEE DOI 0812
BibRef
Earlier:
Advances in texture analysis- energy dominant component & multiple hypothesis testing,
ICIP04(III: 1509-1512).
IEEE DOI
PDF File. 0505
BibRef
And:
Modulation-feature based textured image segmentation using curve evolution,
ICIP04(II: 1201-1204).
IEEE DOI
PDF File. 0505
AM-FM texture models and Dominant Component Analysis (DCA) paradigm for feature extraction. Weighted curve evolution. BibRef

Kokkinos, I.[Iasonas], Maragos, P.[Petros],
Synergy between Object Recognition and Image Segmentation Using the Expectation-Maximization Algorithm,
PAMI(31), No. 8, August 2009, pp. 1486-1501.
IEEE DOI 0906
BibRef
Earlier:
An Expectation Maximization Approach to the Synergy between Image Segmentation and Object Categorization,
ICCV05(I: 617-624).
IEEE DOI 0510
BibRef

Kokkinos, I.[Iasonas],
Boundary Detection Using F-Measure-, Filter- and Feature- (F3) Boost,
ECCV10(II: 650-663).
Springer DOI 1009
BibRef
And:
Highly accurate boundary detection and grouping,
CVPR10(2520-2527).
IEEE DOI 1006
BibRef

Evangelopoulos, G.[Georgios], Maragos, P.[Petros],
Image decomposition into structure and texture subcomponents with multifrequency modulation constraints,
CVPR08(1-8).
IEEE DOI 0806
BibRef
And:
Texture modulation-constrained image decomposition,
ICIP08(793-796).
IEEE DOI 0810
BibRef

Wang, X.F.[Xiao-Feng], Huang, D.S.[De-Shuang], Xu, H.[Huan],
An efficient local Chan-Vese model for image segmentation,
PR(43), No. 3, March 2010, pp. 603-618.
Elsevier DOI 1001
Extended structure tensor; Image segmentation; Intensity inhomogeneity; Level set method; Local Chan-Vese model See also Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model, A. BibRef

Lampert, T.A.[Thomas A.], O'Keefe, S.E.M.[Simon E.M.],
An active contour algorithm for spectrogram track detection,
PRL(31), No. 10, 15 July 2010, pp. 1201-1206.
Elsevier DOI 1008
BibRef
Earlier:
Active contour detection of linear patterns in spectrogram images,
ICPR08(1-4).
IEEE DOI 0812
Active contour; Periodic time series; Remote sensing; Low signal-to-noise ratio; Spectrogram; Statistical pattern recognition See also detailed investigation into low-level feature detection in spectrogram images, A. BibRef

Lampert, T.A.[Thomas A.], O'Keefe, S.E.M.[Simon E.M.],
On the detection of tracks in spectrogram images,
PR(46), No. 5, May 2013, pp. 1396-1408.
Elsevier DOI 1302
Active contour; Energy minimisation; Remote sensing; Spectrogram; Statistical pattern recognition; Structural pattern recognition BibRef

Zheng, S.F.[Song-Feng], Yuille, A.L.[Alan L.], Tu, Z.W.[Zhuo-Wen],
Detecting Object Boundaries Using Low-, Mid-, and High-level Information,
CVIU(114), No. 10, October 2010, pp. 1055-1067.
Elsevier DOI 1003
BibRef
Earlier: A1, A3, A2: CVPR07(1-8).
IEEE DOI 0706
Boundary detection; Low-level information; High-level information; Shape matching; Cue integration BibRef

Martin, D.R.[David R.], Fowlkes, C.C.[Charless C.], Malik, J.[Jitendra],
Learning to detect natural image boundaries using local brightness, color, and texture cues,
PAMI(26), No. 5, May 2004, pp. 530-549.
IEEE Abstract. 0404
BibRef
Earlier: A2, A1, A3:
Learning Affinity Functions for Image Segmentation: Combining Patch-Based and Gradient-Based Approaches,
CVPR03(II: 54-61).
IEEE DOI 0307
BibRef
Earlier: A2, A1, A3:
Understanding Gestalt Cues and Ecological Statistics Using A Database of Human Segmented Images,
PercOrg01(xx-yy). 0106
Detect and localize boundaries using local measurements. BibRef

Arbelaez, P.[Pablo], Fowlkes, C.C.[Charless C.], and Martin, D.R.[David R.],
The Berkeley Segmentation Dataset and Benchmark,
Online2007. Dataset, Segmentation. Dataset, BSDS. Code, Segmentation.
WWW Link. The updated code and data for the earlier paper. See also Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics, A. BibRef 0700

Martin, D.R.[David R.], Fowlkes, C.C.[Charless C.], Tal, D.[Doron], Malik, J.[Jitendra],
A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics,
ICCV01(II: 416-423).
IEEE DOI 0106
Award, PAMI Helmholtz Prize, 2015. BibRef
And:
A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms,
PercOrg01(xx-yy). Multiple human segmentations and a segmentation consistency measure. Human-human are consistent with the measure, different images are not consistent. Promised online availability. 1000 images with hand segmentations. Multiple hand segmentations. BibRef

Martin, D.R.[David R.],
An Empirical Approach to Grouping and Segmentation,
Ph.D.dissertation, Univ. of California, Berkeley, 2002. BibRef 0200

Arbelaez, P.[Pablo], Maire, M.[Michael], Fowlkes, C.C.[Charless C.], Malik, J.[Jitendra],
Contour Detection and Hierarchical Image Segmentation,
PAMI(33), No. 1, January 2011, pp. 898-916.
IEEE DOI 1104
BibRef
Earlier:
From contours to regions: An empirical evaluation,
CVPR09(2294-2301).
IEEE DOI 0906
See also Using contours to detect and localize junctions in natural images. Contour detection and turn contours into hierarchy of regions. BibRef

Maire, M.[Michael], Yu, S.X.[Stella X.], Perona, P.[Pietro],
Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling,
ACCV14(IV: 273-287).
Springer DOI 1504
BibRef

Maire, M.[Michael], Yu, S.X.[Stella X.],
Progressive Multigrid Eigensolvers for Multiscale Spectral Segmentation,
ICCV13(2184-2191)
IEEE DOI 1403
BibRef

Hariharan, B.[Bharath], Arbelaez, P.[Pablo], Bourdev, L.[Lubomir], Maji, S.[Subhransu], Malik, J.[Jitendra],
Semantic contours from inverse detectors,
ICCV11(991-998).
IEEE DOI 1201
Contours of category-specific objects. See also Semantic Boundaries Dataset and Benchmark. BibRef

Wei, K., Jing, Z.L., Li, Y.X., Tuo, H.Y.,
Extended scheme of Chan-Vese models for colour image segmentation,
IET-IPR(5), No. 7, 2011, pp. 583-597.
DOI Link 1108
See also Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model, A. BibRef

Yang, X.W.[Xing-Wei], Liu, H.R.[Hai-Rong], Latecki, L.J.[Longin Jan],
Contour-based object detection as dominant set computation,
PR(45), No. 5, May 2012, pp. 1927-1936.
Elsevier DOI 1201
Object detection; Shape similarity; Dominant sets BibRef

