8.7.1.5 Geodesic Active Contours

Chapter Contents (Back)
Snakes. Geodesic Active Contours. Minimal path or geodesic methods.

Sapiro, G.[Guillermo],
Color Snakes,
CVIU(68), No. 2, November 1997, pp. 247-253.
DOI Link 9712
BibRef
Earlier:
Vector (Self) Snakes: Vector (self) snakes: A Geometric framework for Color, Texture and Multiscale Image Segmentation,
ICIP96(I: 817-820).
IEEE DOI 9610
BibRef

Caselles, V.[Vicent], Catté, F., Coll, T., and Dibos, F.,
A Geometric Model for Active Contours in Image Processing,
NumMath(66), 1993, pp. 1-31. BibRef 9300

Caselles, V.,
Geometric models for active contours,
ICIP95(III: 9-12).
IEEE DOI 9510
BibRef

Caselles, V.[Vicent], Kimmel, R.[Ron], Sapiro, G.[Guillermo],
Geodesic Active Contours,
IJCV(22), No. 1, February 1997, pp. 61-79.
DOI Link BibRef 9702
Earlier: ICCV95(694-699).
IEEE DOI Award, ICCV Test of Time. Geodesic -- geometric formulation, not energy approach. BibRef

Caselles, V.[Vicent], Facciolo, G.[Gabriele], Meinhardt, E.[Enric],
Anisotropic Cheeger Sets And Applications,
SIIMS(2), No. 4, 2009, pp. 1211-1254.
DOI Link 1002
Cheeger sets; anisotropic total variation; active contours; edge linking BibRef

Goldenberg, R.[Roman], Kimmel, R.[Ron], Rivlin, E.[Ehud], Rudzsky, M.[Michael],
Fast geodesic active contours,
IP(10), No. 10, October 2001, pp. 1467-1475.
IEEE DOI 0110
BibRef
Earlier: ScaleSpace99(34-45). BibRef

Sapiro, G.[Guillermo],
Vector-Valued Active Contours,
CVPR96(680-685).
IEEE DOI BibRef 9600

Sapiro, G.[Guillermo],
From Active Contours to Anisotropic Diffusion: Connections Between Basic PDE's in Image Processing,
ICIP96(I: 477-480).
IEEE DOI BibRef 9600

Aubert, G.[Gilles], Blanc-Féraud, L.[Laure],
Some Remarks on the Equivalence between 2D and 3D Classical Snakes and Geodesic Active Contours,
IJCV(34), No. 1, September-October 1999, pp. 19-28.
DOI Link BibRef 9909

Paragios, N.[Nikos], Deriche, R.[Rachid],
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects,
PAMI(22), No. 3, March 2000, pp. 266-280.
IEEE DOI 0005
BibRef
And: Corrections: PAMI(22), No. 4, April 2000, pp. 415.
IEEE DOI BibRef
Earlier:
Geodesic Active Regions for Motion Estimation and Tracking,
ICCV99(688-694).
IEEE DOI
PS File. BibRef
And: INRIA--RR-3631, 1999.
HTML Version.
See also Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation. BibRef

Paragios, N.[Nikos], Deriche, R.[Rachid],
Geodesic active regions and level set methods for motion estimation and tracking,
CVIU(97), No. 3, March 2005, pp. 259-282.
Elsevier DOI 0412
BibRef

Paragios, N.[Nikos],
Geodesic Active Regions and Level Set Methods: Contributions and Applications in Artificial Vision,
Ph.D.thesis, January 2000. School of Computer Engineering of University of Nice / Sophia Antipolis.
PS File. BibRef 0001

Paragios, N., and Deriche, R.,
Detecting Multiple Moving Targets Using Deformable Contours,
ICIP97(II: 183-186).
IEEE DOI
PS File. BibRef 9700

Paragios, N.[Nikos], Deriche, R.[Rachid],
Unifying Boundary and Region-based Information for Geodesic Active Tracking,
CVPR99(II: 300-305).
IEEE DOI
PS File. BibRef 9900

