8.4.2 Watershed Algorithms, Watershed Segmentation

Chapter Contents (Back)
Watershed. Segmentation, Watershed.

Beucher, S.,
Watersheds of Functions and Picture Segmentation,
ICASSP82(1928-1931). The original watershed paper.
See also Segmentation Tools in Mathematical Morphology. BibRef 8200

Meyer, F., and Beucher, S.,
Morphological Segmentation,
JVCIR(1), No. 1, September 1990, pp. 21-46. Watershed technique. BibRef 9009

Meyer, F.,
Topographic Distance and Watershed Lines,
SP(38), No. 1, July 1994, pp. 113-125. BibRef 9407

Meyer, F.,
Flooding and Segmentation,
MMAISP00(189-198). BibRef 0001

Meyer, F.,
Stochastic watershed hierarchies,
ICAPR15(1-8)
IEEE DOI 1511
image segmentation BibRef

Watson, A.I.,
A New Method of Classification for Landsat Data Using the 'Watershed' Algorithm,
PRL(6), 1987, pp. 15-19. BibRef 8700

Vincent, L., and Soille, P.[Pierre],
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations,
PAMI(13), No. 6, June 1991, pp. 583-598.
IEEE DOI Description of Watershed techniques. BibRef 9106

Najman, L., Schmitt, M.,
Geodesic Saliency of Watershed Contours and Hierarchical Segmentation,
PAMI(18), No. 12, December 1996, pp. 1163-1173.
IEEE DOI 9701
Morphology. Morphology based segmentation, find the regions based on analysis of the contours.
See also Comments on Geodesic Saliency of Watershed Contours and Hierarchical Segmentation. BibRef

Najman, L., Schmitt, M.,
Watershed of a continuous function,
SP(38), No. 1, 1994, pp. 99-112. BibRef 9400

Cousty, J.[Jean], Couprie, M.[Michel], Najman, L.[Laurent], Bertrand, G.[Gilles],
Fusion Graphs: Merging Properties and Watersheds,
JMIV(30), No. 1, January 2008, pp. 87-104.
Springer DOI 0801
BibRef
Earlier: A1, A4, A2, A3:
Grayscale Watersheds on Perfect Fusion Graphs,
IWCIA06(60-73).
Springer DOI 0606
BibRef

Cousty, J.[Jean], Bertrand, G.[Gilles], Najman, L.[Laurent], Couprie, M.[Michel],
Watershed Cuts: Minimum Spanning Forests and the Drop of Water Principle,
PAMI(31), No. 8, August 2009, pp. 1362-1374.
IEEE DOI 0906
BibRef
Earlier:
On Watershed Cuts and Thinnings,
DGCI08(xx-yy).
Springer DOI 0804
Watersheds in edge-weighted graphs.
See also On Topological Watersheds. BibRef

Cousty, J.[Jean], Bertrand, G.[Gilles], Najman, L.[Laurent], Couprie, M.[Michel],
Watershed Cuts: Thinnings, Shortest Path Forests, and Topological Watersheds,
PAMI(32), No. 5, May 2010, pp. 925-939.
IEEE DOI 1003
Thinning paradigm for 3 watershed cut strategies. Parallel, sequential, and link to flooding algorithms. BibRef

Cousty, J.[Jean], Bertrand, G.[Gilles], Couprie, M.[Michel], Najman, L.[Laurent],
Collapses and Watersheds in Pseudomanifolds of Arbitrary Dimension,
JMIV(50), No. 3, November 2014, pp. 261-285.
Springer DOI
WWW Link. 1410
BibRef
Earlier:
Collapses and Watersheds in Pseudomanifolds,
IWCIA09(397-410).
Springer DOI 0911
BibRef

Cousty, J.[Jean], Najman, L.[Laurent],
Morphological floodings and optimal cuts in hierarchies,
ICIP14(4462-4466)
IEEE DOI 1502
DH-HEMTs BibRef

Cousty, J.[Jean], Najman, L.[Laurent], Serra, J.[Jean],
Raising in watershed lattices,
ICIP08(2196-2199).
IEEE DOI 0810
BibRef