Butenuth, M.[Matthias], Heipke, C.[Christian],
Network snakes: Graph-based object delineation with active contour models,
MVA(23), No. 1, January 2012, pp. 91-109.
WWW Link. 1201
BibRef
Earlier:
Network Snakes-Supported Extraction of Field Boundaries from Imagery,
DAGM05(417).
Springer DOI 0509
BibRef

Butenuth, M.[Matthias],
Topology-Preserving Network Snakes,
ISPRS08(B3a: 229 ff).
PDF File. 0807
BibRef
Earlier:
Segmentation of Imagery Using Network Snakes,
PCV06(xx-yy).
PDF File. 0609
BibRef

Pätz, T.[Torben], Preusser, T.[Tobias],
Segmentation of Stochastic Images With a Stochastic Random Walker Method,
IP(21), No. 5, May 2012, pp. 2424-2433.
IEEE DOI 1204
BibRef
Earlier:
Ambrosio-Tortorelli Segmentation of Stochastic Images,
ECCV10(V: 254-267).
Springer DOI 1009
See also Approximation of functionals depending on jumps by elliptic functionals via ..-convergence. BibRef

Pätz, T.[Torben], Kirby, R.M.[Robert M.], Preusser, T.[Tobias],
Ambrosio-Tortorelli Segmentation of Stochastic Images: Model Extensions, Theoretical Investigations and Numerical Methods,
IJCV(103), No. 2, June 2013, pp. 190-212.
WWW Link. 1306
BibRef

Pätz, T.[Torben], Preusser, T.[Tobias],
Segmentation of Stochastic Images using Level Set Propagation with Uncertain Speed,
JMIV(48), No. 3, March 2014, pp. 467-487.
WWW Link. 1403
BibRef

Wang, H.J.[Hai-Jun], Liu, M.[Ming],
Medical Images Segmentation Using Active Contours Driven by Global and Local Image Fitting Energy,
IJIG(12), No. 2, April 2012, pp. 1250015.
DOI Link 1205
BibRef

Gao, S.B.[Shang-Bing], Yang, J.[Jian], Yan, Y.Y.[Yun-Yang],
A local modified Chan-Vese model for segmenting inhomogeneous multiphase images,
IJIST(22), No. 2, June 2012, pp. 103-113.
DOI Link 1202
See also Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model, A. BibRef

Gao, S.B.[Shang-Bing], Yang, J.[Jian],
Saliency-seeded localizing region-based active contour for automatic natural object segmentation,
ICPR12(3644-3647).
WWW Link. 1302
BibRef

Nakhmani, A., Tannenbaum, A.,
Self-Crossing Detection and Location for Parametric Active Contours,
IP(21), No. 7, July 2012, pp. 3150-3156.
IEEE DOI 1206
BibRef

Cardinale, J.[Janick], Paul, G.[Grégory], Sbalzarini, I.F.[Ivo F.],
Discrete Region Competition for Unknown Numbers of Connected Regions,
IP(21), No. 8, August 2012, pp. 3531-3545.
IEEE DOI 1208
BibRef

Paul, G.[Grégory], Cardinale, J.[Janick], Sbalzarini, I.F.[Ivo F.],
Coupling Image Restoration and Segmentation: A Generalized Linear Model/Bregman Perspective,
IJCV(104), No. 1, August 2013, pp. 69-93.
Springer DOI 1307
BibRef

Jung, M.[Miyoun], Peyré, G.[Gabriel], Cohen, L.D.[Laurent D.],
Non-local segmentation and inpaiting,

Nonlocal Active Contours,
SIIMS(5), No. 3 2012, pp. 1022-1054.
DOI Link 1209
BibRef
Earlier: ICIP11(3373-3376).
IEEE DOI 1201
BibRef
Earlier:
Non-local Active Contours,
SSVM11(255-266).
Springer DOI 1201
BibRef
Earlier:
Texture Segmentation via Non-local Non-parametric Active Contours,
EMMCVPR11(74-88).
Springer DOI 1107
BibRef

Shyu, K.K.[Kuo-Kai], Pham, V.T.[Van-Truong], Tran, T.T.[Thi-Thao], Lee, P.L.[Po-Lei],
Unsupervised active contours driven by density distance and local fitting energy with applications to medical image segmentation,
MVA(23), No. 6, November 2012, pp. 1159-1175.
WWW Link. 1210
BibRef

Tran, T.T.[Thi-Thao], Pham, V.T.[Van-Truong], Shyu, K.K.[Kuo-Kai],
Moment-based alignment for shape prior with variational B-spline level set,
MVA(24), No. 5, July 2013, pp. 1075-1091.
WWW Link. 1306
BibRef

Tran, T.T.[Thi-Thao], Pham, V.T.[Van-Truong], Shyu, K.K.[Kuo-Kai],
Zernike moment and local distribution fitting fuzzy energy-based active contours for image segmentation,
SIViP(8), No. 1, January 2014, pp. 11-25.
WWW Link. 1402
BibRef

Tran, T.T.[Thi-Thao], Pham, V.T.[Van-Truong], Shyu, K.K.[Kuo-Kai],
Image segmentation using fuzzy energy-based active contour with shape prior,
JVCIR(25), No. 7, 2014, pp. 1732-1745.
Elsevier DOI 1410
Image segmentation BibRef

Li, D.[Danyi], Li, W.F.[Wei-Feng], Liao, Q.[Qingmin],
Active contours driven by local and global probability distributions,
JVCIR(24), No. 5, July 2013, pp. 522-533.
Elsevier DOI 1306
Image segmentation; Signed pressure force function; Active contour model; Intensity inhomogeneity; Level set method; Local probability distribution; Data-based prior probability; Hybrid model BibRef

Liu, J.[Jun], Zhang, H.L.[Hai-Li],
Image Segmentation Using a Local GMM in a Variational Framework,
JMIV(46), No. 2, June 2013, pp. 161-176.
WWW Link. 1306
BibRef

Israel-Jost, V.[Vincent], Darbon, J.[Jérôme], Angelini, E.D.[Elsa D.], Bloch, I.[Isabelle],
Conciliating syntactic and semantic constraints for multi-phase and multi-channel region segmentation,
CVIU(117), No. 8, August 2013, pp. 819-826.
Elsevier DOI 1306
BibRef
And:
On the implementation of the multi-phase region segmentation, solving the hidden phase problem,
ICIP14(4338-4342)
IEEE DOI 1502
Computational modeling Segmentation; Piecewise-constant Mumford-Shah functional; Multi-channel fusion; Syntax and semantics of segmentation BibRef

Zhang, Y.[Yan], Matuszewski, B.J.[Bogdan J.], Histace, A.[Aymeric], Precioso, F.[Frédéric],
Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation,
JMIV(47), No. 1-2, September 2013, pp. 35-47.
Springer DOI 1307
BibRef
Earlier:
Statistical Shape Model of Legendre Moments with Active Contour Evolution for Shape Detection and Segmentation,
CAIP11(I: 51-58).
Springer DOI 1109
BibRef

Ksantini, R.[Riadh], Boufama, B.S.[Boubakeur S.], Memar, S.[Sara],
A new efficient active contour model without local initializations for salient object detection,
JIVP(2013), No. 1, 2013, pp. 40.
DOI Link 1307
BibRef

