8.7.1 Active Contours, Snakes or Deformable Curves

Chapter Contents (Back)
Segmentation, Edges. Deformable Curves. Snakes. Active Contours. Curve Evolution. Tracking. There may be a somewhat arbitrary division of papers to limit the large size of the files -- look in other related sections also.
See also Contours Through a Sequence.
See also Deformable Models, University of Manchester Papers.
See also Deformable Solids -- Terzopoulos Papers.
See also Deformable Solids -- Pentland Papers.
See also Nonrigid, Non-Rigid, Deformable Motion Analysis and Tracking.
See also Deformable Models for Segmentation.
See also Active Contours and Snakes, Region Segmentation Issues.
See also Active Volumes, Deformable Solids, 3-D Snakes, etc..

8.7.1.1 Snakes, General Techniques and Descriptions

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

qsnake_demo,
2007. Code, Snakes.
HTML Version. Basic tool to play with snakes (active contour models).

Kass, M., Witkin, A.P., and Terzopoulos, D.,
Snakes: Active Contour Models,
IJCV(1), No. 4, January 1988, pp. 321-331.
Springer DOI BibRef 8801 IJCV(1), No. 4, January 1988, pp. 321-331.
Springer DOI BibRef
Earlier: ICCV87(259-268). Award, Marr Prize, HM. Award, ICCV Test of Time. Local properties. Much like a 2D version of the 3D symmetry seeking method. From one study the third most cited paper. BibRef

Blake, A.[Andrew],
Introduction to Active Contours and Visual Dynamics,
Online BookJune 1999,
HTML Version. Dept. Engineering Science, University of Oxford. BibRef 9906

Cohen, L.D.[Laurent D.],
On Active Contour Models and Balloons,
CVGIP(53), No. 2, March 1991, pp. 211-218.
Elsevier DOI
PS File. Balloon Models. Contour expands to meet the edge, rather than straightens into a line. For 3D:
See also Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images. BibRef 9103

Cohen, L.D.[Laurent D.],
Etude de quelques problemes semi-lineaires paraboliques et elliptiques,
Ph.D.Thesis, Universite Paris 6, 1986. BibRef 8600

Cohen, L.D.[Laurent D.],
Etude des modeles de contours actifs et d'autres techniques de traitement d'Images,
2nd Ph.D.Thesis, Universite Paris-Sud Orsay, 1990. BibRef 9000

Xu, G.[Gang], Segawa, E.[Eigo], Tsuji, S.[Saburo],
Robust Active Contours with Insensitive Parameters,
PR(27), No. 7, July 1994, pp. 879-884.
Elsevier DOI BibRef 9407
Earlier:
A robust active contour model with insensitive parameters,
ICCV93(562-566).
IEEE DOI BibRef
Earlier: A2, A1, A3:
Minimal region extraction using expanding active contours,
ICPR92(III:688-691).
IEEE DOI 9208
BibRef

Kimia, B.B., Tannenbaum, A., Zucker, S.W.,
On the Evolution of Curves Via a Function of Curvature. I. The Classical Case,
JMAA(163), 1992, pp. 438-458. BibRef 9200

Kimia, B.B.[Benjamin B.], Brown University Tannenbaum, A.[Allen], University of Minnesota Zucker, S.W.[Steven W.], McGill University
On Optimal Control Methods in Computer Vision and Image Processing,
GDDCV94(Chapter 10). BibRef 9400

Urago, S.[Sabine], Zerubia, J.B.[Josiane B.], Berthod, M.[Marc],
A Markovian Model for Contour Grouping,
PR(28), No. 5, May 1995, pp. 683-693. BibRef 9505
Earlier:
Elsevier DOI ICPR94(A:556-558).
IEEE DOI BibRef

Lai, K.F., Chin, R.T.,
Deformable Contours: Modeling and Extraction,
PAMI(17), No. 11, November 1995, pp. 1084-1090.
IEEE DOI
PS File. BibRef 9511
Earlier: CVPR94(601-608).
IEEE DOI
PS File. BibRef

Lai, K.F., Chin, R.T.,
On Regularization, Formulation, and Initialization of the Active contour models (Snakes),
ACCV93(542-545).
PS File. BibRef 9300

Lai, K.F.,
Deformable Contours: Modeling, Extraction, Detection and Classification,
Ph.D.August 1994. BibRef 9408 Univ. of Wisconsin
PS File. BibRef

Pauwels, E.J.[Eric J.], Fiddelaers, P.[Peter], Van Gool, L.J.[Luc J.],
Enhancement of Planar Shape Through Optimization of Functionals for Curves,
PAMI(17), No. 11, November 1995, pp. 1101-1105.
IEEE DOI BibRef 9511
Earlier:
Shape Extraction for Curves Using Geometry-Driven Diffusion and Functional Optimization,
ICCV95(396-401).
IEEE DOI Deformable contours. BibRef

Pauwels, E.J., Fiddelaers, P., Van Gool, L.J.,
Autonomous Grouping of Contour-Segments Using an Adaptive Region-Growing Algorithm,
ICPR96(II: 586-590).
IEEE DOI 9608
(ESAT, B) BibRef

Fiddelaers, P., Pauwels, E.J., Van Gool, L.J.,
Geometry-Driven Curve Evolution,
ECCV94(A:427-432).
Springer DOI BibRef 9400

Pauwels, E.J.[Eric J.], Fiddelaers, P.[Peter], Mindru, F.[Florica],
Fully unsupervised clustering using centre-surround receptive fields with applications to colour-segmentation,
CAIP97(17-24).
Springer DOI 9709
BibRef

Pauwels, E.J.[Eric J.], Proesmans, M., Van Gool, L.J.[Luc J.], Moons, T., Oosterlinck, A.,
Segmentation and Image Enhancement Using Coupled Anisotropic Diffusion,
SPIE(2094), 1993, pp. 836-847. BibRef 9300

Wang, M., Evans, J., Hassebrook, L., Knapp, C.,
A Multistage, Optimal Active Contour Model,
IP(5), No. 11, November 1996, pp. 1586-1591.
IEEE DOI 9611
BibRef

Ngoi, K.P., Jia, J.C.,
A New Color Image Energy for Active Contours in Natural Scenes,
PRL(17), No. 12, October 25 1996, pp. 1271-1277. 9612
BibRef