Cousty, J.[Jean], Bertrand, G.[Gilles],
Uniqueness of the Perfect Fusion Grid on Zd,
JMIV(34), No. 3, July 2009, pp. xx-yy.
Springer DOI 0906
BibRef

Shafarenko, L., Petrou, M., Kittler, J.V.,
Automatic Watershed Segmentation of Randomly Textured Color Images,
IP(6), No. 11, November 1997, pp. 1530-1544.
IEEE DOI 9710
Color. Texture.
See also Histogram Based Segmentation in a Perceptually Uniform Color Space. BibRef

Moga, A.N.[Alina N.], Gabbouj, M.[Moncef],
Parallel Image Component Labeling with Watershed Transformation,
PAMI(19), No. 5, May 1997, pp. 441-450.
IEEE DOI 9705
Watershed. Delimit the extent of connected components locally (on each processor). Use pyramidal structure. Expand the regions out from minima until they colides with another. BibRef

Moga, A.N., Gabbouj, M.,
A Parallel Marker Based Watershed Transformation,
ICIP96(II: 137-140).
IEEE DOI BibRef 9600

Dorbin, B.P., Viero, T., Gabbouj, M.,
Fast Watershed Algorithms: Analysis and Extensions,
SPIE(2180), 1994, pp. 209-220. BibRef 9400

Moga, A.N.,
Parallel Watershed Algorithms for Image Segmentation,
Ph.D.Thesis, Tampere Univ. of Tech., Finland, February 1997. BibRef 9702

Moga, A.N., Cramariuc, B., Gabbouj, M.,
An Efficient Watershed Segmentation Algorithm Suitable for Parallel Implementation,
ICIP95(II: 101-104).
IEEE DOI 9510
BibRef

Lemarechal, C., Fjortoft, R., Marthon, P., Cubero-Castan, E.,
Comments on Geodesic Saliency of Watershed Contours and Hierarchical Segmentation,
PAMI(20), No. 7, July 1998, pp. 762-766.
IEEE DOI 9808

See also Geodesic Saliency of Watershed Contours and Hierarchical Segmentation. BibRef

Schmitt, M.,
Response to the Comment on Geodesic Saliency of Watershed Contours and Hierarchical Segmentation,
PAMI(20), No. 7, July 1998, pp. 764-766.
IEEE DOI 9808
BibRef

Wang, D.M.[De-Min],
A Multiscale Gradient Algorithm for Image Segmentation Using Watersheds,
PR(30), No. 12, December 1997, pp. 2043-2052.
Elsevier DOI 9805
For Video application:
See also Unsupervised Video Segmentation Based on Watersheds and Temporal Tracking. BibRef

Gauch, J.M.[John M.],
Image Segmentation and Analysis via Multiscale Gradient Watershed Hierarchies,
IP(8), No. 1, January 1999, pp. 69-79.
IEEE DOI BibRef 9901

Haris, K., Efstratiadis, S.N., Maglaveras, N., Katsaggelos, A.K.,
Hybrid Image Segmentation Using Watersheds And Fast Region Merging,
IP(7), No. 12, December 1998, pp. 1684-1699.
IEEE DOI 9812
BibRef

Haris, K., Efstratiadis, S., Maglaveras, N.,
Hierarchical Image Segmentation Based on Contour Dynamics,
ICIP01(I: 54-57).
IEEE DOI 0108
BibRef

Haris, K., Efstratiadis, S.N., Maglaveras, N.,
Watershed-based image segmentation with fast region merging,
ICIP98(III: 338-342).
IEEE DOI 9810
BibRef

Pratikakis, I.E., Sahli, H., Cornelis, J.,
Low level image partitioning guided by the gradient watershed hierarchy,
SP(75), No. 2, 1 June 1999, pp. 173-195. BibRef 9906

Li, W.[Wei], Benie, G.B.[Goze B.], He, D.C.[Dong-Chen], Wang, S.R.[Sheng-Rui], Ziou, D.[Djemel], Gwyn, Q.H.J.[Q. Hugh J.],
Watershed-based hierarchical SAR image segmentation,
JRS(20), No. 17, November 1999, pp. 3377. BibRef 9911