Paragios, N.[Nikolaos], and Deriche, R.[Rachid],
A PDE-Based Level-Set Approach for Detection and Tracking of Moving Objects,
ICCV98(1139-1145).
IEEE DOI
PS File. BibRef 9800

Paragios, N.[Nikos], Deriche, R.[Rachid],
Geodesic Active Regions: A New Framework to Deal with Frame Partition Problems in Computer Vision,
JVCIR(13), No. 1/2, March/June 2002, pp. 249-268.
DOI Link 0204
Active contours and level sets. BibRef

Deriche, R., Bouvin, S.[Stephane], Faugeras, O.D.,
Front Propagation and Level-Set Approach for Geodesic Active Stereovision,
ACCV98(xx-yy).
PS File. BibRef 9800
And: VS98(Image Processing for Visual Surveillance).
PS File. BibRef

Paragios, N.[Nikos], Mellina-Gotardo, O.[Olivier], Ramesh, V.[Visvanathan],
Gradient Vector Flow Fast Geodesic Active Contours,
PAMI(26), No. 3, March 2004, pp. 402-407.
IEEE Abstract. 0402
BibRef
Earlier: ICCV01(I: 67-73).
IEEE DOI 0106
BibRef

Paragios, N.[Nikos], Deriche, R.[Rachid],
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation,
IJCV(46), No. 3, February-March 2002, pp. 223-247.
DOI Link 0202

See also Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects. Combine level set method with active contours. BibRef

Paragios, N., Deriche, R.,
Geodesic Active Regions for Supervised Texture Segmentation,
ICCV99(926-932).
IEEE DOI
PS File. BibRef 9900
And:
Geodesic Active Regions for Texture Segmentation,
INRIA--RR-3440, 1998.
HTML Version. BibRef
Earlier:
Geodesic Active Contours for Supervised Texture Segmentation,
CVPR99(II: 422-427).
IEEE DOI
PS File. BibRef

Paragios, N., Deriche, R.,
Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach,
ECCV00(II: 224-240).
Springer DOI 0003
BibRef

Appleton, B.[Ben], Talbot, H.[Hugues],
Globally Optimal Geodesic Active Contours,
JMIV(23), No. 1, July 2005, pp. 67-86.
Springer DOI 0505
BibRef

Appleton, B.[Ben], Talbot, H.[Hugues],
Efficient and Consistent Recursive Filtering of Images with Reflective Extension,
ScaleSpace03(699-712).
Springer DOI 0310
BibRef

Appleton, B.[Ben], Talbot, H.[Hugues],
Globally Minimal Surfaces by Continuous Maximal Flows,
PAMI(28), No. 1, January 2006, pp. 106-118.
IEEE DOI 0512
BibRef

Pujol, O.[Oriol], Gil, D.[Debora], Radeva, P.I.[Petia I.],
Fundamentals of Stop and Go active models,
IVC(23), No. 8, 1 August 2005, pp. 681-691.
Elsevier DOI 0508
Snake formulation. decouples regularity and convergence BibRef

Pujol, O.[Oriol], Radeva, P.I.[Petia I.],
Texture Segmentation By Statistical Deformable Models,
IJIG(4), No. 3, July 2004, pp. 433-452. 0407
BibRef

Niethammer, M.[Marc], Vela, P.A.[Patricio A.], Tannenbaum, A.[Allen],
On the Evolution of Vector Distance Functions of Closed Curves,
IJCV(65), No. 1-2, November 2005, pp. 5-27.
Springer DOI 0604
BibRef

Niethammer, M.[Marc], Vela, P.A.[Patricio A.], Tannenbaum, A.[Allen],
Geometric Observers for Dynamically Evolving Curves,
PAMI(30), No. 6, June 2008, pp. 1093-1108.
IEEE DOI 0804
Visual tracking based on non-parametric implicit, level-set, curve descriptions. Interpolation and filtering with correspondence. BibRef