Durikovic, R., Kaneda, K., and Yamashita, H.,
Dynamic Contour: A Texture Approach and Contour Operations,
VC(11), 1995, pp. 277-289. BibRef 9500

Rieger, J.H.,
Generic Evolutions of Edges on Families of Diffused Greyvalue Surfaces,
JMIV(5), No. 3, September 1995, pp. 207-217. BibRef 9509

Doyen, L.,
Mutational Equations for Shapes and Vision-Based Control,
JMIV(5), No. 2, June 1995, pp. 99-109. BibRef 9506

Wang, G.Z., Zheng, J.M.,
Bounds on the Moving Control Points of Hybrid Curves,
GMIP(59), No. 1, January 1997, pp. 19-25. 9703
BibRef

Siddiqi, K.[Kaleem], Lauziere, Y.B.[Yves Berube], Tannenbaum, A.[Allen], Zucker, S.W.[Steven W.],
Area and Length Minimizing Flows for Shape Segmentation,
IP(7), No. 3, March 1998, pp. 433-443.
IEEE DOI 9803
BibRef
Earlier: CVPR97(621-627).
IEEE DOI 9704
Overcome some of the problems with standard snake control. BibRef

Zucker, S.W., Siddiqi, K., and Tannenbaum, A.,
Area Minimizing Flows,
ICIP97(III: 392-395).
IEEE DOI BibRef 9700

Moisan, L.[Lionel],
Affine Plane Curve Evolution: A Fully Consistent Scheme,
IP(7), No. 3, March 1998, pp. 411-420.
IEEE DOI 9803
BibRef

Moisan, L.[Lionel],
Periodic Plus Smooth Image Decomposition,
JMIV(39), No. 2, February 2011, pp. 161-179.
WWW Link. 1103
BibRef

Lai, K.F.[Kok F.], Chin, R.T.[Roland T.],
On Modeling, Extraction, Detection and Classification of Deformable Contours from Noisy Images,
IVC(16), No. 1, January 30 1998, pp. 55-62.
Elsevier DOI 9803
BibRef

Marques, J.S.,
A Link Between Image Based and Feature Based Active Contours,
SP(67), No. 3, June 1998, pp. 271-278. 9808
BibRef

Thompson, S.F.[Scott F.], Rosenfeld, A.[Azriel],
Discrete, Nonlinear Curvature-Dependent Contour Evolution,
PR(31), No. 12, December 1998, pp. 1949-1959. BibRef 9812
Earlier:
Elsevier DOI UMD--TR3825, August 1997.
WWW Link. BibRef

Davatzikos, C.[Christos], Prince, J.L.[Jerry L.],
Convexity Analysis of Active Contour Problems,
IVC(17), No. 1, January 1999, pp. 27-36.
Elsevier DOI BibRef 9901
Earlier: CVPR96(674-679).
IEEE DOI BibRef
Earlier:
Adaptive Active Contour Algorithms for Extracting and Mapping Thick Curves,
CVPR93(524-529).
IEEE DOI BibRef

Shen, D.G.[Ding-Gang], Davatzikos, C.[Christos],
An Adaptive-Focus Deformable Model Using Statistical and Geometric Information,
PAMI(22), No. 8, August 2000, pp. 906-913.
IEEE DOI 0010
Use an attribute vector of the region, deform contour to seek regions with similar attribute vectors. Shape at each pahse influenced by most reliable matches.
See also adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures, An. BibRef

Davatzikos, C.[Christos], Tao, X.D.[Xiao-Dong], Shen, D.G.[Ding-Gang],
Hierarchical active shape modls, using the wavelet transform,
MedImg(22), No. 3, March 2003, pp. 414-423.
IEEE Abstract. 0306
BibRef

Schnabel, J.A.[Julia A.], Arridge, S.R.[Simon R.],
Active Shape Focusing,
IVC(17), No. 5/6, April 1999, pp. 419-428.
Elsevier DOI BibRef 9904
Earlier:
Multi-Scale Active Shape Description,
ScaleSpace97(xx). 9702
BibRef
Earlier:
Active Contour Models for Shape Description Using Multiscale Differential Invariants,
BMVC95(xx)
PDF File. Multi-scale descriptors to allow for extracting models with high curvature points.
HTML Version. or
PS File. BibRef

Ehrhardt, M.J., Arridge, S.R.,
Vector-Valued Image Processing by Parallel Level Sets,
IP(23), No. 1, January 2014, pp. 9-18.
IEEE DOI 1402
gradient methods BibRef

Peckar, W.[Wladimir], Schnörr, C.[Christoph], Rohr, K.[Karl], Stiehl, H.S.[H. Siegfried],
Parameter-Free Elastic Deformation Approach for 2D and 3D Registration Using Prescribed Displacements,
JMIV(10), No. 2, March 1999, pp. 143-162.
DOI Link BibRef 9903
Earlier:
Two-step parameter-free elastic image registration with prescribed point displacements,
CIAP97(I: 527-534).
Springer DOI 9709
BibRef

Latecki, L.J.[Longin Jan], Megalooikonomou, V.[Vasileios], Wang, Q.A.[Qi-Ang], Yu, D.G.[De-Guang],
An elastic partial shape matching technique,
PR(40), No. 11, November 2007, pp. 3069-3080.
Elsevier DOI 0707
Shape similarity; Sequences matching; DAG; Shortest path
See also Convexity Rule for Shape Decomposition Based on Discrete Contour Evolution. BibRef

Lu, C.E.[Cheng-En], Latecki, L.J.[Longin Jan], Zhu, G.X.[Guang-Xi],
Contour Extraction Using Particle Filters,
ISVC08(II: 192-201).
Springer DOI 0812
BibRef

El Moataz, A.[Abderrahim], Schüpp, S.[Sophie], Clouard, R.[Régis], Herlin, P.[Paulette], Bloyet, D.[Daniel],
Using active contours and mathematical morphology tools for quantification of immunohistochemical images,
SP(71), No. 2, 15 December 1998, pp. 215-226. BibRef 9812