Hansen, M.W., Higgins, W.E.,
Watershed-Based Maximum-Homogeneity Filtering,
IP(8), No. 7, July 1999, pp. 982-988.
IEEE DOI BibRef 9907
Earlier:
Image enhancement using watershed-based maximum homogeneity filtering,
ICIP95(I: 482-485).
IEEE DOI 9510
BibRef

Arcelli, C.[Carlo],
Topological changes in grey-tone digital pictures,
PR(32), No. 6, June 1999, pp. 1019-1023.
Elsevier DOI Reduce to a few regions. BibRef 9906

Bleau, A.[Andrè], Leon, L.J.[L. Joshua],
Watershed-Based Segmentation and Region Merging,
CVIU(77), No. 3, March 2000, pp. 317-370.
DOI Link 0004
BibRef

Bieniek, A.[Andreas], Moga, A.,
An efficient watershed algorithm based on connected components,
PR(33), No. 6, June 2000, pp. 907-916.
Elsevier DOI 0004
BibRef

Bieniek, A.[Andreas], Burkhardt, H.[Hans], Marschner, H.[Heiko], Noolle, M.[Michael], Schreiber, G.[Gerald],
A Parallel Watershed Algorithm,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Pitas, I., Cotsaces, C.I.,
Memory Efficient Propagation-Based Watershed and Influence Zone Algorithms for Large Images,
IP(9), No. 7, July 2000, pp. 1185-1199.
IEEE DOI 0006
BibRef

Kim, J.B.[Jong-Bae], Kim, H.J.[Hang-Joon],
Multiresolution-based watersheds for efficient image segmentation,
PRL(24), No. 1-3, January 2003, pp. 473-488.
Elsevier DOI 0211
BibRef

Kim, J.B.[Jong Bae], Kim, H.J.[Hang Joon],
A wavelet-based watershed image segmentation for VOP generation,
ICPR02(III: 505-508).
IEEE DOI 0211
BibRef

Nguyen, H.T.[Hieu Tat], Worring, M.[Marcel], van den Boomgaard, R.[Rein],
Watersnakes: Energy-Driven Watershed Segmentation,
PAMI(25), No. 3, March 2003, pp. 330-342.
IEEE DOI 0301
BibRef
And: TR12, Intelligent Sensory Information Systems Group, Univ. of Amsterdam, 2000.
PS File. Combine energy approach with watershed approach by including (as energy) distance to the watershed. Impose a priori conditions (smoothness).
See also Tracking nonparameterized object contours in video. BibRef

Nguyen, H.T.[Hieu Tat], Ji, Q.A.[Qi-Ang],
Shape-Driven Three-Dimensional Watersnake Segmentation of Biological Membranes in Electron Tomography,
MedImg(27), No. 5, May 2008, pp. 616-628.
IEEE DOI 0711
BibRef
Earlier:
Improved watershed segmentation using water diffusion and local shape priors,
CVPR06(I: 985-992).
IEEE DOI 0606
BibRef

Malpica, N.[Norberto], Ortuño, J.E.[Juan E.], Santos, A.[Andrés],
A multichannel watershed-based algorithm for supervised texture segmentation,
PRL(24), No. 9-10, June 2003, pp. 1545-1554.
Elsevier DOI 0304
BibRef

Vanhamel, I., Pratikakis, I.E., Sahli, H.,
Multiscale gradient watersheds of color images,
IP(12), No. 6, June 2003, pp. 617-626.
IEEE DOI 0307
BibRef

Vanhamel, I., Pratikakis, I.E., Sahli, H.,
Multiscale Graph Theory Based Color Segmentation,
ICIP06(769-772).
IEEE DOI 0610
BibRef
Earlier: A1, A3, A2:
Nonlinear Multiscale Graph Theory based Segmentation of Color Images,
ICPR06(II: 407-411).
IEEE DOI 0609
BibRef
Earlier: A1, A2, A3:
Hierarchical segmentation using dynamics of multi-scale color gradient watersheds,
ScaleSpace01(xx-yy). 0106
BibRef

