8.7.1.7 Snakes, Algorithms for Computation

Chapter Contents (Back)
Deformable Curves. Snakes. Active Contours. Curve Evolution.

Mishra, A.K.[Ajay K.], Aloimonos, Y.[Yiannis], Cheong, L.F.[Loong Fah],
Code: Active Segmentation With Fixation,
Online2010. Code, Segmentation. Code, Snakes.
HTML Version. Code for ICCV 2009 paper.
See also Active Segmentation with Fixation. BibRef 1000

Amini, A.A., Weymouth, T.E., and Jain, R.C.,
Using Dynamic Programming for Solving Variational Problems in Vision,
PAMI(12), No. 9, September 1990, pp. 855-867.
IEEE DOI Dynamic Programming. Variational Problems. Discussed as a general technique for problems such as surface fitting and the like. Here it is applied to contours. BibRef 9009

Amini, A.A., Tehrani, S., and Weymouth, T.E.,
Using Dynamic Programming for Minimizing the Energy of Active Contours in the Presence of Hard Constraints,
ICCV88(95-99).
IEEE DOI An early related paper to the above journal paper. BibRef 8800

Williams, D.J.[Donna J.], and Shah, M.,
A Fast Algorithm for Active Contours and Curvature Estimation,
CVGIP(55), No. 1, January 1992, pp. 14-26.
Elsevier DOI BibRef 9201
Earlier:
A Fast Algorithm for Active Contours,
ICCV90(592-595).
IEEE DOI An analysis of Kass and Amini and a proposed implementation that is faster, O(nm), that dynamic programming approaches. BibRef

Goshtasby, A., O'Neill, W.D.,
Curve-Fitting by a Sum of Gaussians,
GMIP(56), No. 4, July 1994, pp. 281-288. BibRef 9407

Goshtasby, A.[Ardeshir], Shyu, H.L.[Hai-Lun],
Edge-Detection by Curve-Fitting,
IVC(13), No. 3, April 1995, pp. 169-177.
Elsevier DOI elongated regions. BibRef 9504

Sander, P.T.,
Estimating Curvature by Kalman Filters,
VF91(469-477). Estimating the curvature of a collection of points (not really snakes since the grouping is not given). BibRef 9100

Chiou, G.I., Hwang, J.N.[Jenq-Neng],
A neural network-based stochastic active contour model (NNS-SNAKE) for contour finding of distinct features,
IP(4), No. 10, October 1995, pp. 1407-1416.
IEEE DOI 0402
BibRef

Kimmel, R., Kiryati, N., Bruckstein, A.M.,
Analyzing and Synthesizing Images by Evolving Curves with the Osher-Sethian Method,
IJCV(24), No. 1, August 1997, pp. 37-55.
DOI Link 9709

See also Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision and Materials Science.
See also Geometric Level Set Methods in Imaging, Vision, and Graphics. BibRef

Kimmel, R.[Ron],
Numerical Geometry of Images: Theory, Algorithms, and Applications,
Springer2005. ISBN: 0-387-95562-3 Buy this book: Numerical Geometry of Images: Theory, Algorithms, and Applications BibRef 0500

Bruckstein, A.M.,
Analyzing and synthesizing images by evolving curves,
ICIP94(I: 11-15).
IEEE DOI 9411
BibRef

Cohen, L.D.[Laurent D.], Kimmel, R.[Ron],
Global Minimum for Active Contour Models: A Minimal Path Approach,
IJCV(24), No. 1, August 1997, pp. 57-78.
DOI Link 9709
BibRef
And:
Global Minimum for Active Contour Models: A Minimum Path Approach,
CVPR96(666-673).
IEEE DOI
PS File. BibRef

Chen, D.[Da], Mirebeau, J.M.[Jean-Marie], Cohen, L.D.[Laurent D.],
Global Minimum for a Finsler Elastica Minimal Path Approach,
IJCV(122), No. 3, May 2017, pp. 458-483.
Springer DOI 1704
BibRef

Cohen, L.D.[Laurent D.], Kimmel, R.[Ron],
Edge Integration Using Minimal Geodesics,
TRCeremade, Universite Paris Dauphine, January 1995.
PS File. BibRef 9501
And: TR9612, Ceremade, Universite Paris Dauphine.
PS File. BibRef