Niethammer, M., Tannenbaum, A.,
Dynamic geodesic snakes for visual tracking,
CVPR04(I: 660-667).
IEEE DOI 0408
BibRef

Dambreville, S.[Samuel], Sandhu, R.[Romeil], Yezzi, A.J.[Anthony J.], Tannenbaum, A.[Allen],
A Geometric Approach To Joint 2d Region-Based Segmentation and 3D Pose Estimation Using A 3d Shape Prior,
SIIMS(3), No. 1, 2010, pp. 110-132. region-based segmentation and tracking; three-dimensional pose estimation; three-dimensional shape prior; variational methods; differential geometry
DOI Link BibRef 1000
Earlier:
Robust 3D Pose Estimation and Efficient 2D Region-Based Segmentation from a 3D Shape Prior,
ECCV08(II: 169-182).
Springer DOI 0810
Active contour related. BibRef

Dambreville, S., Tannenbaum, A., Yezzi, A.J., Niethammer, M.,
A variational framework combining level-sets and thresholding.,
BMVC07(xx-yy).
PDF File. 0709
BibRef

Melonakos, J.[John], Pichon, E.[Eric], Angenent, S.[Sigurd], Tannenbaum, A.[Allen],
Finsler Active Contours,
PAMI(30), No. 3, March 2008, pp. 412-423.
IEEE DOI 0801
Augmenting the conformal (or geodesic) active contour framework with directional information. Apply to road extraction. BibRef

Mohan, V., Melonakos, J., Niethammer, M., Kubicki, M., Tannenbaum, A.,
Finsler Level Set Segmentation for Imagery in Oriented Domains,
BMVC07(xx-yy).
PDF File. 0709

See also Locally-Constrained Region-Based Methods for DW-MRI Segmentation. BibRef

Chen, L.[Li], Zhou, Y.[Yue], Wang, Y.G.[Yong-Gang], Yang, J.[Jie],
GACV: Geodesic-Aided C-V method,
PR(39), No. 7, July 2006, pp. 1391-1395.
Elsevier DOI Deformable model; Active contour; C-V method; Geodesic active contours 0606
BibRef
Earlier: A1, A2, A3, Only:
A Novel Color C-V Method and Its Application,
ICIAR05(40-47).
Springer DOI 0509
Chan and Vese
See also Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model, A. BibRef

Sagiv, C., Sochen, N.A., Zeevi, Y.Y.,
Integrated Active Contours for Texture Segmentation,
IP(15), No. 6, June 2006, pp. 1633-1646.
IEEE DOI 0606
BibRef
Earlier:
Geodesic active contours applied to texture feature space,
ScaleSpace01(xx-yy). 0106
BibRef

Papandreou, G., Maragos, P.,
Multigrid Geometric Active Contour Models,
IP(16), No. 1, January 2007, pp. 229-240.
IEEE DOI 0701
BibRef
Earlier:
A fast multigrid implicit algorithm for the evolution of geodesic active contours,
CVPR04(II: 689-694).
IEEE DOI 0408
BibRef

Pi, L.[Ling], Fan, J.S.[Jin-Song], Shen, C.M.[Chao-Min],
Color Image Segmentation for Objects of Interest with Modified Geodesic Active Contour Method,
JMIV(27), No. 1, January 2007, pp. 51-57.
Springer DOI 0702
BibRef

Pi, L.[Ling], Shen, C.M.[Chao-Min], Li, F.[Fang], Fan, J.S.[Jin-Song],
A variational formulation for segmenting desired objects in color images,
IVC(25), No. 9, 1 September 2007, pp. 1414-1421.
Elsevier DOI 0707
Active contours; Chan-Vese model; Desired objects; Discrimination function
See also Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model, A. BibRef