Memar, S.[Sara], Jin, K.[Karen], Boufama, B.S.[Boubakeur S.],
Object Detection Using Active Contour Model with Depth Clue,
ICIAR13(640-647).
Springer DOI 1307
BibRef

Memar, S.[Sara], Ksantini, R.[Riadh], Boufama, B.S.[Boubakeur S.],
Feature-based active contour model and occluding object detection,
JOSA-A(33), No. 4, April 2016, pp. 648-662.
DOI Link 1604
BibRef
Earlier:
Multiple Object Detection with Occlusion Using Active Contour Model and Fuzzy C-Mean,
ICIAR14(I: 224-233).
Springer DOI 1410
Image processing; Pattern recognition BibRef

Ksantini, R.[Riadh], Shariat, F.[Farnaz], Boufama, B.S.[Boubakeur S.],
An Efficient and Fast Active Contour Model for Salient Object Detection,
CRV09(124-131).
IEEE DOI 0905
BibRef

Yan, P.K.[Ping-Kun], Zhang, W.[Wuxia], Turkbey, B.[Baris], Choyke, P.L.[Peter L.], Li, X.L.[Xue-Long],
Global structure constrained local shape prior estimation for medical image segmentation,
CVIU(117), No. 9, 2013, pp. 1017-1026.
Elsevier DOI 1307
Target-oriented shape modeling BibRef

Gao, Y., Bouix, S.[Sylvain], Shenton, M., Tannenbaum, A.,
Sparse Texture Active Contour,
IP(22), No. 10, 2013, pp. 3866-3878.
IEEE DOI 1309
Active contour; Sparse representation; Texture representation BibRef

Li, D.[Danyi], Li, W.F.[Wei-Feng], Liao, Q.M.[Qing-Min],
A Fuzzy Geometric Active Contour Method for Image Segmentation,
IEICE(E96-D), No. 9, September 2013, pp. 2107-2114.
WWW Link. 1309
BibRef

Mei, J., Si, Y., Gao, H.,
A Curve Evolution Approach for Unsupervised Segmentation of Images With Low Depth of Field,
IP(22), No. 10, 2013, pp. 4086-4095.
IEEE DOI 1309
Image segmentation BibRef

Zhang, H.G.[Hui-Gang], Bai, X.[Xiao], Zhou, J.[Jun], Cheng, J.[Jian], Zhao, H.[Huijie],
Object Detection Via Structural Feature Selection and Shape Model,
IP(22), No. 12, 2013, pp. 4984-4995.
IEEE DOI 1312
image matching BibRef

Bai, X.[Xiao], Zhang, H.G.[Hui-Gang], Zhou, J.[Jun],
VHR Object Detection Based on Structural Feature Extraction and Query Expansion,
GeoRS(52), No. 10, October 2014, pp. 6508-6520.
IEEE DOI 1407
Feature extraction BibRef

Zhang, H.G.[Hui-Gang], Wang, J.X.[Jun-Xiu], Bai, X.[Xiao], Zhou, J.[Jun], Cheng, J.[Jian], Zhao, H.[Huijie],
Object detection via foreground contour feature selection and part-based shape model,
ICPR12(2524-2527).
WWW Link. 1302
BibRef

Bova, N.[Nicola], Ibáńez, Ó.[Óscar], Cordón, Ó.[Óscar],
Extended Topological Active Nets,
IVC(31), No. 12, 2013, pp. 905-920.
Elsevier DOI 1312
Deformable models integrating features of region and boundary based segmentation. BibRef

Balla-Arabe, S., Gao, X., Wang, B., Yang, F., Brost, V.,
Multi-Kernel Implicit Curve Evolution for Selected Texture Region Segmentation in VHR Satellite Images,
GeoRS(52), No. 8, August 2014, pp. 5183-5192.
IEEE DOI 1403
Active contours BibRef

Gueguen, L.[Lionel], Velasco-Forero, S.[Santiago], Soille, P.[Pierre],
Local Mutual Information for Dissimilarity-Based Image Segmentation,
JMIV(48), No. 3, March 2014, pp. 625-644.
Springer DOI 1403
BibRef

Wu, Q., An, J.,
An Active Contour Model Based on Texture Distribution for Extracting Inhomogeneous Insulators From Aerial Images,
GeoRS(52), No. 6, June 2014, pp. 3613-3626.
IEEE DOI 1403
Active contours BibRef

Tari, S.[Sibel], Genctav, M.[Murat],
From a Non-Local Ambrosio-Tortorelli Phase Field to a Randomized Part Hierarchy Tree,
JMIV(49), No. 1, May 2014, pp. 69-86.
WWW Link. 1404
BibRef
Earlier:
From a Modified Ambrosio-Tortorelli to a Randomized Part Hierarchy Tree,
SSVM11(267-278).
Springer DOI 1201
BibRef

Duggan, N.[Nóirín], Bae, E.[Egil], Jones, E.[Edward], Glavin, M.[Martin], Vese, L.A.[Luminita A.],
A simple boundary reinforcement technique for segmentation without prior,
PRL(46), No. 1, 2014, pp. 27-35.
Elsevier DOI 1407
Image segmentation BibRef

Judah, A.[Aaron], Hu, B.X.[Bao-Xin], Wang, J.G.[Jian-Guo],
An Algorithm for Boundary Adjustment toward Multi-Scale Adaptive Segmentation of Remotely Sensed Imagery,
RS(6), No. 5, 2014, pp. 3583-3610.
DOI Link 1407
BibRef

Liu, G., Zhou, Z., Zhong, H., Xie, S.,
Gradient descent with adaptive momentum for active contour models,
IET-CV(8), No. 4, August 2014, pp. 287-298.
DOI Link 1407
BibRef

Ge, Q.[Qi], Xiao, L.[Liang], Zhang, J.[Jun], Wei, Z.H.[Zhi Hui],
An improved region-based model with local statistical features for image segmentation,
PR(45), No. 4, 2012, pp. 1578-1590.
Elsevier DOI 1410
BibRef
Earlier: A1, A4, A2, A3:
An improved region-based model with local statistical feature,
ICIP11(3341-3344).
IEEE DOI 1201
Active contour model BibRef

Benninghoff, H.[Heike], Garcke, H.[Harald],
Efficient Image Segmentation and Restoration Using Parametric Curve Evolution with Junctions and Topology Changes,
SIIMS(7), No. 3, 2014, pp. 1451-1483.
DOI Link 1410
BibRef

Benninghoff, H.[Heike], Garcke, H.[Harald],
Image Segmentation Using Parametric Contours With Free Endpoints,
IP(25), No. 4, April 2016, pp. 1639-1648.
IEEE DOI 1604
image denoising BibRef

Benninghoff, H.[Heike], Garcke, H.[Harald],
Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes,
JMIV(55), No. 1, May 2016, pp. 105-124.
WWW Link. 1604
BibRef

Storath, M.[Martin], Weinmann, A.[Andreas],
Fast Partitioning of Vector-Valued Images,
SIIMS(7), No. 3, 2014, pp. 1826-1852.
DOI Link 1410
BibRef

Storath, M.[Martin], Weinmann, A.[Andreas], Unser, M.[Michael],
Unsupervised texture segmentation using monogenic curvelets and the Potts model,
ICIP14(4348-4352)
IEEE DOI 1502
Biomedical imaging BibRef