Cohen, L.D.[Laurent D.], Kimmel, R.[Ron],
Fast Marching the Global Minimum of Active Contours,
ICIP96(I: 473-476).
IEEE DOI BibRef 9600

Ghorpade, V.K.[Vijaya K.], Cohen, L.D.[Laurent D.],
Automatic image segmentation with Anisotropic Fast Marching algorithm and geodesic voting,
ICIP15(3009-3013)
IEEE DOI 1512
Anisotropic Fast Marching algorithm BibRef

Eviatar, H., Somorjai, R.L.,
A Fast, Simple Active Contour Algorithm for Biomedical Images,
PRL(17), No. 9, August 1 1996, pp. 969-974. 9609
BibRef

Gunn, S.R.[Steve R.], Nixon, M.S.[Mark S.],
Robust Snake Implementation: A Dual Active Contour,
PAMI(19), No. 1, January 1997, pp. 63-68.
IEEE DOI 9702
BibRef
Earlier:
Improving snake performance via a dual active contour,
CAIP95(600-605).
Springer DOI 9509
BibRef
Earlier:
A Model Based Dual Active Contour,
BMVC94(xx-yy).
PDF File. 9409
Use one contour expanding from inside, and a second contracting from outside. They are interlinked to avoid weak local minima. BibRef

Gunn, S.R.[Steve R.], Nixon, M.S.[Mark S.],
Global and Local Active Contours for Head Boundary Extraction,
IJCV(30), No. 1, October 1998, pp. 43-54.
DOI Link BibRef 9810
Earlier:
Snake Head Boundary Extraction Using Global and Local Energy Minimisation,
ICPR96(II: 581-585).
IEEE DOI 9608
(Univ. of Southampton, UK) BibRef

Dharmagunawardhana, C.[Chathurika], Mahmoodi, S.[Sasan], Bennet, M.[Michael], Niranjan, M.[Mahesan],
Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters,
BMVC12(88).
DOI Link 1301
BibRef

Dharmagunawardhana, C.[Chathurika], Mahmoodi, S.[Sasan], Bennett, M.[Michael], Niranjan, M.[Mahesan],
Gaussian Markov random field based improved texture descriptor for image segmentation,
IVC(32), No. 11, 2014, pp. 884-895.
Elsevier DOI 1410
Gaussian Markov random field BibRef

Dharmagunawardhana, C.[Chathurika], Mahmoodi, S.[Sasan], Bennett, M.[Michael], Niranjan, M.[Mahesan],
Rotation invariant texture descriptors based on Gaussian Markov random fields for classification,
PRL(69), No. 1, 2016, pp. 15-21.
Elsevier DOI 1601
Gaussian-Markov random field BibRef

Almakady, Y.[Yasseen], Mahmoodi, S.[Sasan], Conway, J.[Joy], Bennett, M.[Michael],
Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification,
CVIU(194), 2020, pp. 102931.
Elsevier DOI 2005
Lungs. BibRef
And: A1, A2, A4, Only: ICIP20(340-344)
IEEE DOI 2011
COPD, 3D-GMRF, Volumetric texture, Classification. Feature extraction, Biomedical imaging, Mathematical model, Diseases, Lung, Histograms,
See also Texture-Based Region Tracking Using Gaussian Markov Random Fields for Cilia Motion Analysis. BibRef

Almakady, Y.[Yasseen], Mahmoodi, S.[Sasan], Bennett, M.[Michael],
Adaptive volumetric texture segmentation based on Gaussian Markov random fields features,
PRL(140), 2020, pp. 101-108.
Elsevier DOI 2012
BibRef

Mahmoodi, S.[Sasan], Gunn, S.R.[Steve R.],
Snake based unsupervised texture segmentation using Gaussian Markov Random Field Models,
ICIP11(3353-3356).
IEEE DOI 1201
BibRef

Chandran, S.[Sharat], Potty, A.K.,
Energy Minimization of Contours Using Boundary Conditions,
PAMI(20), No. 5, May 1998, pp. 546-549.
IEEE DOI 9806
A dynamic programming solution for snakes designed to avoid local minima. BibRef