Schüpp, S., El Moataz, A., Bloyet, D.,
Discrete Approach for Active Contours for Biomedical Applications,
SCIA01(P-W3A). 0206
BibRef

Xu, C.Y.[Chen-Yang], Prince, J.L.[Jerry L.],
Generalized gradient vector flow external forces for active contours,
SP(71), No. 2, 15 December 1998, pp. 131-139. BibRef 9812
Earlier:
Gradient Vector Flow: A New External Force for Snakes,
CVPR97(66-71).
IEEE DOI 9704
Code, Snakes. Code:
HTML Version. BibRef

Xu, C.Y., Prince, J.L.,
Snakes, Shapes, and Gradient Vector Flow,
IP(7), No. 3, March 1998, pp. 359-369.
IEEE DOI 9803
BibRef

Salden, A.H.[Alfons H.], ter Haar Romeny, B.M.[Bart M.], Viergever, M.A.[Max A.],
Linearised Euclidean Shortening Flow of Curve Geometry,
IJCV(34), No. 1, September-October 1999, pp. 29-67.
DOI Link BibRef 9909

Davison, N.E., Eviatar, H., Somorjai, R.L.,
Snakes simplified,
PR(33), No. 10, October 2000, pp. 1651-1664.
Elsevier DOI 0006
BibRef

Höwing, F., Dooley, L.S., Wermser, D.,
Fuzzy active contour model,
VISP(147), No. 4, 2000, pp. 323-330. 0010
BibRef

Choi, W.P.[Wai-Pak], Lam, K.M.[Kin-Man], Siu, W.C.[Wan-Chi],
An adaptive active contour model for highly irregular boundaries,
PR(34), No. 2, February 2001, pp. 323-331.
Elsevier DOI 0011
BibRef

Sakalli, M., Lam, K.M.[Kin-Man], Yan, H.[Hong],
A Faster Converging Snake Algorithm to Locate Object Boundaries,
IP(15), No. 5, May 2006, pp. 1182-1191.
IEEE DOI 0605
BibRef
Earlier:
Shivering greedy snakes, gradient-guided in wavelet domain,
ICIP98(II: 886-890).
IEEE DOI 9810
BibRef

Fornefett, M.[Mike], Rohr, K.[Karl], Stiehl, H.S.[H. Siegfried],
Radial basis functions with compact support for elastic registration of medical images,
IVC(19), No. 1-2, January 2001, pp. 87-96.
Elsevier DOI 0101
BibRef
Earlier:
Elastic Registration of Medical Images Using Radial Basis Functions with Compact Support,
CVPR99(I: 402-407).
IEEE DOI BibRef

Darrell, T.J.[Trevor J.], Covell, M.M.[Michele M.],
Correspondence with Cumulative Similarity Transforms,
PAMI(23), No. 2, February 2001, pp. 222-227.
IEEE DOI 0102
Local image transform for tracking near occluding boundaries. Tracking mouths and hands. BibRef

Covell, M.M.[Michele M.], Darrell, T.J.[Trevor J.],
Dynamic Occluding Contours: A New External-energy Term for Snakes,
CVPR99(II: 232-238).
IEEE DOI BibRef 9900

Darrell, T.J.[Trevor J.],
A Radial Cumulative Similarity Transform for Robust Image Correspondence,
CVPR98(656-662).
IEEE DOI BibRef 9800

Darrell, T.J., Pentland, A.P.,
On the representation of occluded shapes,
CVPR91(728-729).
IEEE DOI 0403
BibRef

Velasco, F.A.[Fernando A.], Marroquín, J.L.[José L.],
Robust parametric active contours: the Sandwich Snakes,
MVA(12), No. 5, 2001, pp. 238-242.
Springer DOI 0103
BibRef

Velasco, F.A.[Fernando A.], Marroquin, J.L.[Jose L.],
Growing snakes: active contours for complex topologies,
PR(36), No. 2, February 2003, pp. 475-482.
Elsevier DOI 0211
BibRef

Park, H.W.[Hyun-Wook], Schoepflin, T., Kim, Y.M.[Yong-Min],
Active contour model with gradient directional information: Directional snake,
CirSysVideo(11), No. 2, February 2001, pp. 252-256.
IEEE Top Reference. 0104
BibRef

Fenster, S.D.[Samuel D.], Kender, J.R.[John R.],
Sectored Snakes: Evaluating Learned-Energy Segmentations,
PAMI(23), No. 9, September 2001, pp. 1028-1034.
IEEE DOI 0110
BibRef
Earlier: ICCV98(420-426).
IEEE DOI BibRef
And: DARPA98(1193-1199). Learning appled to segmentation with user specified criteria. Learning allows it to not go to the strongest boundary. Secrots of the snake -- intensity and gradient ove equal length sectors of the snake, not globally. BibRef

Dumitras, A., Venetsanopoulos, A.N.,
Angular map-driven snakes with application to object shape description in color images,
IP(10), No. 12, December 2001, pp. 1851-1859.
IEEE DOI 0201
BibRef
Earlier:
Color Image-based Angular Map-driven Snakes,
ICIP01(I: 129-132).
IEEE DOI 0108
BibRef

Wang, Z.Q.[Zhi-Qian], Ben-Arie, J.[Jezekiel],
Detection and segmentation of generic shapes based on affine modeling of energy in Eigenspace,
IP(10), No. 11, November 2001, pp. 1621-1629.
IEEE DOI 0201
BibRef
Earlier:
Detection and Segmentation of Generic Shapes Based on Vectorial Affine Modeling of Energy in Eigenspace,
ICPR00(Vol III: 971-975).
IEEE DOI 0009
Grouping in edge maps. Shapes are affine transformed version of basic shapes (rectangles, circles). Use vectorial boundary. Detect then verify.
See also Pictorial Recognition of Objects Employing Affine Invariance in the Frequency-Domain. BibRef

Wang, Z.Q.[Zhi-Qian], Ben-Arie, J.[Jezekiel],
Generic Object Detection using Model Based Segmentation,
CVPR99(II: 428-433).
IEEE DOI BibRef 9900
Earlier:
Model based segmentation and detection of affine transformed shapes in cluttered images,
ICIP98(III: 75-79).
IEEE DOI 9810
Finding generic objects (rectangles, dicrles, etc.) BibRef