Roy, S.[Sawrav], Mukhopadhyay, S.[Susanta], Mishra, M.K.[Manoj K.],
Enhancement of morphological snake based segmentation by imparting image attachment through scale-space continuity,
PR(48), No. 7, 2015, pp. 2254-2268.
Elsevier DOI 1504
Object segmentation BibRef

Subudhi, P.[Priyambada], Mukhopadhyay, S.[Susanta],
Object Segmentation in Texture Images Using Texture Gradient Based Active Contours,
PReMI17(124-131).
Springer DOI 1711
BibRef

Dai, L.Z.[Ling-Zheng], Ding, J.D.[Jun-Di], Yang, J.[Jian],
Inhomogeneity-embedded active contour for natural image segmentation,
PR(48), No. 8, 2015, pp. 2513-2529.
Elsevier DOI 1505
Active contour BibRef

Saadatmand-Tarzjan, M.,
Self-affine snake for medical image segmentation,
PRL(59), No. 1, 2015, pp. 1-10.
Elsevier DOI 1505
Parametric active contours BibRef

Lecca, M.[Michela], Messelodi, S.[Stefano], Serapioni, R.P.[Raul Paolo],
A New Region-based Active Contour Model for Object Segmentation,
JMIV(53), No. 2, October 2015, pp. 233-249.
Springer DOI 1508
BibRef

Garrido, L.[Luis], Guerrieri, M., Igual, L.[Laura],
Image Segmentation With Cage Active Contours,
IP(24), No. 12, December 2015, pp. 5557-5566.
IEEE DOI 1512
Gaussian processes BibRef

Antonelli, L.[Laura], De Simone, V.[Valentina], di Serafino, D.[Daniela],
On the Application of the Spectral Projected Gradient Method in Image Segmentation,
JMIV(54), No. 1, January 2016, pp. 106-116.
Springer DOI 1601
BibRef

Chan, D.Y.[Din-Yuen], Hsu, R.C.[Roy Chaoming], Liu, C.T.[Cheng-Ting], Tsai, C.H.[Cheng-Han],
Rectification-conducted adaptive snake for segmenting complex-boundary objects from textured backgrounds,
SIViP(10), No. 1, February 2016, pp. 225-234.
Springer DOI 1601
BibRef

Hussain, S., Chun, Q.[Qi], Asif, M.R., Khan, M.S.,
Active contours for image segmentation using complex domain-based approach,
IET-IPR(10), No. 2, 2016, pp. 121-129.
DOI Link 1602
Gaussian processes BibRef

Xia, G.S., Liu, G., Yang, W., Zhang, L.,
Meaningful Object Segmentation From SAR Images via a Multiscale Nonlocal Active Contour Model,
GeoRS(54), No. 3, March 2016, pp. 1860-1873.
IEEE DOI 1603
Active contours BibRef

Bui, T.D.[T. Duc], Ahn, C., Shin, J.,
Fast localised active contour for inhomogeneous image segmentation,
IET-IPR(10), No. 6, 2016, pp. 483-494.
DOI Link 1606
computational complexity BibRef

Fang, Z.W.[Zhi-Wen], Cao, Z.G.[Zhi-Guo], Xiao, Y.[Yang], Zhu, L.[Lei], Yuan, J.S.[Jun-Song],
Adobe Boxes: Locating Object Proposals Using Object Adobes,
IP(25), No. 9, September 2016, pp. 4116-4128.
IEEE DOI 1609
object detection. First get coarse object proposals. Then search in area around proposals. Then converge bounding box. BibRef

Heimowitz, A., Keller, Y.,
Image Segmentation via Probabilistic Graph Matching,
IP(25), No. 10, October 2016, pp. 4743-4752.
IEEE DOI 1610
graph theory BibRef

Xu, Y.C.[Yong-Chao], Géraud, T.[Thierry], Najman, L.[Laurent],
Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection,
PRL(83, Part 3), No. 1, 2016, pp. 278-286.
Elsevier DOI 1609
BibRef
Earlier:
Salient level lines selection using the Mumford-Shah functional,
ICIP13(1227-1231)
IEEE DOI 1402
BibRef
Earlier:
Context-based energy estimator: Application to object segmentation on the tree of shapes,
ICIP12(1577-1580).
IEEE DOI 1302
Level line Image segmentation. Given a family of closed contours find the correct one. BibRef

Xu, Y.C.[Yong-Chao], Carlinet, E.[Edwin], Geraud, T.[Thierry], Najman, L.[Laurent],
Meaningful disjoint level lines selection,
ICIP14(2938-2942)
IEEE DOI 1502
Algorithm design and analysis BibRef

Jevnisek, R.J.[Roy Josef], Avidan, S.[Shai],
Semi global boundary detection,
CVIU(152), No. 1, 2016, pp. 21-28.
Elsevier DOI 1609
Edge/Boundary detection BibRef

Niu, S.[Sijie], Chen, Q.A.[Qi-Ang], de Sisternes, L.[Luis], Ji, Z.X.[Ze-Xuan], Zhou, Z.M.[Ze-Ming], Rubin, D.L.[Daniel L.],
Robust noise region-based active contour model via local similarity factor for image segmentation,
PR(61), No. 1, 2017, pp. 104-119.
Elsevier DOI 1705
Local similarity factor BibRef

Hoogi, A., Subramaniam, A., Veerapaneni, R., Rubin, D.L.[Daniel L.],
Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis,
MedImg(36), No. 3, March 2017, pp. 781-791.
IEEE DOI 1703
Active contours BibRef

Gao, G.W.[Guo-Wei], Wen, C.L.[Cheng-Lin], Wang, H.B.[Hui-Bin],
Fast and robust image segmentation with active contours and Student's-t mixture model,
PR(63), No. 1, 2017, pp. 71-86.
Elsevier DOI 1612
Segmentation BibRef

Bampis, C.G.[Christos G.], Maragos, P.[Petros], Bovik, A.C.,
Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation,
IP(26), No. 1, January 2017, pp. 35-50.
IEEE DOI 1612
BibRef
Earlier: A1, A2, Only:
Unifying the random walker algorithm and the SIR model for graph clustering and image segmentation,
ICIP15(2265-2269)
IEEE DOI 1512
graph theory. Random Walker BibRef

Pratondo, A.[Agus], Chui, C.K.[Chee-Kong], Ong, S.H.[Sim-Heng],
Integrating machine learning with region-based active contour models in medical image segmentation,
JVCIR(43), No. 1, 2017, pp. 1-9.
Elsevier DOI 1702
Machine learning BibRef

Zhang, L.[Ling], Peng, X.G.[Xin-Guang], Li, G.[Gang], Li, H.F.[Hai-Fang],
A Novel Active Contour Model for Image Segmentation Using Local and Global Region-Based Information,
MVA(28), No. 1-2, February 2017, pp. 75-89.
WWW Link. 1702
BibRef

Huo, G., Yang, S.X., Li, Q., Zhou, Y.,
A Robust and Fast Method for Sidescan Sonar Image Segmentation Using Nonlocal Despeckling and Active Contour Model,
Cyber(47), No. 4, April 2017, pp. 855-872.
IEEE DOI 1704
Active contours BibRef