Wong, Y.Y., Yuen, P.C., Tong, C.S.,
Contour Length Terminating Criterion for Snake Model,
PR(31), No. 5, May 1998, pp. 597-606.
Elsevier DOI 9805
BibRef

Ma, T., Tagare, H.D.,
Consistency and Stability of Active Contours with Euclidean and Non-Euclidean Arc Lengths,
IP(8), No. 11, November 1999, pp. 1549-1559.
IEEE DOI 9911
BibRef

Chen, Y.M.[Yun-Mei], Tagare, H.D.[Hemant D.], Thiruvenkadam, S.R.[Sheshadri R.], Huang, F.[Feng], Wilson, D.[David], Gopinath, K.S.[Kaundinya S.], Briggs, R.W.[Richard W.], Geiser, E.A.[Edward A.],
Using Prior Shapes in Geometric Active Contours in a Variational Framework,
IJCV(50), No. 3, December 2002, pp. 315-328.
DOI Link 0211
BibRef

Chen, Y., Thiruvenkadam, S.R.[Sheshadri R.], Tagare, H.D., Huang, F.[Feng], Wilson, D.,
On the Incorporation of Shape Priors into Geometric Active Contours,
LevelSet01(xx-yy). 0106
BibRef

Thiruvenkadam, S.R.[Sheshadri R.], Chan, T.F.[Tony F.], Hong, B.W.[Byung-Woo],
Segmentation Under Occlusions Using Selective Shape Prior,
SIIMS(1), No. 1, 2008, pp. 115-142. image segmentation; variational methods; level set methods
DOI Link BibRef 0800
Earlier: SSVM07(191-202).
Springer DOI 0705
BibRef

Chen, Y.M.[Yun-Mei], Huang, F.[Feng], Tagare, H.D.[Hemant D.], Rao, M.[Murali],
A Coupled Minimization Problem for Medical Image Segmentation with Priors,
IJCV(71), No. 3, March 2007, pp. 259-272.
Springer DOI 0001
BibRef

Yue, Y.[Yong], Tagare, H.D.[Hemant D.],
Learning to segment using machine-learned penalized logistic models,
MMBIA09(58-65).
IEEE DOI 0906
BibRef

Chen, Y.M.[Yun-Mei], Huang, F.[Feng], Tagare, H.D., Rao, M.[Murali], Wilson, D., Geiser, E.A.,
Using prior shape and intensity profile in medical image segmentation,
ICCV03(1117-1124).
IEEE DOI 0311
BibRef

Chan, T.F.[Tony F.], Sandberg, B.Y.[B. Yezrielev], Vese, L.A.[Luminita A.],
Active Contours without Edges for Vector-Valued Images,
JVCIR(11), No. 2, June 2000, pp. 130-141. 0008

See also Active contours without edges. BibRef

Sandberg, B.Y.[Berta Yezrielev], Chan, T.F.[Tony F.],
A logic framework for active contours on multi-channel images,
JVCIR(16), No. 3, June 2005, pp. 333-358.
Elsevier DOI 0711
Multi-channel; Segmentation; Logic operations; Active contours BibRef

Sandberg, B.Y.[B. Yezrielev], Kang, S.H., Chan, T.F.[Tony F.],
Unsupervised Multiphase Segmentation: A Phase Balancing Model,
IP(19), No. 1, January 2010, pp. 119-130.
IEEE DOI 1001
BibRef

Chan, T.F.[Tony F.], Vese, L.A.[Luminita A.],
Active contours without edges,
IP(10), No. 2, February 2001, pp. 266-277.
IEEE DOI 0001
BibRef
Earlier:
An active contour model without edges,
ScaleSpace99(141-151).
See also Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model, A.
See also variational method in image recovery, A. Implementation:
See also Chan-Vese Segmentation.
See also PALMS Image Partitioning: A New Parallel Algorithm for the Piecewise Affine-Linear Mumford-Shah Model. BibRef

Wu, H.H., Liu, J.C., Chui, C.,
A Wavelet-Frame Based Image Force Model for Active Contouring Algorithms,
IP(9), No. 11, November 2000, pp. 1983-1988.
IEEE DOI 0011
BibRef

Liu, J.C., Hwang, W.L., Chen, M.S., Tsai, J.W., Lin, C.H.,
Wavelet-based Active Contour Model for Object Tracking,
ICIP01(III: 206-209).
IEEE DOI 0108
BibRef