Fang, W.[Wen], Chan, K.L.[Kap Luk],
Incorporating shape prior into geodesic active contours for detecting partially occluded object,
PR(40), No. 8, August 2007, pp. 2163-2172.
Elsevier DOI 0704
BibRef
Earlier:
Learning a New Statistical Shape Prior Model for Object Detection by Geodesic Active Contours,
AVSBS06(42-42).
IEEE DOI 0611
BibRef
And:
Using Statistical Shape Priors in Geodesic Active Contours for Robust Object Detection,
ICPR06(II: 304-307).
IEEE DOI 0609
Geodesic active contours; Shape prior; Object detection BibRef

Wang, J.Y.[Jun-Yan], Chan, K.L.[Kap Luk],
Incorporating Patch Subspace Model in Mumford-Shah Type Active Contours,
IP(22), No. 11, 2013, pp. 4473-4485.
IEEE DOI 1310
convergence BibRef

Wang, J.Y.[Jun-Yan], Chan, K.L.[Kap Luk],
Active Contour with a Tangential Component,
JMIV(51), No. 2, February 2015, pp. 229-247.
WWW Link. 1503
BibRef

Wang, J.Y.[Jun-Yan], Yeung, S.K.[Sai-Kit], Chan, K.L.[Kap Luk],
Matching-constrained active contours with affine-invariant shape prior,
CVIU(132), No. 1, 2015, pp. 39-55.
Elsevier DOI 1502
Automatic object segmentation BibRef

Hua, C.[Cui], Gao, L.Q.[Li-Qun],
Geodesic active contour, inertia and initial speed,
PRL(29), No. 16, 1 December 2008, pp. 2197-2205.
Elsevier DOI 0811
Image segmentation; Geodesic active contours; Gradient vector flow; Inertia force field; Initial speed BibRef

Mio, W.[Washington], Bowers, J.C.[John C.], Liu, X.W.[Xiu-Wen],
Shape of Elastic Strings in Euclidean Space,
IJCV(82), No. 1, April 2009, pp. xx-yy.
Springer DOI 0902
BibRef

Zheng, Y.[Ying], Li, G.Y.[Guang-Yao], Sun, X.H.[Xie-Hua], Zhou, X.M.[Xin-Min],
Geometric active contours without re-initialization for image segmentation,
PR(42), No. 9, September 2009, pp. 1970-1976.
Elsevier DOI 0905
Geometric active contours; GACV model; Cumani operator; Image segmentation BibRef

Zheng, Y.[Ying], Li, G.Y.[Guang-Yao], Sun, X.H.[Xie-Hua], Zhou, X.M.[Xin-Min],
A geometric active contour model without re-initialization for color images,
IVC(27), No. 9, 3 August 2009, pp. 1411-1417.
Elsevier DOI 0906
Squared local contrast; Deviation penalization term; The GACV model; Geometric active contour BibRef

Bunyak, F.[Filiz], Palaniappan, K.[Kannappan],
Efficient segmentation using feature-based graph partitioning active contours,
ICCV09(873-880).
IEEE DOI 0909
BibRef

Nath, S.K.[Sumit K.], Palaniappan, K.[Kannappan],
Fast Graph Partitioning Active Contours for Image Segmentation Using Histograms,
JIVP(2009), No. 2009, pp. xx-yy.
DOI Link 1002
BibRef
Earlier:
Adaptive Robust Structure Tensors for Orientation Estimation and Image Segmentation,
ISVC05(445-453).
Springer DOI 0512
BibRef

Bunyak, F., Palaniappan, K.[Kannappan], Nath, S.K.[Sumit K.], Seetharaman, G.,
Geodesic Active Contour Based Fusion of Visible and Infrared Video for Persistent Object Tracking,
WACV07(35-35).
IEEE DOI 0702
BibRef

Nath, S.K.[Sumit K.], Palaniappan, K.[Kannappan], Bunyak, F.[Filiz],
Accurate Spatial Neighborhood Relationships for Arbitrarily-Shaped Objects Using Hamilton-Jacobi GVD,
SCIA07(421-431).
Springer DOI 0706
BibRef