Gao, G., Wen, C., Wang, H., Xu, L.,
Fast Multiregion Image Segmentation Using Statistical Active Contours,
SPLetters(24), No. 4, April 2017, pp. 417-421.
IEEE DOI 1704
image segmentation BibRef

Reska, D.[Daniel], Boldak, C.[Cezary], Kretowski, M.[Marek],
Towards multi-stage texture-based active contour image segmentation,
SIViP(11), No. 5, July 2017, pp. 809-816.
Springer DOI 1706
BibRef

Lv, P.[Peng], Zhao, Q.J.[Qing-Jie], Chen, Y.M.[Yan-Ming], Zhao, L.J.[Liu-Jun],
Multiple cues-based active contours for target contour tracking under sophisticated background,
VC(33), No. 9, September 2017, pp. 1103-1119.
WWW Link. 1708
BibRef

Niethammer, M.[Marc], Pohl, K.M.[Kilian M.], Janoos, F.[Firdaus], Wells III, W.M.[William M.],
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models,
SIIMS(10), No. 3, 2017, pp. 1069-1103.
DOI Link 1710
BibRef


Dehkordi, M.T.,
A new active contour model for tumor segmentation,
IPRIA17(233-236)
IEEE DOI 1712
Gaussian processes, biomedical MRI, image filtering, image segmentation, medical image processing, probability, tumours, feature BibRef

Premachandran, V.[Vittal], Bonev, B.[Boyan], Lian, X.C.[Xiao-Chen], Yuille, A.L.[Alan L.],
PASCAL Boundaries: A Semantic Boundary Dataset with a Deep Semantic Boundary Detector,
WACV17(73-81)
IEEE DOI 1609
Dataset, Edeg Detection. See also Semantic Boundaries Dataset and Benchmark. Computer vision, Context, Databases, Detectors, Image edge detection, Image segmentation, Semantics Related to: See also PASCAL Visual Object Classes Challenge 2012, The. BibRef

Yuan, J.[Jing], Yin, K.[Ke], Bai, Y.G.[Yi-Guang], Feng, X.C.[Xiang-Chu], Tai, X.C.[Xue-Cheng],
Bregman-Proximal Augmented Lagrangian Approach to Multiphase Image Segmentation,
SSVM17(524-534).
Springer DOI 1706
BibRef

Gao, M., Chen, H., Zheng, S., Fang, B.,
A factorization based active contour model for texture segmentation,
ICIP16(4309-4313)
IEEE DOI 1610
Active contours BibRef

Moinar, J., Szucs, A.I., Molnar, C., Horvath, P.,
Active contours for selective object segmentation,
WACV16(1-9)
IEEE DOI 1606
Active contours BibRef

Gu, Y.[Ying], Xiong, W.[Wei], Wang, L.L.[Li-Lian], Cheng, J.R.[Jie-Rong], Du, J.[Jia], Chen, W.Y.[Wen-Yu], Wang, Y.[Yue], Chia, S.[ShueChing],
A new Mumford-Shah type model involving a smoothing operator for multiphase image segmentation,
ICIP15(1990-1994)
IEEE DOI 1512
Gaussian; Image segmentation; bilateral; smoothing operator BibRef

Dogan, G.[Günay],
An Efficient Lagrangian Algorithm for an Anisotropic Geodesic Active Contour Model,
SSVM17(408-420).
Springer DOI 1706
BibRef
Earlier:
Fast Minimization of Region-Based Active Contours Using the Shape Hessian of the Energy,
SSVM15(307-319).
Springer DOI 1506
BibRef
And:
An Efficient Curve Evolution Algorithm for Multiphase Image Segmentation,
EMMCVPR15(292-306).
Springer DOI 1504
BibRef

Khadidos, A.[Alaa], Sanchez, V.[Victor], Li, C.T.[Chang-Tsun],
Active contours based on weighted gradient vector flow and balloon forces for medical image segmentation,
ICIP14(902-906)
IEEE DOI 1502
Active contours BibRef

Cai, L.[Ling], Wang, F.[Fengna], Enescu, V.[Valentin], Sahli, H.[Hichem],
Object Segmentation Based on Contour-Skeleton Duality,
ICPR14(2537-2542)
IEEE DOI 1412
Image edge detection BibRef

Dahl, V.A.[Vedrana Andersen], Dahl, A.B.[Anders Bjorholm],
A Probabilistic Framework for Curve Evolution,
SSVM17(421-432).
Springer DOI 1706
BibRef
Earlier: A2, A1:
Dictionary Based Image Segmentation,
SCIA15(26-37).
Springer DOI 1506
BibRef
Earlier: A2, A1:
Dictionary Snakes,
ICPR14(142-147)
IEEE DOI 1412
Active contours BibRef

Dahl, V.A.[Vedrana Andersen], Christiansen, A.N.[Asger Nyman], Baerentzen, J.A.[Jakob Andreas],
Multiphase Image Segmentation Using the Deformable Simplicial Complex Method,
ICPR14(1002-1007)
IEEE DOI 1412
Deformable models BibRef

Zhang, H.H.[Hong-Hui], Wang, J.D.[Jing-Dong], Tan, P.[Ping], Wang, J.L.[Jing-Lu], Quan, L.[Long],
Learning CRFs for Image Parsing with Adaptive Subgradient Descent,
ICCV13(3080-3087)
IEEE DOI 1403
Adaptive Subgradient Descent; Conditional Random Field; Image Parsing BibRef

Li, Z.Y.[Zhen-Yang], Gavves, E.[Efstratios], van de Sande, K.E.A.[Koen E.A.], Snoek, C.G.M.[Cees G.M.], Smeulders, A.W.M.[Arnold W.M.],
Codemaps: Segment, Classify and Search Objects Locally,
ICCV13(2136-2143)
IEEE DOI 1403
link classification score and local neighborhood. BibRef

Yanez, E.M.[Eva M.], Cuevas, C.[Carlos], Garcia, N.[Narciso],
A combined active contours method for segmentation using localization and multiresolution,
ICIP13(1257-1261)
IEEE DOI 1402
Active contours BibRef

Javed, U.[Umer], Riaz, M.M.[M.Mohsin], Khokher, M.R.[Muhammad Rizwan], Ghafoor, A.[Abdul], Cheema, T.A.[Tanveer A.],
Fuzzy logic and local features based medical image segmentation,
ICIP13(1148-1152)
IEEE DOI 1402
Active contours BibRef

Derraz, F.[Foued], Pinti, A.[Antonio], Boussahla, M.[Miloud], Peyrodie, L.[Laurent], Toumi, H.[Hechmi],
Image Segmentation Using Active Contours and Evidential Distance,
CIARP13(I:472-479).
Springer DOI 1311
BibRef

Thomas, A.[Anu], Oommen, B.J.[B. John],
A Novel Border Identification Algorithm Based on an 'Anti-Bayesian' Paradigm,
CAIP13(196-203).
Springer DOI 1308
BibRef

Villeneuve, G.[Guillaume], Bergevin, R.[Robert],
On Structuring Multiple Grouping Hypotheses in Generic Object Detection,
CRV13(340-347)
IEEE DOI 1308
Complexity theory. Contour grouping to detect unknown objects. BibRef