Ray, N.[Nilanjan], Chanda, B.[Bhabatosh], Das, J.[Jyotirmay],
A fast and flexible multiresolution snake with a definite termination criterion,
PR(34), No. 7, July 2001, pp. 1483-1490.
Elsevier DOI 0105
BibRef

Han, C.[Chao], Hatsukami, T.S., Hwang, J.N.[Jenq-Neng], Yuan, C.[Chun],
A fast minimal path active contour model,
IP(10), No. 6, June 2001, pp. 865-873.
IEEE DOI 0106
BibRef

Castrillón, M.[Marco], Valdés, A.[Antonio],
Projective Evolution of Plane Curves,
IJCV(42), No. 3, May-June 2001, pp. 191-201.
DOI Link 0108
Projective invariant evolution operators have singularities. BibRef

Delingette, H., Montagnat, J.,
Shape and Topology Constraints on Parametric Active Contours,
CVIU(83), No. 2, August 2001, pp. 140-171.
DOI Link 0109
BibRef
Earlier:
New Algorithms for Controlling Active Contours Shape and Topology,
ECCV00(II: 381-395).
Springer DOI 0003
Combine advantages of early Lagrangian formulation with the lat Eulerian framework for parametric active contours. Control of contour topology (create or fuse components on closed or open contours), geometry (contour resolution, number of verticies) and deformation (by vertex spacing and smoothness). BibRef

Park, J.[Jaesang], Keller, J.M.[James M.],
Snakes on the Watershed,
PAMI(23), No. 10, October 2001, pp. 1201-1205.
IEEE DOI 0110
Combine watershed approach and snakes in a two-step snake algorithm. BibRef

Kulkarni, S.[Subhash], Chatterji, B.N.,
Accurate shape modeling with front propagation using adaptive level sets,
PRL(23), No. 13, November 2002, pp. 1559-1568.
Elsevier DOI 0206
steering function derived from histogram features. BibRef

Ghebreab, S., Smeulders, A.W.M., Pfluger, P.R.,
Necklaces: Inhomogeneous and Point-Enhanced Deformable Models,
CVIU(86), No. 2, May 2002, pp. 96-117.
DOI Link 0301
Boundary features. BibRef

Ghebreab, S.[Sennay], Smeulders, A.W.M.[Arnold W.M.],
Strings: Variational deformable models of multivariate continuous boundary features,
PAMI(25), No. 11, November 2003, pp. 1399-1410.
IEEE Abstract. 0311
A variational deformable model learnd from a collection of examples rather than analytical knowledge. BibRef

Keshet, R.[Renato], Heijmans, H.J.A.M.[Henk J.A.M.],
Adjunctions in Pyramids, Curve Evolution and Scale-Spaces,
IJCV(52), No. 2-3, May-June 2003, pp. 139-151.
DOI Link 0301
BibRef
Earlier:
Adjunctions in pyramids and curve evolution,
ScaleSpace01(xx-yy). 0106
BibRef

Bredno, J.[Jorg], Lehmann, T.M.[Thomas M.], Spitzer, K.[Klaus],
A General Discrete Contour Model in Two, Three, and Four Dimensions for Topology-Adaptive Multichannel Segmentation,
PAMI(25), No. 5, May 2003, pp. 550-563.
IEEE Abstract. 0304
Representation using simplex meshes. BibRef

Srinark, T.[Thitiwan], Kambhamettu, C.[Chandra],
A framework for multiple snakes and its applications,
PR(39), No. 9, September 2006, pp. 1555-1565.
Elsevier DOI 0606
BibRef
Earlier:
A Framework for Multiple Snakes,
CVPR01(II:202-209).
IEEE DOI 0110
Multiple snakes; Multiple-object segmentation. Group energy to handle energy across multiple snakes. BibRef

Lam, S.Y., Tong, C.S.,
Enhanced Snake algorithm by embedded domain transformation,
PR(39), No. 9, September 2006, pp. 1566-1574.
Elsevier DOI 0606
Domain transformation; Conformal mapping; Robust contour detection BibRef