Small, C.G.[Christopher G.], Le, H.L.[Hui-Ling],
The statistical analysis of dynamic curves and sections,
PR(35), No. 7, July 2002, pp. 1597-1609.
Elsevier DOI 0204

See also Multidimensional scaling of simplex shapes. BibRef

Heo, G.[Giseon], Small, C.G.[Christopher G.],
Form representions and means for landmarks: A survey and comparative study,
CVIU(102), No. 2, May 2006, pp. 188-203.
Elsevier DOI Survey, Segmentation. Tomography; Invariance 0605
BibRef

Metaxas, D.N.[Dimitris N.], Kakadiaris, I.A.[Ioannis A.],
Elastically Adaptive Deformable Models,
PAMI(24), No. 10, October 2002, pp. 1310-1321.
IEEE Abstract. 0210
BibRef
Earlier: ECCV96(II:550-559).
Springer DOI Adapt the parameters. BibRef

Tan, S.[Shan], Kakadiaris, I.A.[Ioannis A.],
Kernel active contour,
ICCV09(521-528).
IEEE DOI 0909
BibRef

Li, Z.L.[Zheng-Long], Liu, Q.S.[Qing-Shan], Lu, H.Q.[Han-Qing], Metaxas, D.N.[Dimitris N.],
Lennard-Jones force field for Geometric Active Contour,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Huang, R.[Rui], Sang, N.[Nong], Pavlovic, V.[Vladimir], Metaxas, D.N.[Dimitris N.],
A Belief Propagation algorithm for bias field estimation and image segmentation,
ICIP11(37-40).
IEEE DOI 1201
BibRef

Huang, R.[Rui], Pavlovic, V.[Vladimir], Metaxas, D.N.[Dimitris N.],
A new spatio-temporal MRF framework for video-based object segmentation,
MLMotion08(xx-yy). 0810
BibRef
Earlier:
A Hybrid Framework for Image Segmentation Using Probabilistic Integration of Heterogeneous Constraints,
CVBIA05(82-92).
Springer DOI 0601
BibRef
Earlier:
A graphical model framework for coupling MRFs and deformable models,
CVPR04(II: 739-746).
IEEE DOI 0408
BibRef

Bianchi, A.G.C.[Andrea G. Campos], dos Santos, M.F.[Marinilce F.], Britto, D.E.H.[Dânia E. Hamassaki], da Fontoura Costa, L.[Luciano],
Inferring shape evolution,
PRL(24), No. 7, April 2003, pp. 1005-1014.
Elsevier DOI 0301
BibRef

Wang, J.K.[Jian-Kang], Li, X.B.[Xiao-Bo],
Guiding Ziplock Snakes with a Priori Information,
IP(12), No. 2, February 2003, pp. 176-185.
IEEE DOI 0304
BibRef
Earlier: ICPR00(Vol I: 614-617).
IEEE DOI 0009
BibRef

Wang, S.[Song], Siskind, J.M.[Jeffrey Mark],
Image segmentation with ratio cut,
PAMI(25), No. 6, June 2003, pp. 675-690-4.
IEEE Abstract. 0306
Ratio of corresponding sums of two different weights of edges along th ecut moundary. BibRef

Wang, S.[Song], Siskind, J.M.[Jeffrey Mark],
Image Segmentation with Minimum Mean Cut,
ICCV01(I: 517-524).
IEEE DOI 0106
Minimum mean edge weight in the connected graph. BibRef

González Linares, J.M., Guil, N., Zapata, E.L.,
An efficient 2D deformable objects detection and location algorithm,
PR(36), No. 11, November 2003, pp. 2543-2556.
Elsevier DOI 0309
BibRef
Earlier:
Deformable Shapes Detection by Stochastic Optimization,
ICIP00(Vol I: 780-783).
IEEE DOI 0008
BibRef

Brunet, N.[Nuria], Perez, F.[Francisco], de la Torre, F.[Fernando],
Learning good features for Active Shape Models,
Subspace09(206-211).
IEEE DOI 0910
BibRef

Gonzalez-Mora, J.[Jose], de la Torre, F.[Fernando], Guil, N.[Nicolas], Zapata, E.L.[Emilio L.],
Learning a generic 3D face model from 2D image databases using incremental Structure-from-Motion,
IVC(28), No. 7, July 2010, pp. 1117-1129.
Elsevier DOI 1006
Structure-from-Motion; Incremental learning; Morphable Models; Active Appearance Models BibRef

Gonzalez-Mora, J.[Jose], de la Torre, F.[Fernando], Murthi, R.[Rajesh], Guil, N.[Nicolas], Zapata, E.L.[Emilio L.],
Bilinear Active Appearance Models,
NRTL07(1-8).
IEEE DOI 0710
BibRef

Gonzalez-Mora, J.[Jose], Guil, N.[Nicolas], Zapata, E.L.[Emilio L.], de la Torre, F.[Fernando],
Efficient image alignment using linear appearance models,
CVPR09(2230-2237).
IEEE DOI 0906
BibRef

Wang, X.[Xun], He, L.[Lei], Tang, Y.J.[Ying-Jie], Wee, W.G.,
A divide and conquer deformable contour method with a model based searching algorithm,
SMC-B(33), No. 5, October 2003, pp. 738-751.
IEEE Abstract. 0310
BibRef

Wang, X.[Xun], Wee, W.G.,
A new deformable contour method,
CIAP99(430-435).
IEEE DOI 9909
BibRef

He, L.[Lei], Wee, W.G.[William G.], Zheng, S.F.[Song-Feng], Wang, L.[Li],
A level set model without initial contour,
WACV09(1-6).
IEEE DOI 0912
BibRef

Wang, X.[Xun], He, L.[Lei], Wee, W.G.[William G.],
Deformable Contour Method: A Constrained Optimization Approach,
IJCV(59), No. 1, August 2004, pp. 87-108.
DOI Link 0404
BibRef
Earlier: Add A3: Han, C.Y., BMVC02(Poster Session). 0208
BibRef

Wang, X.[Xun], Gao, F.[Feng], Wee, W.G.,
Mean Field Annealing Deformable Contour Method: A Constrained Global Optimization Approach,
ICARCV06(1-8).
IEEE DOI 0612
BibRef