Antunes, M.[Mário], Lopes, L.S.[Luís Seabra],
Contour-Based Object Extraction and Clutter Removal for Semantic Vision,
ICIAR13(170-180).
Springer DOI 1307
BibRef

Antunes, M.[Mário], Lopes, L.S.[Luís Seabra],
Unsupervised Internet-Based Category Learning for Object Recognition,
ICIAR13(766-773).
Springer DOI 1307
BibRef

Mori, F.[Fumihiko], Mori, T.[Terunori],
Region Segmentation and Object Extraction Based on Virtual Edge and Global Features,
CompPhot12(I:182-193).
Springer DOI 1304
BibRef

Naikal, N.[Nikhil], Singaraju, D.[Dheeraj], Sastry, S.S.[S. Shankar],
Using Models of Objects with Deformable Parts for Joint Categorization and Segmentation of Objects,
ACCV12(II:79-93).
Springer DOI 1304
BibRef

Srikham, M.[Manassanan],
Active contours segmentation with edge based and local region based,
ICPR12(1989-1992).
WWW Link. 1302
BibRef

Shah, P.[Pratik], Gupta, M.D.[Mithun Das],
Simultaneous Registration and Segmentation by L1 Minimization,
MLMI12(128-135).
Springer DOI 1211
BibRef

Klodt, M.[Maria], Cremers, D.[Daniel],
A convex framework for image segmentation with moment constraints,
ICCV11(2236-2243).
IEEE DOI 1201
relaxation for segmentation. Shape priors BibRef

Aitfares, W., Herbulot, A., Devy, M., Bouyakhf, E.H., Regragui, F.,
A novel region-based active contour approach relying on local and global information,
ICIP11(1029-1032).
IEEE DOI 1201
BibRef

Jager, F.[Fabian],
Contour-based segmentation and coding for depth map compression,
VCIP11(1-4).
IEEE DOI 1201
BibRef

Asahi, T.[Takeshi], Ortega, J.H.[Jaime H.], Lecaros, R.[Rodrigo],
Multicolor image segmentation using Ambrosio-Tortorelli approximation,
ICIP11(2865-2868).
IEEE DOI 1201
BibRef

Schlecht, J.[Joseph], Ommer, B.[Björn],
Contour-based object detection,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Shemesh, M.[Michal], Ben-Shahar, O.[Ohad],
Free Boundary Conditions Active Contours with Applications for Vision,
ISVC11(I: 180-191).
Springer DOI 1109
BibRef

Guo, Y.R.[Yan-Rong], Jiang, J.G.[Jian-Guo], Hao, S.J.[Shi-Jie], Zhan, S.[Shu],
Distribution-Based Active Contour Model for Medical Image Segmentation,
ICIG11(61-65).
IEEE DOI 1109
BibRef

Krajsek, K.[Kai], Dedovic, I.[Ines], Scharr, H.[Hanno],
An Estimation Theoretical Approach to Ambrosio-Tortorelli Image Segmentation,
DAGM11(41-50).
Springer DOI 1109
See also Approximation of functionals depending on jumps by elliptic functionals via ..-convergence. BibRef

Thieu, Q.T.[Quang Tung], Luong, M.[Marie], Rocchisani, J.M.[Jean-Marie], Sirakov, N.M.[Nikolay Metodiev], Viennet, E.[Emmanuel],
Segmentation by a Local and Global Fuzzy Gaussian Distribution Energy Minimization of an Active Contour Model,
IWCIA12(298-312).
Springer DOI 1211
BibRef

Thieu, Q.T.[Quang Tung], Luong, M.[Marie], Rocchisani, J.M.[Jean-Marie], Viennet, E.[Emmanuel], Tran, D.,
Novel Convex Active Contour Model Using Local and Global Information,
DICTA11(346-351).
IEEE DOI 1205
BibRef

Thieu, Q.T.[Quang Tung], Luong, M.[Marie], Rocchisani, J.M.[Jean-Marie], Viennet, E.[Emmanuel],
A Convex Active Contour Region-Based Model for Image Segmentation,
CAIP11(I: 135-143).
Springer DOI 1109
BibRef

Guevara, A.[Alvaro], Conrad, C.[Christian], Mester, R.[Rudolf],
Curvature oriented clustering of sparse motion vector fields,
Southwest12(161-164).
IEEE DOI 1205
BibRef
Earlier:
Boosting segmentation results by contour relaxation,
ICIP11(1405-1408).
IEEE DOI 1201
BibRef
Earlier: A3, A2, A1:
Multichannel Segmentation Using Contour Relaxation: Fast Super-Pixels and Temporal Propagation,
SCIA11(250-261).
Springer DOI 1105
See also Learning multi-view correspondences from temporal coincidences. BibRef

Gao, Y.[Yi], Tannenbaum, A.R.[Allen R.], Kikinis, R.[Ron],
Simultaneous Multi-object Segmentation Using Local Robust Statistics and Contour Interaction,
MCV10(195-203).
Springer DOI 1009
BibRef

Saha, B.N.[Baidya Nath], Ray, N.[Nilanjan], Zhang, H.[Hong],
Automating Snakes for Multiple Objects Detection,
ACCV10(III: 39-51).
Springer DOI 1011
BibRef

Pont-Tuset, J.[Jordi], Marques, F.[Ferran],
Contour detection using Binary Partition Trees,
ICIP10(1609-1612).
IEEE DOI 1009
BibRef

Yu, W.[Wei], Franchetti, F.[Franz], Chang, Y.J.[Yao-Jen], Chen, T.H.[Tsu-Han],
Fast and robust active contours for image segmentation,
ICIP10(641-644).
IEEE DOI 1009
BibRef

Chen, G.[Gang], Zhang, H.Y.[Hai-Ying], Chen, I.[Iron], Yang, W.[Wen],
Active Contours with Thresholding Value for Image Segmentation,
ICPR10(2266-2269).
IEEE DOI 1008
BibRef

Hasan, B.[Basela], Hogg, D.C.[David C.],
Segmentation using Deformable Spatial Priors with Application to Clothing,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Sargin, M.E., Bertelli, L., Manjunath, B.S., Rose, K.,
Probabilistic occlusion boundary detection on spatio-temporal lattices,
ICCV09(560-567).
IEEE DOI 0909
BibRef

Xu, Q.Z.[Qi-Zhi], Hu, L.[Lei], Li, B.[Bo], Liu, Y.K.[Yang-Ke],
Object Contour Extraction Based on Intensity and Texture Information,
CISP09(1-6).
IEEE DOI 0910
BibRef

Wan, G.H.[Guo-Hong], Huang, X.H.[Xin-Han], Wang, M.[Min],
An Improved Active Contours Model Based on Morphology for Image Segmentation,
CISP09(1-5).
IEEE DOI 0910
BibRef

Ouyang, C.S.[Cheng-Su], Huang, Y.X.[Yong-Xuan], Yuan, J.[Jun],
A Novel Snake Model for X-Ray Image Segmentation,
CISP09(1-4).
IEEE DOI 0910
BibRef

Yang, Q.X.[Qiu-Xia], Tang, L.R.[Liang-Rui], Yu, W.W.[Wen-Wen],
Waterdrops Shape Extraction of Hydrophobic Image Based on Snake Model,
CISP09(1-3).
IEEE DOI 0910
BibRef

de Vieilleville, F.[François], Lachaud, J.O.[Jacques-Olivier],
Digital Deformable Model Simulating Active Contours,
DGCI09(203-216).
Springer DOI 0909
BibRef