Pratikakis, I.E., Sahli, H., Cornelis, J.,
Hierarchical Segmentation Using Dynamics of Multiscale Gradient Watersheds,
SCIA99(Image Analysis). BibRef 9900
Earlier:
Hierarchy Determination of the Gradient Watershed Adjacent Groups,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Katartzis, A., Vanhamel, I., Sahli, H.,
A Hierarchical Markovian Model for Multiscale Region-Based Classification of Vector-Valued Images,
GeoRS(43), No. 3, March 2005, pp. 548-558.
IEEE Abstract. 0501
BibRef

Vanhamel, I., Katartzis, A., Sahli, H.,
Hierarchical segmentation via a diffusion scheme in color/texture feature space,
ICIP03(I: 969-972).
IEEE DOI 0312
BibRef

Hill, P.R., Canagarajah, C.N., Bull, D.R.,
Image segmentation using a texture gradient based watershed transform,
IP(12), No. 12, December 2003, pp. 1618-1633.
IEEE DOI 0402
BibRef

Nieniewski, M.,
Extraction of Diffuse Objects From Images by Means of Watershed and Region Merging: Example of Solar Images,
SMC-B(34), No. 1, February 2004, pp. 796-801.
IEEE Abstract. 0403
Developed for segmentation of solar images obtained from extreme-UV imaging telescope (EIT). BibRef

Grau, V., Mewes, A.U.J., Alcaniz, M., Kikinis, R., Warfield, S.K.,
Improved watershed transform for medical image segmentation using prior information,
MedImg(23), No. 4, April 2004, pp. 447-458.
IEEE Abstract. 0406
BibRef

Rambabu, C., Chakrabarti, I., Mahanta, A.,
Flooding-based watershed algorithm and its prototype hardware architecture,
VISP(151), No. 3, June 2004, pp. 224-234.
IEEE Abstract. 0409
BibRef

Tsai, Y.P.[Yu-Pao], Lai, C.C.[Chih-Chuan], Hung, Y.P.[Yi-Ping], Shih, Z.C.,
A Bayesian Approach to Video Object Segmentation via Merging 3-D Watershed Volumes,
CirSysVideo(15), No. 1, January 2005, pp. 175-180.
IEEE Abstract. 0501
BibRef
Earlier: A3, A1, A2 only: ICPR02(I: 496-499).
IEEE DOI 0211
BibRef

Makrogiannis, S., Economou, G., Fotopoulos, S., Bourbakis, N.G.,
Segmentation of Color Images Using Multiscale Clustering and Graph Theoretic Region Synthesis,
SMC-A(35), No. 2, March 2005, pp. 224-238.
IEEE Abstract. 0501
Combine region segmentation and clustering. Multi-scale clustering, then combine these regions into the final result. BibRef

Makrogiannis, S., Vanhamel, I., Sahli, H., Fotopoulos, S.,
Scale Space Segmentation of Color Images Using Watersheds and Fuzzy Region Merging,
ICIP01(I: 734-737).
IEEE DOI 0108
BibRef

Makrogiannis, S., Economou, G., Fotopoulos, S.,
Region oriented compression of color images using fuzzy inference and fast merging,
PR(35), No. 9, September 2002, pp. 1807-1820.
Elsevier DOI 0206
BibRef

Theoharatos, C., Pothos, V.K., Laskaris, N.A., Economou, G., Fotopoulos, S.,
Multivariate image similarity in the compressed domain using statistical graph matching,
PR(39), No. 10, October 2006, pp. 1892-1904.
Elsevier DOI 0606
Image similarity; Multivariate statistics; Graph matching; Minimal spanning tree (MST); Similarity measures; Discrete cosine transform (DCT); JPEG image compression; Image retrieval
See also Combining self-organizing neural nets with multivariate statistics for efficient color image retrieval.
See also local spectral distribution approach to face recognition, A. BibRef

Pothos, V.K.[Vasileios K.], Theoharatos, C.[Christos], Economou, G.[George], Ifantis, A.[Apostolos],
Texture retrieval based on a non-parametric measure for multivariate distributions,
CIVR07(502-509).
DOI Link 0707
BibRef