He, L.[Lei], Zheng, S.F.[Song-Feng], Wang, L.[Li],
Integrating local distribution information with level set for boundary extraction,
JVCIR(21), No. 4, May 2010, pp. 343-354.
Elsevier DOI 1006
Image segmentation; Implicit active contour; Gaussian mixture model; Hueckel edge operator; Zernike moments; Local distribution fitting; Level set without initial contour; Piecewise smooth image BibRef

Bischoff, S.[Stephan], Kobbelt, L.P.[Leif P.],
Parameterization-free active contour models with topology control,
VC(20), No. 4, June 2004, pp. 217-228.
Springer DOI 0406
BibRef

Xu, Q.[Qing], Yang, J.[Jie], Ding, S.Y.[Si-Yi],
Texture Segmentation using LBP embedded Region Competition,
ELCVIA(5), No. 1, 2005, pp. 41-47.
DOI Link 0505
BibRef

Burghardt, D.[Dirk],
Controlled Line Smoothing by Snakes,
GeoInfo(9), No. 3, September 2005, pp. 237-252.
Springer DOI 0509
BibRef

Cheng, J., Foo, S.W.,
Dynamic Directional Gradient Vector Flow for Snakes,
IP(15), No. 6, June 2006, pp. 1563-1571.
IEEE DOI 0606
BibRef

Ning, J.F.[Ji-Feng], Wu, C.K.[Cheng-Ke], Liu, S.G.[Shi-Gang], Yang, S.Q.[Shu-Qin],
NGVF: An improved external force field for active contour model,
PRL(28), No. 1, 1 January 2007, pp. 58-63.
Elsevier DOI 0611
Active contour model; Gradient vector flow; GVF in the normal direction; Image interpolation BibRef

Ning, J.F.[Ji-Feng], Wu, C.K.[Cheng-Ke], Liu, S.G.[Shi-Gang], Wen, P.Z.[Pei-Zhi],
A New Active Contour Model: Curvature Gradient Vector Flow,
ACCV06(I:633-642).
Springer DOI 0601
BibRef

Li, H.[Hua], Yezzi, A.J.[Anthony J.],
Local or Global Minima: Flexible Dual-Front Active Contours,
PAMI(29), No. 1, January 2007, pp. 1-14.
IEEE DOI 0701
BibRef
Earlier: CVBIA05(356-366).
Springer DOI 0601
Adjust global-local by adjusting the size of the active region on the contour. BibRef

Shih, F.Y.[Frank Y.], Zhang, K.[Kai],
Locating object contours in complex background using improved snakes,
CVIU(105), No. 2, February 2007, pp. 93-98.
Elsevier DOI 0702
Snake; Active contour model; Image segmentation; Edge detection BibRef

Ozertem, U., Erdogmus, D.,
Nonparametric Snakes,
IP(16), No. 9, September 2007, pp. 2361-2368.
IEEE DOI 0709
BibRef

Adam, A.[Amit], Kimmel, R.[Ron], Rivlin, E.[Ehud],
On Scene Segmentation and Histograms-Based Curve Evolution,
PAMI(31), No. 9, September 2009, pp. 1708-1714.
IEEE DOI 0907
Base on comparing distributions of features. Cross-bin metrics. BibRef

Tatu, A.[Aditya], Lauze, F.[François], Sommer, S.[Stefan], Nielsen, M.[Mads],
On Restricting Planar Curve Evolution to Finite Dimensional Implicit Subspaces with Non-Euclidean Metric,
JMIV(38), No. 3, November 2010, pp. 226-240.
WWW Link. 1011
BibRef

Arnaudon, A.[Alexis], van der Meulen, F.[Frank], Schauer, M.[Moritz], Sommer, S.[Stefan],
Diffusion Bridges for Stochastic Hamiltonian Systems and Shape Evolutions,
SIIMS(15), No. 1, 2022, pp. 293-323.
DOI Link 2204
BibRef

Tatu, A.[Aditya], Lauze, F.[François], Nielsen, M.[Mads], Olsen, O.F.[Ole Fogh],
Curve Evolution in Subspaces,
SSVM07(754-764).
Springer DOI 0705
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Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers,
SSVM19(369-380).
Springer DOI 1909
BibRef

Hansen, J.D.K.[Jacob Daniel Kirstejn], Lauze, F.[François],
Local Mean Multiphase Segmentation with HMMF Models,
SSVM17(396-407).
Springer DOI 1706
BibRef

Bansal, S.[Sumukh], Tatu, A.[Aditya],
Active Contour Models for Manifold Valued Image Segmentation,
JMIV(52), No. 2, June 2015, pp. 303-314.
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Earlier: A2, A1:
A Novel Active Contour Model for Texture Segmentation,
EMMCVPR15(223-236).
Springer DOI 1504
BibRef

Kovacs, A.[Andrea], Sziranyi, T.[Tamas],
Harris function based active contour external force for image segmentation,
PRL(33), No. 9, 1 July 2012, pp. 1180-1187.
Elsevier DOI 1202
BibRef
Earlier:
High Definition Feature Map for GVF Snake by Using Harris Function,
ACIVS10(I: 163-172).
Springer DOI 1012
Boundary extraction; Gradient vector flow; Vector field convolution; Harris corner detection BibRef

Song, Y.Q.[Yu-Qing], Liu, Z.[Zhe], Chen, J.M.[Jian-Mei], Zhu, F.[Feng], Xie, C.H.[Cong-Hua],
Medical image segmentation based on non-parametric mixture models with spatial information,
SIViP(6), No. 4, November 2012, pp. 569-578.
WWW Link. 1210
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Tian, Y.[Yun], Duan, F.Q.[Fu-Qing], Zhou, M.Q.[Ming-Quan], Wu, Z.K.[Zhong-Ke],
Active contour model combining region and edge information,
MVA(24), No. 1, January 2013, pp. 47-61.
WWW Link. 1301
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Gadermayr, M.[Michael], Maier, A.[Andreas], Uhl, A.[Andreas],
Active contours methods with respect to Vickers indentations,
MVA(24), No. 6, August 2013, pp. 1183-1196.
WWW Link. 1307
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Liu, G.Q.[Guo-Qi], Zhou, Z.H.[Zhi-Heng], Xie, S.L.[Sheng-Li], Wu, D.C.[Dong-Cheng],
Dynamically Constrained Vector Field Convolution for Active Contour Model,
IEICE(E96-D), No. 11, November 2013, pp. 2500-2503.
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Pereyra, M., Batatia, H., McLaughlin, S.,
Exploiting Information Geometry to Improve the Convergence of Nonparametric Active Contours,
IP(24), No. 3, March 2015, pp. 836-845.
IEEE DOI 1502
image segmentation BibRef

