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.
Elsevier DOI 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 Award, ICCV Test of Time. 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.
Elsevier DOI 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

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.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI 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.[Jörg Hendrik], Schmidt, S.[Stefan], Schnörr, C.[Christoph],
A study of Nesterov's scheme for Lagrangian decomposition and MAP labeling,
CVPR11(1817-1823).
IEEE DOI 1106
BibRef
Earlier: A3, A1, A2, A4:
Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization,
EMMCVPR11(89-103).
Springer DOI 1107
BibRef
Earlier: A2, A3, A4, Only:
MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation,
ECCV10(III: 735-747).
Springer DOI 1009
BibRef
Earlier: A2, A4, Only:
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

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

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.
Elsevier DOI 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.
Elsevier DOI 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

Kalogeiton, V.[Vicky], Weinzaepfel, P., Ferrari, V.[Vittorio], Schmid, C.[Cordelia],
Joint Learning of Object and Action Detectors,
ICCV17(2001-2010)
IEEE DOI 1802
image motion analysis, learning (artificial intelligence), object detection, object recognition, action detection benefit, Videos BibRef

Bergo, F.P.G.[Felipe P.G.], Falcão, 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.], Falcão, 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.], Falcão, 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

Falcão, 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.], Falcão, 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

de Moraes Braz, C.[Caio], Miranda, P.A.V.[Paulo A.V.], Ciesielski, K.C.[Krzysztof Chris], Cappabianco, F.A.M.[Fábio A.M.],
Graph-Based Segmentation with Local Band Constraints,
DGCI19(155-166).
Springer DOI 1905
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

Demario, C.L., Miranda, P.A.V.,
Relaxed Oriented Image Foresting Transform for Seeded Image Segmentation,
ICIP19(1520-1524)
IEEE DOI 1910
Relaxed Segmentation, Oriented Image Foresting Transform, Random Walks 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 Venantâ€“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.[Solène], 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.[Solène], Le Guyader, C.[Carole],
A Non-local Topology-Preserving Segmentation-Guided Registration Model,
JMIV(59), No. 3, November 2017, pp. 432-455.
Springer DOI 1710
BibRef

Debroux, N.[Noémie], Le Guyader, C.[Carole],
A Joint Segmentation/Registration Model Based on a Nonlocal Characterization of Weighted Total Variation and Nonlocal Shape Descriptors,
SIIMS(11), No. 2, 2018, pp. 957-990.
DOI Link 1807
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, Helmholtz Prize. BibRef
And:
A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms,
PercOrg01(xx-yy). Dataset, Human Segmentation. BSDS300 DAtaset 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

Mayer, C.[Christoph], Timofte, R.[Radu], Paul, G.[Grégory],
Towards closing the gap in weakly supervised semantic segmentation with DCNNs: Combining local and global models,
CVIU(208-209), 2021, pp. 103209.
Elsevier DOI 2106
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

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

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.J.[Hui-Jie],
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.J.[Hui-Jie],
Object detection via foreground contour feature selection and part-based shape model,
ICPR12(2524-2527).
WWW Link. 1302
BibRef

Bova, N.[Nicola], Ibáñez, 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

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

Storathc1, M., Weinmann, A., Frikel, J., Unser, M.,
Joint image reconstruction and segmentation using the Potts model,
Inverse Problems(31), 2015, pp. 025003.
DOI Link Piecewise Affine-Linear Mumford-Shah Model (PALMS)
See also PALMS Image Partitioning: A New Parallel Algorithm for the Piecewise Affine-Linear Mumford-Shah Model. BibRef 1500

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

Sun, W.Y.[Wen-Yan], Dong, E.Q.[En-Qing], Qiao, H.J.[Hui-Jie],
A fuzzy energy-based active contour model with adaptive contrast constraint for local segmentation,
SIViP(12), No. 1, January 2018, pp. 91-98.
WWW Link. 1801
BibRef

Wang, Q.[Qi], Spratling, M.W.,
Contour detection refined by a sparse reconstruction-based discrimination method,
SIViP(12), No. 2, February 2018, pp. 207-214.
Springer DOI 1802
BibRef

Onal, S.[Sinan], Chen, X.[Xin], Balasooriya, M.M.[Madagedara Maduka],
Interior point search for nonparametric image segmentation,
SIViP(12), No. 2, February 2018, pp. 363-370.
Springer DOI 1802
BibRef