Shan, H.[Hao], Ma, J.W.[Jian-Wei],
Curvelet-based geodesic snakes for image segmentation with multiple objects,
PRL(31), No. 5, 1 April 2010, pp. 355-360.
Elsevier DOI 1002
Curvelets; Geometric snakes; Geodesic active contours; Image segmentation; Multiscale; Wavelets BibRef

Zhang, H.Z.[Huai-Zhong], Morrow, P.J.[Philip J.], McClean, S.[Sally], Saetzler, K.[Kurt],
A stability approach to convergence of curve evolution methods,
PRL(31), No. 13, 1 October 2010, pp. 1909-1918.
Elsevier DOI 1003
BibRef
Earlier:
Incorporating Feature Based Priors into the Geodesic Active Contour Model and its Application in Biomedical Imagery,
IMVIP07(67-74).
IEEE DOI 0709
Active contours; Curve evolution; Termination criterion; Energy functional Improves Geodesic Active Contour by using user information. BibRef

Zhang, H.Z.[Huai-Zhong], Morrow, P.J.[Philip J.], McClean, S.[Sally], Saetzler, K.[Kurt],
Coupling edge and region-based information for boundary finding in biomedical imagery,
PR(45), No. 2, February 2012, pp. 672-684.
Elsevier DOI 1110
Curve evolution; Boundary finding; Prior model; GAC; Multivariate model BibRef

Gai, J.D.[Jia-Ding], Stevenson, R.L.[Robert L.],
Robust contour tracking based on a coupling between geodesic active contours and conditional random fields,
JVCIR(22), No. 1, January 2011, pp. 33-47.
Elsevier DOI 1101
BibRef
Earlier:
Contour tracking based on a synergistic approach of geodesic active contours and conditional random fields,
ICIP10(2801-2804).
IEEE DOI 1009
Contour tracking; 3D conditional random field; Geodesic active contours; Level set methods; Variational inference; Belief propagation; Motion detection; Markov random field BibRef

Tao, W.B.[Wen-Bing], Tai, X.C.[Xue-Cheng],
Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization,
IVC(29), No. 8, July 2011, pp. 499-508.
Elsevier DOI 1108
Multiple piecewise constant; Active contour without edges; Geodesic active contour; Graph cuts; Image segmentation; Level set BibRef

Shan, H.[Hao], He, C.T.[Chang-Tao], Wang, N.[Na],
MCA aided geodesic active contours for image segmentation with textures,
PRL(45), No. 1, 2014, pp. 235-243.
Elsevier DOI 1407
Morphological component analysis diversity BibRef

Peyré, G.[Gabriel], Péchaud, M.[Mickael], Keriven, R.[Renaud], Cohen, L.D.[Laurent D.],
Geodesic Methods in Computer Vision and Graphics,
FTCGV(5), Issue 3-4, 2009, pp. 197-397.
DOI Link 1410
Published December 2010. BibRef

Yang, F.[Fang], Cohen, L.D.[Laurent D.],
Geodesic Distance and Curves Through Isotropic and Anisotropic Heat Equations on Images and Surfaces,
JMIV(55), No. 2, June 2016, pp. 210-228.
WWW Link. 1604
BibRef

Chen, D.[Da], Zhu, J.[Jian], Zhang, X.X.[Xin-Xin], Shu, M.L.[Ming-Lei], Cohen, L.D.[Laurent D.],
Geodesic Paths for Image Segmentation With Implicit Region-Based Homogeneity Enhancement,
IP(30), 2021, pp. 5138-5153.
IEEE DOI 2106
Image segmentation, Measurement, Computational modeling, Active contours, Image edge detection, Mathematical model, Shape, interactive image segmentation BibRef