Makrogiannis, S., Schelkens, P., Fotopoulos, S., Cornelis, J.P.H.,
Region-oriented Compression of Color Images Using Fuzzy Inference and Shape Adaptive DCT,
ICIP01(III: 478-481).
IEEE DOI 0108
BibRef

Makrogiannis, S., Theoharatos, C., Economou, G., Fotopoulos, S.,
Color image segmentation using multiscale fuzzy C-means and graph theoretic merging,
ICIP03(I: 985-988).
IEEE DOI 0312
BibRef

Makrogiannis, S., Economou, G., Fotopoulos, S.,
A region dissimilarity relation that combines feature-space and spatial information for color image segmentation,
SMC-B(35), No. 1, February 2005, pp. 44-53.
IEEE Abstract. 0501

See also Combining self-organizing neural nets with multivariate statistics for efficient color image retrieval. BibRef

Patino, L.[Luis],
Fuzzy relations applied to minimize over segmentation in watershed algorithms,
PRL(26), No. 6, 1 May 2005, pp. 819-828.
Elsevier DOI 0501
BibRef

Jung, C.R.[Cláudio Rosito], Scharcanski, J.[Jacob],
Robust watershed segmentation using wavelets,
IVC(23), No. 7, 1 July 2005, pp. 661-669.
Elsevier DOI 0506
BibRef

Jung, C.R.[Cláudio Rosito],
Combining wavelets and watersheds for robust multiscale image segmentation,
IVC(25), No. 1, January 2007, pp. 24-33.
Elsevier DOI 0611
Segmentation; Watersheds; Wavelets; Multiresolution; Denoising; Region merging BibRef

Jung, C.R.[Cláudio Rosito],
Unsupervised multiscale segmentation of color images,
PRL(28), No. 4, 1 March 2007, pp. 523-533.
Elsevier DOI 0701
Watersheds; Wavelets; Multiresolution; Color images; Region merging BibRef

Zhao, C.G.[Chen Guang], Zhuang, T.G.[Tian Ge],
A hybrid boundary detection algorithm based on watershed and snake,
PRL(26), No. 9, 1 July 2005, pp. 1256-1265.
Elsevier DOI 0506
BibRef

Sun, H.[Han], Yang, J.Y.[Jing-Yu], Ren, M.[Mingwu],
A fast watershed algorithm based on chain code and its application in image segmentation,
PRL(26), No. 9, 1 July 2005, pp. 1266-1274.
Elsevier DOI 0506
BibRef

Beare, R.,
A Locally Constrained Watershed Transform,
PAMI(28), No. 7, July 2006, pp. 1063-1074.
IEEE DOI 0606
Introduce border constraints by modifying the path definition of the watershed transform. BibRef

Osma-Ruiz, V.[Víctor], Godino-Llorente, J.I.[Juan I.], Sáenz-Lechón, N.[Nicolás], Gómez-Vilda, P.[Pedro],
An improved watershed algorithm based on efficient computation of shortest paths,
PR(40), No. 3, March 2007, pp. 1078-1090.
Elsevier DOI 0611
Watershed; Image segmentation; Arrowing BibRef

de Carvalho, M.A.G.[Marco Antonio Garcia], de Alencar Lotufo, R.[Roberto], Couprie, M.[Michel],
Morphological segmentation of yeast by image analysis,
IVC(25), No. 1, January 2007, pp. 34-39.
Elsevier DOI 0611
Yeast; Watershed; Scale-space; Image analysis BibRef

Frucci, M.[Maria], Ramella, G.[Giuliana], Sanniti di Baja, G.[Gabriella],
Using resolution pyramids for watershed image segmentation,
IVC(25), No. 6, 1 June 2007, pp. 1021-1031.
Elsevier DOI 0704
Segmentation; Watershed transformation; Resolution pyramid
See also Shape and topology preserving multi-valued image pyramids for multi-resolution skeletonization. BibRef

Frucci, M.[Maria], Perner, P.[Petra], Sanniti di Baja, G.[Gabriella],
Watershed Segmentation Via Case-Based Reasoning,
BVAI07(244-253).
Springer DOI 0710
BibRef