Pereyra, M., McLaughlin, S.,
Fast Unsupervised Bayesian Image Segmentation With Adaptive Spatial Regularisation,
IP(26), No. 6, June 2017, pp. 2577-2587.
IEEE DOI 1705
Bayes methods, hidden Markov models, image denoising, image segmentation, least squares approximations, Bayesian estimation technique, Bayesian model, K-means problem, SVA Bayesian estimator, SVA analysis, adaptive spatial regularisation, convex total-variation denoising problem, fast unsupervised Bayesian image segmentation, fast unsupervised K-class image segmentation, hidden Potts-Markov random fields, inference procedure, integer-constrained terms, least-square clustering problem, marginalisation, parallel computing technique, real image, self-adjusting regularisation parameter, small-variance-asymptotic analysis, synthetic image, unknown regularisation parameter, Approximation algorithms, Bayes methods, Computational modeling, Hidden Markov models, Image segmentation, Inference algorithms, Optimization, Bayesian methods, Image segmentation, Potts Markov random field, convex optimisation, spatial, mixture, models BibRef

Pi, M.H., Ma, J., Zhang, W., Zhou, Z.,
Signal-walking-driven active contour model,
IET-IPR(9), No. 12, 2015, pp. 1101-1106.
DOI Link 1512
edge detection BibRef

Ge, Q.[Qi], Shen, F.M.[Fu-Min], Jing, X.Y.[Xiao-Yuan], Wu, F.[Fei], Xie, S.P.[Shi-Peng], Yue, D.[Dong], Li, H.B.[Hai-Bo],
Active contour evolved by joint probability classification on Riemannian manifold,
SIViP(10), No. 7, October 2016, pp. 1257-1264.
Springer DOI 1609
BibRef

Yang, Y.[Yong], Guo, L.[Ling], Ye, Y.D.[Yang-Dong],
Robust natural image segmentation by using spatially constrained multivariate mixed Student's t-distribution and TV flow edge,
JVCIR(40, Part A), No. 1, 2016, pp. 178-196.
Elsevier DOI 1609
Spatially constrained image segmentation BibRef

Boutiche, Y.[Yamina], Abdesselam, A.[Abdelhamid],
Fast algorithm for hybrid region-based active contours optimisation,
IET-IPR(11), No. 3, March 2017, pp. 200-209.
DOI Link 1703
BibRef

Han, B.[Bin], Wu, Y.Q.[Yi-Quan],
A novel active contour model based on modified symmetric cross entropy for remote sensing river image segmentation,
PR(67), No. 1, 2017, pp. 396-409.
Elsevier DOI 1704
Image segmentation BibRef

Hu, Z.P.[Zheng-Ping], Zhang, Z.B.[Zhen-Bin], Sun, Z.[Zhe], Zhao, S.H.[Shu-Huan],
Salient object detection via sparse representation and multi-layer contour zooming,
IET-CV(11), No. 4, June 2017, pp. 309-318.
DOI Link 1705
BibRef

Sakaridis, C.[Christos], Drakopoulos, K.[Kimon], Maragos, P.[Petros],
Theoretical Analysis of Active Contours on Graphs,
SIIMS(10), No. 3, 2017, pp. 1475-1510.
DOI Link 1710
BibRef

Park, H.M.[Han-Mu], Cho, D.Y.[Dae-Yong], Yoon, K.J.[Kuk-Jin],
Greedy refinement of object proposals via boundary-aligned minimum bounding box search,
IET-CV(12), No. 3, April 2018, pp. 357-363.
DOI Link 1804
BibRef

Zhang, Z.Z.[Zi-Zhao], Xing, F.Y.[Fu-Yong], Wang, H.Z.[Han-Zi], Yan, Y.[Yan], Huang, Y.[Ying], Shi, X.S.[Xiao-Shuang], Yang, L.[Lin],
Revisiting graph construction for fast image segmentation,
PR(78), 2018, pp. 344-357.
Elsevier DOI 1804
Image segmentation, Graph partition, Manifold BibRef

Zhang, Z.Z.[Zi-Zhao], Xing, F.Y.[Fu-Yong], Shi, X.S.[Xiao-Shuang], Yang, L.[Lin],
SemiContour: A Semi-Supervised Learning Approach for Contour Detection,
CVPR16(251-259)
IEEE DOI 1612
Semi-supervised learning for contours BibRef

Lin, C.[Chuan], Xu, G.[Guili], Cao, Y.J.[Yi-Jun],
Contour detection model using linear and non-linear modulation based on non-CRF suppression,
IET-IPR(12), No. 6, June 2018, pp. 993-1003.
DOI Link 1805
BibRef

Lin, C.[Chuan], Xu, G.[Guili], Cao, Y.J.[Yi-Jun],
Contour detection model based on neuron behaviour in primary visual cortex,
IET-CV(12), No. 6, September 2018, pp. 863-872.
DOI Link 1808
BibRef

Manno-Kovacs, A.[Andrea],
Direction Selective Contour Detection for Salient Objects,
CirSysVideo(29), No. 2, February 2019, pp. 375-389.
IEEE DOI 1902
BibRef
Earlier:
Direction selective vector field convolution for contour detection,
ICIP14(4722-4726)
IEEE DOI 1502
Active contours, Force, Feature extraction, Image edge detection, Data mining, Shape, Image segmentation, Direction selectivity, boundary detection. BibRef

Luo, S., Sarabandi, K., Tong, L., Guo, S.,
An Improved Fuzzy Region Competition-Based Framework for the Multiphase Segmentation of SAR Images,
GeoRS(58), No. 4, April 2020, pp. 2457-2470.
IEEE DOI 2004
Active contour model, fuzzy function, hierarchical strategy, region competition, synthetic aperture radar (SAR) image segmentation BibRef