Seghers, D., Loeckx, D.[Dirk], Maes, F.[Frederik], Vandermeulen, D., Suetens, P.[Paul],
Minimal Shape and Intensity Cost Path Segmentation,
MedImg(26), No. 8, August 2007, pp. 1115-1129.
IEEE DOI 0709
Trained like active shape models, but simultaneous solution. BibRef

Keustermans, J.[Johannes], Smeets, D.[Dirk], Vandermeulen, D.[Dirk], Suetens, P.[Paul],
Automated Cephalometric Landmark Localization Using Sparse Shape and Appearance Models,
MLMI11(249-256).
Springer DOI 1109
BibRef

Keustermans, J.[Johannes], Mollemans, W.[Wouter], Vandermeulen, D.[Dirk], Suetens, P.[Paul],
Automated Cephalometric Landmark Identification Using Shape and Local Appearance Models,
ICPR10(2464-2467).
IEEE DOI 1008
BibRef

Keustermans, J.[Johannes], Seghers, D.[Dieter], Mollemans, W.[Wouter], Vandermeulen, D.[Dirk], Suetens, P.[Paul],
Image Segmentation Using Graph Representations and Local Appearance and Shape Models,
GbRPR09(353-365).
Springer DOI 0905
BibRef

Maalouf, A.[Aldo], Carre, P.[Philippe], Augereau, B.[Bertrand], Fernandez-Maloigne, C.[Christine],
Cooperation of the partial differential equation methods and the wavelet transform for the segmentation of multivalued images,
SP:IC(23), No. 1, January 2008, pp. 14-30.
Elsevier DOI 0801
BibRef
Earlier:
Foveal Wavelet-Based Color Active Contour,
ICIP07(I: 245-248).
IEEE DOI 0709
Partial differential equations; Wavelet; Segmentation; Color images BibRef

Carré, P.[Philippe], Denis, P.[Patrice], Fernandez-Maloigne, C.[Christine],
Spatial color image processing using Clifford algebras: Application to color active contour,
SIViP(8), No. 7, October 2014, pp. 1357-1372.
Springer DOI 1410
BibRef

Thevenaz, P.[Philippe], Unser, M.,
Snakuscules,
IP(17), No. 4, April 2008, pp. 585-593.
IEEE DOI 0803
BibRef
Earlier:
The Snakuscule,
ICIP06(1633-1636).
IEEE DOI 0610
BibRef

Thevenaz, P.[Philippe], Delgado-Gonzalo, R.[Ricard], Unser, M.[Michael],
The Ovuscule,
PAMI(33), No. 2, February 2011, pp. 382-393.
IEEE DOI 1101
Snake in shape of ellipse. BibRef

Delgado-Gonzalo, R., Thevenaz, P., Seelamantula, C.S., Unser, M.,
Snakes With an Ellipse-Reproducing Property,
IP(21), No. 3, March 2012, pp. 1258-1271.
IEEE DOI 1203
BibRef

Delgado-Gonzalo, R.[Ricard], Uhlmann, V., Schmitter, D., Unser, M.[Michael],
Snakes on a Plane: A perfect snap for bioimage analysis,
SPMag(32), No. 1, January 2015, pp. 41-48.
IEEE DOI 1502
biomedical optical imaging BibRef

Delgado-Gonzalo, R., Schmitter, D., Uhlmann, V., Unser, M.,
Efficient Shape Priors for Spline-Based Snakes,
IP(24), No. 11, November 2015, pp. 3915-3926.
IEEE DOI 1509
affine transforms
See also Trigonometric Interpolation Kernel to Construct Deformable Shapes for User-Interactive Applications. BibRef

Badoual, A., Schmitter, D., Uhlmann, V., Unser, M.,
Multiresolution Subdivision Snakes,
IP(26), No. 3, March 2017, pp. 1188-1201.
IEEE DOI 1703
image resolution BibRef

Uhlmann, V., Fageot, J., Unser, M.,
Hermite Snakes With Control of Tangents,
IP(25), No. 6, June 2016, pp. 2803-2816.
IEEE DOI 1605
Active contours BibRef

Charmi, M.A., Derrode, S., Ghorbel, F.,
Fourier-based geometric shape prior for snakes,
PRL(29), No. 7, 1 May 2008, pp. 897-904.
Elsevier DOI 0804
Snakes; Shape prior; Fourier transform; Invariant; Completeness; Object tracking BibRef