Subudhi, P.[Priyambada], Mukhopadhyay, S.[Susanta],
A novel texture segmentation method based on co-occurrence energy-driven parametric active contour model,
SIViP(12), No. 4, May 2018, pp. 669-676.
WWW Link. 1805
BibRef

Cai, Q.[Qing], Liu, H.Y.[Hui-Ying], Zhou, S.P.[San-Ping], Sun, J.F.[Jing-Feng], Li, J.[Jing],
An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation,
PR(82), 2018, pp. 79-93.
Elsevier DOI 1806
Active contour model, Image segmentation, Intensity inhomogeneous image, Adaptive scale operator, Bias field estimation BibRef

Nguyen, T.T.[Tuan T.], Dahl, V.A.[Vedrana A.], Bærentzen, J.A.[J. Andreas],
Multi-phase image segmentation with the adaptive deformable mesh,
PRL(117), 2019, pp. 97-103.
Elsevier DOI 1901
Deformable model, Active contour, Explicit mesh, Triangle mesh, Multi-phase, Adaptive mesh, Mumford-Shah BibRef

Han, B.[Bin], Wu, Y.Q.[Yi-Quan],
Active contours driven by global and local weighted signed pressure force for image segmentation,
PR(88), 2019, pp. 715-728.
Elsevier DOI 1901
Active contour, GWSPF, LWSPF, Global and local within-class variances BibRef

Ma, D.D.[Dong-Dong], Liao, Q.M.[Qing-Min], Chen, Z.Q.[Zi-Qin], Liao, R.[Ran], Ma, H.[Hui],
Adaptive local-fitting-based active contour model for medical image segmentation,
SP:IC(76), 2019, pp. 201-213.
Elsevier DOI 1906
Segmentation, Active contour, Adaptive local fitting, Medical images BibRef

Niu, Y.F.[Yue-Feng], Cao, J.Z.[Jian-Zhong],
Local difference-based active contour model for medical image segmentation and bias correction,
IET-IPR(13), No. 10, 22 August 2019, pp. 1755-1762.
DOI Link 1909
BibRef

Dong, B.[Bin], Jin, R.[Ri], Weng, G.R.[Gui-Rong],
Active contour model based on local bias field estimation for image segmentation,
SP:IC(78), 2019, pp. 187-199.
Elsevier DOI 1909
Active contour model, Image segmentation, Bias field estimation, Intensity inhomogeneous image, Fuzzy c-means BibRef

Roberts, M.[Michael], Spencer, J.[Jack],
Chan-Vese Reformulation for Selective Image Segmentation,
JMIV(61), No. 8, October 2019, pp. 1173-1196.
WWW Link. 1909
BibRef

Tan, L.[Lu], Li, L.[Ling], Liu, W.Q.[Wan-Quan], Sun, J.[Jie], Zhang, M.[Min],
A Novel Euler's Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm,
JMIV(62), No. 1, January 2020, pp. 98-119.
Springer DOI 2001
BibRef

Han, B.[Bin], Wu, Y.[Yiquan],
Active contour model for inhomogenous image segmentation based on Jeffreys divergence,
PR(107), 2020, pp. 107520.
Elsevier DOI 2008
Active contour model, Inhomogenous image segmentation, Local and global data fitting energies, Jeffreys divergence, Adaptive weight BibRef

Li, M.M., Li, B.Z.,
A Novel Active Contour Model for Noisy Image Segmentation Based on Adaptive Fractional Order Differentiation,
IP(29), 2020, pp. 9520-9531.
IEEE DOI 1806
Image segmentation, Computational modeling, Adaptation models, Active contours, Level set, Numerical models, Mathematical model, variational method BibRef

Xiao, L.[Ling], Wu, B.[Bo], Hu, Y.M.[You-Min],
OSED: Object-specific edge detection,
JVCIR(72), 2020, pp. 102918.
Elsevier DOI 2010
Region proposal, Edge detection, Deep supervision, Convolutional neural network BibRef

Wang, T.R.[Tian-Ren], Zhang, T.[Teng], Lovell, B.C.[Brian C.],
EBIT: Weakly-supervised image translation with edge and boundary enhancement,
PRL(138), 2020, pp. 534-539.
Elsevier DOI 2010
Weakly-supervised, GAN, Canny, Silhouette, Disentanglement BibRef