Chen, D.[Da], Mirebeau, J.M.[Jean-Marie], Shu, H.Z.[Hua-Zhong], Cohen, L.D.[Laurent D.],
A Region-Based Randers Geodesic Approach for Image Segmentation,
IJCV(132), No. 2, February 2024, pp. 349-391.
Springer DOI 2402
BibRef
Earlier: A1, A2, A4, Only:
A New Finsler Minimal Path Model with Curvature Penalization for Image Segmentation and Closed Contour Detection,
CVPR16(355-363)
IEEE DOI 1612
BibRef
Earlier:
Global Minimum for Curvature Penalized Minimal Path Method,
BMVC15(xx-yy).
DOI Link 1601
Minimal path or geodesic methods. BibRef

Mirebeau, J.M.[Jean-Marie],
Efficient fast marching with Finsler metrics,
NumMath(126), No. 3, 2013, pp. 515-557.
Springer DOI BibRef 1300

Mirebeau, J.M.[Jean-Marie],
Anisotropic fast-marching on cartesian grids using lattice basis reduction,
NumAnal(52), No. 4, 2014, pp. 1573-1599.
DOI Link BibRef 1400

Mirebeau, J.M.[Jean-Marie],
Fast-Marching Methods for Curvature Penalized Shortest Paths,
JMIV(60), No. 6, July 2018, pp. 784-815.
WWW Link.
WWW Link. 1806
BibRef

Duits, R., Meesters, S.P.L., Mirebeau, J.M.[Jean-Marie], Portegies, J.M.[Jorg M.],
Optimal Paths for Variants of the 2D and 3D Reeds-Shepp Car with Applications in Image Analysis,
JMIV(60), No. 6, July 2018, pp. 816-848.
Springer DOI 1806
BibRef

Schenk, F.[Fabian], Aichinger, P.[Philipp], Roesner, I.[Imme], Urschler, M.[Martin],
Automatic high-speed video glottis segmentation using salient regions and 3D geodesic active contours,
BMVA(2015), No. 3, 2015, pp. 1-15.
PDF File. 1509
BibRef

Bekkers, E.J., Duits, R., Mashtakov, A., Sanguinetti, G.R.,
A PDE Approach to Data-Driven Sub-Riemannian Geodesics in SE(2),
SIIMS(8), No. 4, 2015, pp. 2740-2770.
DOI Link 1601
BibRef
Earlier:
Data-Driven Sub-Riemannian Geodesics in SE(2),
SSVM15(613-625).
Springer DOI 1506
BibRef

Singh, N.[Nikhil], Hinkle, J.[Jacob], Joshi, S.[Sarang], Fletcher, P.T.[P. Thomas],
Hierarchical Geodesic Models in Diffeomorphisms,
IJCV(117), No. 1, March 2016, pp. 70-92.
Springer DOI 1604
BibRef

Nardi, G.[Giacomo], Peyré, G.[Gabriel], Vialard, F.X.[François-Xavier],
Geodesics on Shape Spaces with Bounded Variation and Sobolev Metrics,
SIIMS(9), No. 1, 2016, pp. 238-274.
DOI Link 1604
BibRef

Haltakov, V.[Vladimir], Unger, C.[Christian], Ilic, S.[Slobodan],
Geodesic pixel neighborhoods for 2D and 3D scene understanding,
CVIU(148), No. 1, 2016, pp. 164-180.
Elsevier DOI 1606
BibRef
Earlier:
Geodesic pixel neighborhoods for multi-class image segmentation,
BMVC14(xx-yy).
HTML Version. 1410
Semantic segmentation BibRef

Rahmoun, S.[Somia], Mairesse, F.[Fabrice], Uji-i, H.[Hiroshi], Hofkens, J.[Johan], Sliwa, T.[Tadeusz],
Curve computation by geodesics and graph modelling for polymer analysis,
SIViP(11), No. 8, November 2017, pp. 1469-1476.
Springer DOI 1710
BibRef

Roberts, M.[Michael], Chen, K.[Ke], Irion, K.L.[Klaus L.],
A Convex Geodesic Selective Model for Image Segmentation,
JMIV(61), No. 4, May 2019, pp. 482-503.
Springer DOI 1904
BibRef