Lachaud, J.O.[Jacques-Olivier], Vialard, A.[Anne],
Discrete Deformable Boundaries for the Segmentation of Multidimensional Images,
VF01(542 ff.).
Springer DOI 0209
BibRef

Suzuki, T.[Tetsuaki], Hebert, M.[Martial],
Estimating object region from local contour configuration,
VCL-ViSU09(69-76).
IEEE DOI 0906
Boundary and region info to find foreground objects. BibRef

Myronenko, A.[Andriy], Song, X.[Xubo],
Global active contour-based image segmentation via probability alignment,
CVPR09(2798-2804).
IEEE DOI 0906
BibRef

Huang, F.Z.[Fu-Zhen], Yang, X.H.[Xu-Hong],
Image Segmentation under Occlusion Using Selective Shape Priors,
ICIAR10(I: 89-95).
Springer DOI 1006
BibRef

Huang, F.Z.[Fu-Zhen], She, X.X.[Xing-Xing],
Multiphase Segmentation Based on Implicit Active Shape Models,
ICIAR09(211-219).
Springer DOI 0907
BibRef

Wan, C.K., Yuan, B.Z., Miao, Z.J.,
A new algorithm for static camera foreground segmentation via active coutours and GMM,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Vega-Pons, S.[Sandro], Gil-Rodriguez, J.L.[Jose Luis], Vera Perez, O.L.[Oscar Luis],
Active contour algorithm for texture segmentation using a texture feature set,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Bernardis, E.[Elena], Yu, S.X.[Stella X.],
Finding dots: Segmentation as popping out regions from boundaries,
CVPR10(199-206).
IEEE DOI 1006
BibRef
And:
Structural correspondence as a contour grouping problem,
MMBIA10(194-199).
IEEE DOI 1006
BibRef
Earlier:
Robust Segmentation by Cutting across a Stack of Gamma Transformed Images,
EMMCVPR09(249-260).
Springer DOI 0908
BibRef

Bernardis, E.[Elena], Shi, J.B.[Jian-Bo],
Shape Extraction through Region-Contour Stitching,
ISVC08(I: 393-405).
Springer DOI 0812
BibRef

Le, T.V.[Thang V.], Kulikowski, C.A.[Casimir A.], Muchnik, I.B.[Ilya B.],
A Graph-Based Approach for Image Segmentation,
ISVC08(I: 278-287).
Springer DOI 0812
BibRef

Jain, A.[Arpit], Gupta, A.[Abhinav], Davis, L.S.[Larry S.],
Learning What and How of Contextual Models for Scene Labeling,
ECCV10(IV: 199-212).
Springer DOI 1009
predict importance of edges in region labeling. BibRef

Ravishankar, S.[Saiprasad], Jain, A.[Arpit], Mittal, A.[Anurag],
Multi-stage Contour Based Detection of Deformable Objects,
ECCV08(I: 483-496).
Springer DOI 0810
BibRef

Farzinfar, M.[Mahshid], Xue, Z.[Zhong], Teoh, E.K.[Eam Khwang],
Joint Parametric and Non-parametric Curve Evolution for Medical Image Segmentation,
ECCV08(I: 167-178).
Springer DOI 0810
BibRef

Zadicario, E.[Eyal], Avidan, S.[Shai], Shmueli, A.[Alon], Cohen-Or, D.[Daniel],
Boundary snapping for robust image cutouts,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Vaudrey, T.[Tobi], Gruber, D.[Daniel], Wedel, A.[Andreas], Klappstein, J.[Jens],
Space-Time Multi-Resolution Banded Graph-Cut for Fast Segmentation,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Zhu, Q.H.[Qi-Hui], Song, G.[Gang], Shi, J.B.[Jian-Bo],
Untangling Cycles for Contour Grouping,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Zhuang, Y.T.[Yue-Ting], Chen, C.[Cheng],
Efficient Silhouette Extraction with Dynamic Viewpoint,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Li, Z.[Zhong], Najarian, K.[Kayvan],
Biomedical Image Segmentation Based on Shape Stability,
ICIP07(VI: 281-284).
IEEE DOI 0709
BibRef

Hudy, C.[Christopher], Campbell, J.[Jonathan], Slater, J.[John],
Model-based Edge Tracking for Segmentation of Low Contrast Images,
IMVIP07(212-212).
IEEE DOI 0709
BibRef

Marques, J.S.[Jorge S.], Figueiredo, M.A.T.[Mario A.T.],
Image super-segmentation: Segmentation with multiple labels from shuffled observations,
ICIP11(2849-2852).
IEEE DOI 1201
BibRef

Silveira, M.[Margarida], Marques, J.S.[Jorge S.],
Estimation of Multiple Objects at Unknown Locations with Active Contours,
IbPRIA07(II: 372-379).
Springer DOI 0706
BibRef

Simon, I.[Ian], Seitz, S.M.[Steven M.],
Scene Segmentation Using the Wisdom of Crowds,
ECCV08(II: 541-553).
Springer DOI 0810
BibRef
Earlier:
A Probabilistic Model for Object Recognition, Segmentation, and Non-Rigid Correspondence,
CVPR07(1-7).
IEEE DOI 0706
BibRef

Corpetti, T.,
An Active Contour Method Based on Wavelet for Texture Boundaries,
ICIP06(1109-1112).
IEEE DOI 0610
BibRef

Kim, S.H.[Shin-Hyoung], Jang, J.W.[Jong Whan],
Robust Contour Tracking Using a Modified Snake Model in Stereo Image Sequences,
ICIAR07(307-317).
Springer DOI 0708
BibRef
And:
An Improved Snake-Based Method for Object Contour Detection,
ICIP07(I: 249-252).
IEEE DOI 0709
BibRef

Alattar, A.M.[Ashraf M.], Jang, J.W.[Jong Whan],
A New Stereo Correspondence Method for Snake-Based Object Segmentation,
ICIP07(III: 381-384).
IEEE DOI 0709
BibRef

Kim, S.H.[Shin-Hyoung], Alattar, A.M.[Ashraf M.], Jang, J.W.[Jong Whan],
Snake-Based Objects Tracking in Stereo Sequences with the Optimization of the Number of Snake Points,
ICIP06(193-196).
IEEE DOI 0610
BibRef
And:
Accurate Contour Detection Based on Snakes for Objects with Boundary Concavities,
ICIAR06(I: 226-235).
Springer DOI 0610
BibRef

Han, D.F.[Dong-Feng], Li, W.H.[Wen-Hui], Lu, X.S.[Xiao-Suo], Li, L.[Lin], Wang, Y.[Yi],
Graph-Based Fast Image Segmentation,
SSPR06(468-474).
Springer DOI 0608
BibRef

Han, D.F.[Dong-Feng], Li, W.H.[Wen-Hui], Lu, X.S.[Xiao-Suo], Wang, T.Z.[Tian-Zhu], Wang, Y.[Yi],
Automatic Segmentation Based on AdaBoost Learning and Graph-Cuts,
ICIAR06(I: 215-225).
Springer DOI 0610
BibRef