Corso, J.J.[Jason J.], Hager, G.D.[Gregory D.],
Image description with features that summarize,
CVIU(113), No. 4, April 2009, pp. 446-458.
Elsevier DOI 0903
BibRef
Earlier:
Coherent Regions for Concise and Stable Image Description,
CVPR05(II: 184-190).
IEEE DOI 0507
Image matching; Segmentation; Interest point operator; Feature space; Feature detector Coherent regions are best for matching. BibRef

Corso, J.J., Dewan, M., Hager, G.D.,
Image segmentation through energy minimization based subspace fusion,
ICPR04(II: 120-123).
IEEE DOI 0409
BibRef

Chen, A.Y.C.[Albert Y. C.], Corso, J.J.[Jason J.], Wang, L.[Le],
HOPS: Efficient region labeling using Higher Order Proxy Neighborhoods,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Corso, J.J.[Jason J.],
Toward parts-based scene understanding with pixel-support parts-sparse pictorial structures,
PRL(34), No. 7, 1 May 2013, pp. 762-769.
Elsevier DOI 1303
Scene understanding; Pictorial structures; Image segmentation; Object recognition; Stochastic optimization BibRef

Chen, C.[Cheng], Corso, J.J.[Jason J.],
Joint occlusion boundary detection and figure/ground assignment by extracting common-fate fragments in a back-projection scheme,
PR(64), No. 1, 2017, pp. 15-28.
Elsevier DOI 1701
BibRef

Duan, Q.[Qi], Angelini, E.D.[Elsa D.], Laine, A.F.[Andrew F.],
Surface Function Actives,
JVCIR(20), No. 7, October 2009, pp. 478-490.
Elsevier DOI 0909
Surface Function Actives; Image segmentation; Deformable model; Real-time segmentation; Variational approach; Interface representation Reduce dimensionality of computation to solve deformable models. BibRef

Rumpf, M.[Martin], Wirth, B.[Benedikt],
A Nonlinear Elastic Shape Averaging Approach,
SIIMS(2), No. 3, 2009, pp. 800-833. shape averaging; nonrigid registration; nonlinear elasticity; MumfordShah approach; phase field approximation; finite element discretization
DOI Link BibRef 0900
Earlier:
An Elasticity Approach to Principal Modes of Shape Variation,
SSVM09(709-720).
Springer DOI 0906
BibRef

Rumpf, M.[Martin], Wirth, B.[Benedikt],
An Elasticity-Based Covariance Analysis of Shapes,
IJCV(92), No. 3, May 2011, pp. 281-295.
WWW Link. 1103
BibRef

Wirth, B.[Benedikt], Bar, L.[Leah], Rumpf, M.[Martin], Sapiro, G.[Guillermo],
A Continuum Mechanical Approach to Geodesics in Shape Space,
IJCV(93), No. 3, July 2011, pp. 293-318.
WWW Link. 1104
BibRef
Earlier:
Geodesics in Shape Space via Variational Time Discretization,
EMMCVPR09(288-302).
Springer DOI 0908
BibRef

Rumpf, M.[Martin], Wirth, B.[Benedikt],
Discrete Geodesic Calculus in Shape Space and Applications in the Space of Viscous Fluidic Objects,
SIIMS(6), No. 4, 2013, pp. 2581-2602.
DOI Link 1402
BibRef

Berkels, B.[Benjamin], Linkmann, G.[Gina], Rumpf, M.[Martin],
An SL(2) Invariant Shape Median,
JMIV(37), No. 2, June 2010, pp. xx-yy.
Springer DOI 1003
Median of shapes, to get an average shape. BibRef

Binczak, S., Sliwa, T., Jacquir, S., Bilbault, J.M.,
Reaction-diffusion network for geometric multiscale high speed image processing,
IVC(28), No. 6, June 2010, pp. 914-926.
Elsevier DOI 1003
Image analysis; Multiscale geometry; Nonlinear signal processing Implementing active contour computations. BibRef