Hu, X.L.[Xiao-Lin], Tang, C.F.[Chu-Feng], Chen, H.[Hang], Li, X.[Xiao], Li, J.M.[Jian-Min], Zhang, Z.X.[Zhao-Xiang],
Improving Image Segmentation with Boundary Patch Refinement,
IJCV(130), No. 11, November 2022, pp. 2571-2589.
Springer DOI 2210
BibRef

Liu, C.[Chaoyu], Qiao, Z.H.[Zhong-Hua], Zhang, Q.[Qian],
An Active Contour Model with Local Variance Force Term and Its Efficient Minimization Solver for Multiphase Image Segmentation,
SIIMS(16), No. 1, 2023, pp. 144-168.
DOI Link 2302
BibRef

Naik, G.[Gunjan], Kelkar, S.[Shubhangi], Garware, B.[Bhushan], Abhyankar, A.[Aditya],
Adaptive kernel-based active contour,
IJCVR(13), No. 2, 2023, pp. 202-218.
DOI Link 2303
BibRef

Yang, Y.J.[Yi-Jin], Gu, X.D.[Xiao-Dong],
Accurate and robust visual tracking using bounding box refinement and online sample filtering,
SP:IC(116), 2023, pp. 116981.
Elsevier DOI 2307
Visual object tracking, Mask initialization network, Bounding box refinement, Online sample filtering BibRef

Ge, P.Q.[Peng-Qiang], Chen, Y.Y.[Yi-Yang], Wang, G.[Guina], Weng, G.R.[Gui-Rong],
An active contour model based on Jeffreys divergence and clustering technology for image segmentation,
JVCIR(99), 2024, pp. 104069.
Elsevier DOI 2403
K-medoids, Active contour model, Jeffreys divergence, Level set method, Data-driven BibRef

Rahman, C.M.A.[Chowdhury M. Abid], Nyeem, H.[Hussain],
Tensor-enhanced shock energy-driven active contours: A novel approach for knowledge-based image segmentation,
JVCIR(103), 2024, pp. 104218.
Elsevier DOI 2409
Active contour, Image segmentation, Level-set, Optimized shock filter, Variational methods BibRef

Zheng, Y.[Yang], Andrienko, O.[Oles], Zhao, Y.L.[Yong-Lei], Park, M.W.[Min-Woo], Pham, T.[Trung],
DPPD: Deformable Polar Polygon Object Detection,
WAD23(78-87)
IEEE DOI 2309
BibRef

Ma, D., But, W., Wu, X.,
Dual-SVM tracker via Multiple Support Instance and LEVER Strategy,
ICPR18(2124-2129)
IEEE DOI 1812
Target tracking, Support vector machines, Training data, Deformable models, Strain, Computational modeling, Training BibRef

Barbu, A.,
A directed graph approach to active contours,
ICIP17(71-75)
IEEE DOI 1803
Active contours, Image edge detection, Image segmentation, Level set, Liver, Optimization, directed graph optimization BibRef

Pham, M.H.,
Detecting the optimal active contour in the computed tomography image by using entropy to choose coefficients in energy equation,
WSSIP15(41-44)
IEEE DOI 1603
computerised tomography BibRef

Lu, C., Liu, S., Jia, J., Tang, C.K.,
Contour Box: Rejecting Object Proposals without Explicit Closed Contours,
ICCV15(2021-2029)
IEEE DOI 1602
Dynamic programming BibRef

Aich, S.[Shubhra], Lee, Y.C.[Yong-Cheol], Lee, C.W.[Chil-Woo],
Probabilistic contour mapping using oriented gradient features and SVM-bagging,
FCV15(1-5)
IEEE DOI 1506
feature extraction BibRef

Jehan-Besson, S., Tilmant, C., de Cesare, A., Lalande, A., Cochet, A., Cousty, J., Lebenberg, J., Lefort, M., Clarysse, P., Clouard, R., Najman, L., Sarry, L., Frouin, F., Garreau, M.,
A mutual reference shape based on information theory,
ICIP14(887-891)
IEEE DOI 1502
Active contours BibRef

Nilufar, S.[Sharmin], Perkins, T.J.[Theodore J.],
Learning to Detect Contours with Dynamic Programming Snakes,
ICPR14(984-989)
IEEE DOI 1412
Dynamic programming BibRef

Tang, Q.L.[Qi-Ling], Sang, N.[Nong], Liu, H.H.[Hai-Hua],
Learning to detect contours in natural images via biologically motivated schemes,
ICIP13(123-126)
IEEE DOI 1402
Brain modeling BibRef

Anh, N.T.L.[Nguyen Tran Lan], Nhat, V.Q.[Vo Quang], Elyor, K.[Kodirov], Kim, S.H.[Soo-Hyung], Lee, G.S.[Guee-Sang],
Fast automatic saliency map driven geometric active contour model for color object segmentation,
ICPR12(2557-2560).
WWW Link. 1302
BibRef

Sun, J.[Jiuyu], Ray, N.[Nilanjan], Zhang, H.[Hong],
VFCCV snake: A novel active contour model combining edge and regional information,
ICIP14(927-931)
IEEE DOI 1502
Active contours BibRef

Ray, N.[Nilanjan], Acton, S.T.[Scott T.], Zhang, H.[Hong],
Seeing through clutter: Snake computation with dynamic programming for particle segmentation,
ICPR12(801-804).
WWW Link. 1302
BibRef

Morita, K.[Keiko], Imiya, A.[Atsushi], Sakai, T.[Tomoya], Hontan, H.[Hidetaka], Masutani, Y.[Yoshitaka],
Alignment and Morphing for the Boundary Curves of Anatomical Organs,
SSSPR12(458-466).
Springer DOI 1211
BibRef

Lee, T.[Tom], Fidler, S.[Sanja], Dickinson, S.J.[Sven J.],
Multi-cue Mid-level Grouping,
ACCV14(III: 376-390).
Springer DOI 1504
BibRef