Biswas, S.[Soumen], Hazra, R.[Ranjay],
Active contours driven by modified LoG energy term and optimised penalty term for image segmentation,
IET-IPR(14), No. 13, November 2020, pp. 3232-3242.
DOI Link 2012
BibRef

Chen, D.[Da], Spencer, J.[Jack], Mirebeau, J.M.[Jean-Marie], Chen, K.[Ke], Shu, M.L.[Ming-Lei], Cohen, L.D.[Laurent D.],
A Generalized Asymmetric Dual-Front Model for Active Contours and Image Segmentation,
IP(30), 2021, pp. 5056-5071.
IEEE DOI 2106
Image segmentation, Active contours, Measurement, Mathematical model, Level set, Numerical models, fast marching method BibRef

Liu, L.[Li], Chen, D.[Da], Shu, M.[Minglei], Cohen, L.D.[Laurent D.],
Grouping Boundary Proposals for Fast Interactive Image Segmentation,
IP(33), 2024, pp. 793-808.
IEEE DOI 2402
Image segmentation, Image edge detection, Proposals, Adaptation models, Mathematical models, Trajectory, Eikonal equation BibRef

Liu, L.[Li], Wang, M.Z.[Ming-Zhu], Zhou, S.W.[Shu-Wang], Shu, M.[Minglei], Cohen, L.D.[Laurent D.], Chen, D.[Da],
Curvilinear Structure Tracking Based on Dynamic Curvature-penalized Geodesics,
PR(134), 2023, pp. 109079.
Elsevier DOI 2212
Curvature-penalized geodesics, Local bending constraint, Coherence penalization, Curvilinear structures, Retinal vessels BibRef

Dong, Z.H.[Zi-Hao], Li, J.P.[Jin-Ping], Fang, T.[Tiyu], Shao, X.L.[Xiu-Li],
Lightweight boundary refinement module based on point supervision for semantic segmentation,
IVC(110), 2021, pp. 104169.
Elsevier DOI 2106
Semantic segmentation, Boundary refinement, Point supervision, Point convolution, Direction field BibRef

Liu, H.X.[Hua-Xiang], Fang, J.X.[Jiang-Xiong], Zhang, Z.J.[Zi-Jian], Lin, Y.C.[Yong-Cheng],
Localised edge-region-based active contour for medical image segmentation,
IET-IPR(15), No. 7, 2021, pp. 1567-1582.
DOI Link 2106
BibRef

Liu, H.X.[Hua-Xiang], Fu, Y.Y.[You-Yao], Zhang, S.Q.[Shi-Qing], Liu, J.[Jun], Fang, J.X.[Jiang-Xiong],
Active contour driven by adaptive-scale local-energy signed pressure force function based on bias correction for medical image segmentation,
IET-IPR(16), No. 14, 2022, pp. 3929-3947.
DOI Link 2212
BibRef

Lei, Y.[Yu], Weng, G.R.[Gui-Rong],
A Robust Hybrid Active Contour Model Based on Pre-Fitting Bias Field Correction for Fast Image Segmentation,
SP:IC(97), 2021, pp. 116351.
Elsevier DOI 2107
Active contour model, Bias field, Adaptive edge indicator function, Intensity inhomogeneity, Image segmentation BibRef

Ge, P.Q.[Peng-Qiang], Chen, Y.Y.[Yi-Yang], Wang, G.[Guina], Weng, G.R.[Gui-Rong],
A hybrid active contour model based on pre-fitting energy and adaptive functions for fast image segmentation,
PRL(158), 2022, pp. 71-79.
Elsevier DOI 2205
Active contour models, Pre-fitting function, Level set method, Adaptive functions BibRef

Kim, N.[Namgil], Kang, B.[Barom], Cho, Y.[Yeonok],
Split-GCN: Effective Interactive Annotation for Segmentation of Disconnected Instance,
PAMI(45), No. 7, July 2023, pp. 9256-9263.
IEEE DOI 2306
Initial polygon, but allow disconnected results. Feature extraction, Topology, Annotations, Shape, Predictive models, Level set, Decoding, human interactive learning, segmentation, semi-auto labeling BibRef