Mishra, A.K.[Akshaya K.], Fieguth, P.W.[Paul W.], Clausi, D.A.[David A.],
Decoupled Active Contour (DAC) for Boundary Detection,
PAMI(33), No. 2, February 2011, pp. 310-324.
IEEE DOI 1101
BibRef
Earlier:
Robust snake convergence based on dynamic programming,
ICIP08(1092-1095).
IEEE DOI 0810
BibRef
And:
Accurate Boundary Localization using Dynamic Programming on Snakes,
CRV08(261-268).
IEEE DOI 0805
decouple the internal/external energy terms.
See also Decoupled Active Surface for Volumetric Image Segmentation. BibRef

Mishra, A.K.[Akshaya K.], Wong, A.[Alexander], Clausi, D.A.[David A.], Fieguth, P.W.[Paul W.],
A Bayesian Information Flow Approach to Image Segmentation,
CRV10(301-308).
IEEE DOI 1005

See also Decoupled Active Surface for Volumetric Image Segmentation.
See also Adaptive Nonlinear Image Denoising and Restoration Using a Cooperative Bayesian Estimation Approach. BibRef

Wong, A.,
A Bayesian Theoretic Approach to Multiscale Complex-Phase-Order Representations,
IP(21), No. 1, January 2012, pp. 28-40.
IEEE DOI 1112
BibRef

Mishra, A.K.[Ajay K.], Aloimonos, Y.[Yiannis], Cheong, L.F.[Loong Fah], Kassim, A.A.[Ashraf A.],
Active Visual Segmentation,
PAMI(34), No. 4, April 2012, pp. 639-653.
IEEE DOI 1203
BibRef
Earlier: A1, A2, A3, Only:
Active Segmentation with Fixation,
ICCV09(468-477).
IEEE DOI 0909

See also Code: Active Segmentation With Fixation. visual attention. Segmentation based on attention. BibRef

Alvarez, L.[Luis], Baumela, L.[Luis], Márquez-Neila, P.[Pablo], Henríquez, P.[Pedro],
A Real Time Morphological Snakes Algorithm,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link 1204
Code, Snakes. BibRef

Marquez-Neila, P.[Pablo], Baumela, L.[Luis], Alvarez, L.[Luis],
A Morphological Approach to Curvature-Based Evolution of Curves and Surfaces,
PAMI(36), No. 1, 2014, pp. 2-17.
IEEE DOI 1312
Computer vision BibRef

Alvarez, L.[Luis], Baumela, L.[Luis], Henriquez, P.[Pedro], Marquez-Neila, P.[Pablo],
Morphological snakes,
CVPR10(2197-2202).
IEEE DOI 1006
Morphological operations for snake model.
See also Real Time Morphological Snakes Algorithm, A. BibRef

Márquez-Neila, P.[Pablo],
Higher-order regularization and morphological techniques for image segmentation,
ELCVIA(14), No. 3, 2015, pp. xx-yy.
DOI Link 1601
Thesis summary. BibRef

Brown, E.S.[Ethan S.], Chan, T.F.[Tony F.], Bresson, X.[Xavier],
Completely Convex Formulation of the Chan-Vese Image Segmentation Model,
IJCV(98), No. 1, May 2012, pp. 103-121.
WWW Link. 1204

See also Active contours without edges. BibRef

Getreuer, P.[Pascal],
Chan-Vese Segmentation,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link 1211

See also Active contours without edges. BibRef

Papalazarou, C.[Chrysi], de With, P.H.N.[Peter H.N.], Rongen, P.M.J.[Peter M.J.],
Sparse-plus-dense-RANSAC for estimation of multiple complex curvilinear models in 2D and 3D,
PR(46), No. 3, March 2013, pp. 925-935.
Elsevier DOI 1212
Model estimation; Curve detection; RANSAC; Medical imaging BibRef

Mosinska, A.[Agata], Marquez-Neila, P.[Pablo], Kozinski, M.[Mateusz], Fua, P.[Pascal],
Beyond the Pixel-Wise Loss for Topology-Aware Delineation,
CVPR18(3136-3145)
IEEE DOI 1812
Image segmentation, Topology, Feature extraction, Computer architecture, Roads, Network topology, Standards BibRef

Mosinska-Domanska, A.[Agata], Sznitman, R.[Raphael], Glowacki, P.[Przemyslaw], Fua, P.[Pascal],
Active Learning for Delineation of Curvilinear Structures,
CVPR16(5231-5239)
IEEE DOI 1612
BibRef