Zhang, Z.Q.[Zhi-Qi], Fidler, S.[Sanja], Waggoner, J.W.[Jarrell W.], Cao, Y.[Yu], Dickinson, S.J.[Sven J.], Siskind, J.M.[Jeffrey Mark], Wang, S.[Song],
Superedge grouping for object localization by combining appearance and shape information,
CVPR12(3266-3273).
IEEE DOI 1208
BibRef

Song, Y.[Yang], Cai, W.D.[Wei-Dong], Huang, H.[Heng], Wang, Y.[Yue], Feng, D.D.[David Dagan],
Context Enhanced Graphical Model for Object Localization in Medical Images,
MCVM12(194-205).
Springer DOI 1305
BibRef
And:
Object localization in medical images based on graphical model with contrast and interest-region terms,
MCV12(1-7).
IEEE DOI 1207
BibRef

Yu, H.M.[Hui-Min], Wang, D.D.[Da-Dong],
Comparison Study of Two Energy Minimization Based Image Segmentation Methods,
DICTA11(633-638).
IEEE DOI 1205
BibRef

Jojczyk, K.[Konrad], Pryczek, M.[Michal], Tomczyk, A.[Arkadiusz], Szczepaniak, P.S.[Piotr S.], Grzelak, P.[Piotr],
Cognitive Hierarchical Active Partitions Using Patch Approach,
ICCVG10(I: 35-42).
Springer DOI 1009
BibRef

Walczak, S.[Stanislaw], Tomczyk, A.[Arkadiusz], Szczepaniak, P.S.[Piotr S.],
Interpretation of Images and Their Sequences Using Potential Active Contour Method,
ICCVG10(I: 89-96).
Springer DOI 1009
BibRef

Catanzaro, B.[Bryan], Su, B.Y.[Bor-Yiing], Sundaram, N.[Narayanan], Lee, Y.[Yunsup], Murphy, M.[Mark], Keutzer, K.[Kurt],
Efficient, High-quality Image Contour Detection,
ICCV09(2381-2388).
IEEE DOI
PDF File. 0909
For the system
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Radulescu, T.[Tiberiu], Buzuloiu, V.[Vasile],
A hidden property of the gradient vector flow diffusion process,
ICIP09(2417-2420).
IEEE DOI 0911
Gradient vector flow as the external force on snakes. BibRef

Wang, Q.[Qing], Ronneberger, O.[Olaf], Schulze, E.[Ekkehard], Baumeister, R.[Ralf], Burkhardt, H.[Hans],
Using Lateral Coupled Snakes for Modeling the Contours of Worms,
DAGM09(542-551).
Springer DOI 0909
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Pei, Z.K.[Zhen-Kui], Zhao, Y.L.[Yan-Li], Liu, Z.[Zhen],
Image segmentation based on Differential Evolution algorithm,
IASP09(48-51).
IEEE DOI 0904
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Zhao, X.M.[Xiao-Ming],
The application of balloon snake model in the extraction of parasite image contour,
IASP09(65-69).
IEEE DOI 0904
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Vatavu, R.D.[Radu-Daniel], Grisoni, L.[Laurent], Pentiuc, S.G.[Stefan-Gheorghe],
Gesture Recognition Based on Elastic Deformation Energies,
GW07(1-12).
Springer DOI 0705
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Hansen, M.S.[Michael Sass], Larsen, R.[Rasmus], Glocker, B.[Ben], Navab, N.[Nassir],
Adaptive parametrization of multivariate B-splines for image registration,
CVPR08(1-8).
IEEE DOI 0806
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Zikic, D.[Darko], Hansen, M.S.[Michael Sass], Glocker, B.[Ben], Khamene, A.[Ali], Larsen, R.[Rasmus], Navab, N.[Nassir],
Computing minimal deformations: application to construction of statistical shape models,
CVPR08(1-8).
IEEE DOI 0806
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Cheng, H.[Hong], Liu, Z.C.[Zi-Cheng], Zheng, N.N.[Nan-Ning], Yang, J.[Jie],
A deformable local image descriptor,
CVPR08(1-8).
IEEE DOI 0806
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Ge, Y.[Yong], Li, S.P.[San-Ping], Li, D.Y.[De-Yu],
New Algorithm for Sub-Pixel Boundary Mapping,
IfromI06(xx-yy).
PDF File. 0607
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Rexhepi, A.[Astrit], Mokhtarian, F.[Farzin],
Robust Moving Region Boundary Extraction Using Second Order Statistics,
SCIA07(828-837).
Springer DOI 0706
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Rexhepi, A.[Astrit], Mokhtarian, F.[Farzin],
Robust Boundary Delineation Using Random-Phase-Shift Active Contours,
SCIA07(411-420).
Springer DOI 0706
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Rexhepi, A.[Astrit], Mokhtarian, F.[Farzin], Rosenfeld, A.,
Sparse, variable-representation active contour models,
ICPR04(III: 683-686).
IEEE DOI 0409
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Ge, X.F.[Xing-Fei], Jie, T.[Tian],
An automatic active contour model for multiple objects,
ICPR02(II: 881-884).
IEEE DOI 0211
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Radeva, P.I., Vitria, J.,
Region-based Approach for Discriminant Snakes,
ICIP01(II: 801-804).
IEEE DOI 0108
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Pardo, X.M., Radeva, P.I., Villanueva, J.J.,
Self-training statistic snake for image segmentation and tracking,
CIAP99(406-411).
IEEE DOI 9909
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Larsen, O.V.[Ole Vilhelm], Radeva, P.I.[Petia I.], Martí, E.[Enric],
Guidelines for choosing optimal parameters of elasticity for snakes,
CAIP95(106-113).
Springer DOI 9509
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Larsen, O.V.[Ole V.], Radeva, P.I.[Petia I.], Martí, E.[Enric],
Bounds on the optimal elasticity parameters for a snake,
CIAP95(37-42).
Springer DOI 9509
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Radeva, P.I.[Petia I.], Martí, E.[Enric],
An improved model of snakes for model-based segmentation,
CAIP95(515-520).
Springer DOI 9509
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Radeva, P.I.[Petia I.], Serrat, J.[Joan], Marti, E.[Enric],
A Snake for Model-Based Segmentation,
ICCV95(816-821).
IEEE DOI Address convergence and attraction problems wiht a new potential field and external force specification. Use the model to specify the internal force. BibRef 9500