Budd, J.M.[Jeremy M.], van Gennip, Y.[Yves], Latz, J.[Jonas], Parisotto, S.[Simone], Schonlieb, C.B.[Carola-Bibiane],
Joint Reconstruction-Segmentation on Graphs,
SIIMS(16), No. 2, 2023, pp. 911-947.
DOI Link 2306
BibRef

Du, T.X.[Tong-Xin], Fang, B.[Bin], Zhou, M.L.[Ming-Liang], Zhao, H.J.[Hen-Jun], Xian, W.Z.[Wei-Zhi], Wu, X.G.[Xue-Gang],
Segmentation Algorithm of the Valid Region in Fisheye Images Using Edge and Region Information,
ICIP20(468-472)
IEEE DOI 2011
Image segmentation, Linear programming, Image edge detection, Level set, Resists, Mathematical model, Detectors, Fisheye image, halation noise BibRef

Wang, J.Q.[Jia-Qi], Zhang, W.W.[Wen-Wei], Cao, Y.H.[Yu-Hang], Chen, K.[Kai], Pang, J.M.[Jiang-Miao], Gong, T.[Tao], Shi, J.P.[Jian-Ping], Loy, C.C.[Chen Change], Lin, D.H.[Da-Hua],
Side-aware Boundary Localization for More Precise Object Detection,
ECCV20(IV:403-419).
Springer DOI 2011
BibRef

Yuan, Y.H.[Yu-Hui], Xie, J.Y.[Jing-Yi], Chen, X.L.[Xi-Lin], Wang, J.D.[Jing-Dong],
Segfix: Model-agnostic Boundary Refinement for Segmentation,
ECCV20(XII: 489-506).
Springer DOI 2010
BibRef

Lu, R.[Rui], Xue, F.[Feng], Zhou, M.H.[Meng-Han], Ming, A.L.[An-Long], Zhou, Y.[Yu],
Occlusion-Shared and Feature-Separated Network for Occlusion Relationship Reasoning,
ICCV19(10342-10351)
IEEE DOI 2004
convolutional neural nets, edge detection, feature extraction, learning (artificial intelligence), occlusion orientation, BibRef

Ni, T.W.[Tian-Wei], Xie, L.X.[Ling-Xi], Zheng, H.J.[Huang-Jie], Fishman, E.K.[Elliot K.], Yuille, A.L.[Alan L.],
Elastic Boundary Projection for 3D Medical Image Segmentation,
CVPR19(2104-2113).
IEEE DOI 2002
BibRef

Kelm, A.P.[André Peter], Rao, V.S.[Vijesh Soorya], Zölzer, U.[Udo],
Object Contour and Edge Detection with RefineContourNet,
CAIP19(I:246-258).
Springer DOI 1909
BibRef

Antunes, D.[Daniel], Lachaud, J.O.[Jacques-Olivier], Talbot, H.[Hugues],
Digital Curvature Evolution Model for Image Segmentation,
DGCI19(15-26).
Springer DOI 1905
BibRef

Bougrine, A., Harba, R., Canals, R., Ledee, R., Jabloun, M.,
A joint snake and atlas-based segmentation of plantar foot thermal images,
IPTA17(1-6)
IEEE DOI 1804
image segmentation, mean square error methods, DSC, RMSE, Snake curve, Snake energy function, atlas-based segmentation, Plantar foot thermal images BibRef

Tremblay, M.[Maxime], Zaccarin, A.[André],
Learning to segment on tiny datasets: A new shape model,
ICIP17(2384-2388)
IEEE DOI 1803
Computational modeling, Feature extraction, Histograms, Image segmentation, Object segmentation, Shape, Training, tiny data set BibRef

Yang, C.,
Semantic boundary refinement by joint inference from edges and regions,
ICIP17(3105-3109)
IEEE DOI 1803
Detectors, Image edge detection, Image segmentation, Pipelines, Semantics, Task analysis, Tools BibRef

Xu, W., Yue, X., Chen, Y., Reformat, M.,
Ensemble of active contour based image segmentation,
ICIP17(86-90)
IEEE DOI 1803
Active contours, Dictionaries, Image segmentation, Level set, Mutual information, Probabilistic logic, segmentation ensemble 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. Context, Databases, Detectors, Image edge detection, Image segmentation, Semantics Related to:
See also PASCAL Visual Object Classes Challenge 2012, The. BibRef