Yu, S.[Song], Yiquan, W.[Wu],
A morphological approach to piecewise constant active contour model incorporated with the geodesic edge term,
MVA(31), No. 4, April 2020, pp. Article28.
Springer DOI 2005
BibRef

Zhao, X.M.[Xue-Mei], Wang, H.J.[Hai-Jian], Wu, J.[Jun], Li, Y.[Yu], Zhao, S.J.[Shi-Jie],
Remote sensing image segmentation using geodesic-kernel functions and multi-feature spaces,
PR(104), 2020, pp. 107333.
Elsevier DOI 2005
Remote sensing, Image segmentation, Riemannian manifold, Manifold projection, Kernel function BibRef

Ma, J., He, J., Yang, X.,
Learning Geodesic Active Contours for Embedding Object Global Information in Segmentation CNNs,
MedImg(40), No. 1, January 2021, pp. 93-104.
IEEE DOI 2012
Active contours, Image segmentation, Level set, Geometry, Task analysis, Convolutional neural networks, level set function BibRef

Santana-Cedrés, D.[Daniel], Monzón, N.[Nelson], Álvarez, L.[Luis],
An Algorithm for 3D Curve Smoothing,
IPOL(11), 2021, pp. 37-55.
DOI Link 2103
Code, Curve Smoothing. BibRef

Ma, J.[Jun], Wang, D.[Dong], Wang, X.P.[Xiao-Ping], Yang, X.P.[Xiao-Ping],
A Characteristic Function-Based Algorithm for Geodesic Active Contours,
SIIMS(14), No. 3, 2021, pp. 1184-1205.
DOI Link 2108
BibRef

Chen, D.[Da], Mirebeau, J.M.[Jean-Marie], Shu, M.[Minglei], Tai, X.C.[Xue-Cheng], Cohen, L.D.[Laurent D.],
Geodesic Models With Convexity Shape Prior,
PAMI(45), No. 7, July 2023, pp. 8433-8452.
IEEE DOI 2306
BibRef
Earlier: A1, A5, A2, A4, Only:
An Elastica Geodesic Approach with Convexity Shape Prior,
ICCV21(6880-6889)
IEEE DOI 2203
Shape, Image segmentation, Mathematical models, Numerical models, Computational modeling, Measurement, Active contours, Geodesic. Measurement, Shape, Computed tomography, Gaussian noise, Segmentation, BibRef

Shan, H.[Hao],
CS-GAC: Compressively sensed geodesic active contours,
PR(146), 2024, pp. 110007.
Elsevier DOI 2311
Compressed sensing/compressive sampling, Geodesic active contours, Edge detection, Wavelet BibRef

Jin, Z.M.[Zheng-Meng], Wang, H.[Hao], Ng, M.K.[Michael K.], Min, L.H.[Li-Hua],
Regularized CNN with Geodesic Active Contour and Edge Predictor for Image Segmentation,
SIIMS(17), No. 4, 2024, pp. 2392-2417.
DOI Link 2501
BibRef

Huang, C.[Chao], Srivastava, A.[Anuj], Liu, R.J.[Rong-Jie],
Geo-FARM: Geodesic Factor Regression Model for Misaligned Pre-shape Responses in Statistical Shape Analysis,
CVPR21(11491-11500)
IEEE DOI 2111
Analytical models, Correlation, Data analysis, Monte Carlo methods, Shape, Imaging, Predictive models BibRef

Hansen, J.D.K., Lauze, F.,
Multiphase Local Mean Geodesic Active Regions,
ICPR18(3031-3036)
IEEE DOI 1812
Hidden Markov models, Image segmentation, Image edge detection, Labeling, Optimization, Standards, Computational modeling BibRef

Chen, D.[Da], Mirebeau, J.M.[Jean-Marie], Cohen, L.[Laurent],
Finsler Geodesics Evolution Model for Region based Active Contours,
BMVC16(xx-yy).
HTML Version. 1805
BibRef