Han, D.F.[Dong-Feng], Li, W.H.[Wen-Hui], Lu, X.S.[Xiao-Suo], Wang, Y.[Yi], Zou, X.Q.[Xiao-Qiang],
Certain Object Segmentation Based on AdaBoost Learning and Nodes Aggregation Iterative Graph-Cuts,
AMDO06(196-202).
Springer DOI 0607
BibRef

Beaulieu, J.,
Pseudo-convex Contour Criterion for Hierarchical Segmentation of SAR Images,
CRV06(29-29).
IEEE DOI 0607
BibRef

Awadallah, M.[Mahmoud], Abbott, A.L.[A. Lynn], Ghannam, S.[Sherin],
Segmentation of sparse noisy point clouds using active contour models,
ICIP14(6061-6065)
IEEE DOI 1502
Active contours BibRef

Lee, S.M.[Sang-Mook], Abbott, A.L.[A. Lynn], Araman, P.A.[Philip A.],
Segmentation on statistical manifold with watershed transform,
ICIP08(625-628).
IEEE DOI 0810
BibRef
Earlier:
Dimensionality Reduction and Clustering on Statistical Manifolds,
ComponentAnalysis07(1-7).
IEEE DOI 0706
BibRef

Lee, S.M.[Sang-Mook], Abbott, A.L., Clark, N.A., Araman, P.A.,
Diffusion on Statistical Manifolds,
ICIP06(233-236).
IEEE DOI 0610
BibRef
Earlier:
Active Contours on Statistical Manifolds And Texture Segmentation,
ICIP05(III: 828-831).
IEEE DOI 0512
BibRef

Jiang, C.Y.[Chun-Yan], Zhang, X.H.[Xin-Hua], Meinel, C.[Christoph],
Hybrid Framework for Medical Image Segmentation,
CAIP05(264).
Springer DOI 0509
BibRef

Gelautz, M.[Margrit], Markovic, D.[Danijela],
Recognition of Object Contours from Stereo Images: An Edge Combination Approach,
3DPVT04(774-780).
IEEE DOI 0412
Combine depth edges from stereo with intensity images from image. Then active contours to get precise extraction. BibRef

Bueno, G.[Gloria], Martínez-Albalá, A.[Antonio], Adán, A.[Antonio],
Fuzzy-Snake Segmentation of Anatomical Structures Applied to CT Images,
ICIAR04(II: 33-42).
Springer DOI 0409
BibRef

Tipwai, P., Madarasmi, S.,
Image search using deformable contours,
ICIP02(I: 437-440).
IEEE DOI 0210
BibRef

Gallo, G., Grasso, G., Nicotra, S., Pulvirenti, A.,
Remote sensed images segmentation through shape refinement,
CIAP01(137-144).
WWW Link. 0210
BibRef

Tan, K.H.[Kar-Han], Ahuja, N.[Narendra],
A Representation for Image Structure and Its Application to Object Selection Using Freehand Sketches,
CVPR01(II:677-683).
IEEE DOI 0110
Rough outline of the desired object. Select the good segmentation. BibRef

Fenster, S.D., Kuo, C.B.G., Kender, J.R.,
Nonparametric Training of Snakes to Find Indistinct Boundaries,
MMBIA01(xx-yy). 0110
BibRef

Ray, N., Havlicek, J.P., Acton, S.T., Pattichis, M.S.[Marios S.],
Active Contour Segmentation Guided by AM-FM Dominant Component Analysis,
ICIP01(I: 78-81).
IEEE DOI 0108
BibRef

Perez, P.[Patrick], Blake, A.[Andrew], Gangnet, M.[Michel],
JetStream: Probabilistic Contour Extraction with Particles,
ICCV01(II: 524-531).
IEEE DOI 0106
To do cutouts for picture editing or road extraction. BibRef

Tan, K.H.[Kar-Han], Ahuja, N.[Narendra],
Selecting Objects With Freehand Sketches,
ICCV01(I: 337-344).
IEEE DOI 0106
Select the rough object, then fit the exact area. BibRef

da Costa, J.P.[Jean Pierre], Germain, C.[Christian], Baylou, P.[Pierre],
Level Curve Tracking Algorithm for Textural Feature Extraction,
ICPR00(Vol III: 909-912).
IEEE DOI 0009
BibRef

Abe, T.[Toru], Matsuzawa, Y.[Yuki],
Active contour model using a priori knowledge of region shape,
ICIP03(III: 409-412).
IEEE DOI 0312
BibRef
Earlier:
Clustering-based control of active contour model,
ICPR02(II: 663-667).
IEEE DOI 0211
BibRef
Earlier:
A Region Extraction Method using Multiple Active Contour Models,
CVPR00(I: 64-69).
IEEE DOI 0005
BibRef
And:
Multiple Active Contour Models with Application to Region Extraction,
ICPR00(Vol I: 626-630).
IEEE DOI 0009
BibRef

Matsuzawa, Y., Kumazawa, I., Abe, T.,
Region Extraction Method Based on Clustering Along an Object Contour,
ICIP01(III: 86-89).
IEEE DOI 0108
BibRef

Matsuzawa, Y.[Yuki], Horiguchi, S.[Susumu], Abe, T.[Toru],
Region Extraction Using Competition of Multiple Active Contour Models,
ICIP99(III:198-202).
IEEE DOI BibRef 9900

Kuo, C.H.[Chung-Hui], Tewfik, A.H.[Ahmed H.],
Unsupervised Color Image Segmentation for Content-Based Application,
ICME00(WP5). 0007
BibRef
Earlier:
Multiscale Sigma Filter and Active Contour for Image Segmentation,
ICIP99(I:353-357).
IEEE DOI BibRef

Belongie, S.J.[Serge J.], Malik, J.,
Finding Boundaries in Natural Images: A New Method Using Point Descriptors and Area Completion,
ECCV98(I: 751).
Springer DOI
HTML Version. BibRef 9800

O'Donnell, T.[Thomas], Dubuisson-Jolly, M.P.[Marie-Pierre], and Gupta, A.[Alok],
A Cooperative Framework for Segmentation Using 2D Active Contours and 3D Hybrid Models as Applied to Branching Cylindrical Structures,
ICCV98(454-459).
IEEE DOI BibRef 9800

Leung, T., Malik, J.,
Contour continuity in region-based image segmentation,
ECCV98(I: 544).
Springer DOI
PS File. BibRef 9800

Zingaretti, P.[Primo], Carbonaro, A.[Antonella], Puliti, P.[Paolo],
Evolutionary image segmentation,
CIAP97(I: 247-254).
Springer DOI 9709
BibRef

Taylor, R.I., Lewis, P.H.,
Colour image segmentation using boundary relaxation,
ICPR92(III:721-724).
IEEE DOI 9208
BibRef

Taylor, R.I., Lewis, P.H.,
A Fractal Shape Signature,
BMVC91(xx-yy).
PDF File. 9109
BibRef

Darrell, T.J., Sclaroff, S., and Pentland, A.P.,
Segmentation by Minimal Description,
ICCV90(112-116).
IEEE DOI BibRef 9000

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Active Contours and Snakes, Video, Motion Segmentation Issues .


Last update:Dec 7, 2017 at 17:23:10