Ramella, G.[Giuliana], Sanniti di Baja, G.[Gabriella],
Detecting Foreground Components in Grey Level Images for Shift Invariant and Topology Preserving Pyramids,
ICIAR04(I: 57-64).
Springer DOI 0409
BibRef

Frucci, M.[Maria], di Baja, G.S.[Gabriella Sanniti],
A New Algorithm for Image Segmentation via Watershed Transformation,
CIAP11(II: 168-177).
Springer DOI 1109
BibRef

Audigier, R.[Romaric], de Alencar Lotufo, R.[Roberto],
Uniquely-Determined Thinning of the Tie-Zone Watershed Based on Label Frequency,
JMIV(27), No. 2, February 2007, pp. 157-173.
Springer DOI 0704
BibRef

Levner, I.[Ilya], Zhang, H.[Hong],
Classification-Driven Watershed Segmentation,
IP(16), No. 5, May 2007, pp. 1437-1445.
IEEE DOI 0704
BibRef

Levner, I.[Ilya], Greiner, R.[Russell], Zhang, H.[Hong],
Supervised image segmentation via ground truth decomposition,
ICIP08(737-740).
IEEE DOI 0810
BibRef

Trieu, D.B.K.[Dang Ba Khac], Maruyama, T.[Tsutomu],
Real-time image segmentation based on a parallel and pipelined watershed algorithm,
RealTimeIP(2), No. 4, December 2007, pp. 319-329.
Springer DOI 0712
BibRef

Trieu, D.B.K.[Dang Ba Khac], Maruyama, T.[Tsutomu],
Real-time color image segmentation based on mean shift algorithm using an FPGA,
RealTimeIP(10), No. 2, June 2015, pp. 345-356.
WWW Link. 1506
BibRef

Sofou, A., Maragos, P.[Petros],
Generalized Flooding and Multicue PDE-Based Image Segmentation,
IP(17), No. 3, March 2008, pp. 364-376.
IEEE DOI 0802
BibRef
Earlier:
PDE-based modeling of image segmentation using volumic flooding,
ICIP03(II: 431-434).
IEEE DOI 0312
BibRef

Meyer, F.[Fernand], Maragos, P.[Petros],
Multiscale Morphological Segmentations based on Watershed, Flooding, and Eikonal PDE,
ScaleSpace99(351-362). BibRef 9900

Meyer, F.[Fernand],
Watershed Segmentation with a Controlled Precision,
ISMM17(83-94).
Springer DOI 1706
BibRef
Earlier:
The Waterfall Hierarchy on Weighted Graphs,
ISMM15(325-336).
Springer DOI 1506
BibRef
And:
Flooding Edge on Node Weighted Graphs,
ISMM13(341-352).
Springer DOI 1305
BibRef

Meyer, F.[Fernand],
A Watershed Algorithm Progressively Unveiling Its Optimality,
ISMM15(717-728).
Springer DOI 1506
BibRef

Meyer, F.[Fernand], Lerallut, R.[Romain],
Morphological Operators for Flooding, Leveling and Filtering Images Using Graphs,
GbRPR07(158-167).
Springer DOI 0706
BibRef

Peter, Z., Bousson, V., Bergot, C., Peyrin, F.,
A constrained region growing approach based on watershed for the segmentation of low contrast structures in bone micro-CT images,
PR(41), No. 7, July 2008, pp. 2358-2368.
Elsevier DOI 0804
Image segmentation; Anisotropic diffusion; Bones; X-ray tomography BibRef

Dagher, I.[Issam], El Tom, K.[Kamal],
WaterBalloons: A hybrid watershed Balloon Snake segmentation,
IVC(26), No. 7, 2 July 2008, pp. 905-912.
Elsevier DOI 0804
Watershed; Active contour model; Snake balloon; Segmentation; WaterBalloons BibRef

Eom, S.[Seongeun], Shin, V.[Vladimir], Ahn, B.[Byungha],
Cellular Watersheds: A Parallel Implementation of the Watershed Transform on the CNN Universal Machine,
IEICE(E90-D), No. 4, April 2007, pp. 791-794.
DOI Link 0704
BibRef

Rao, A.R.[A. Ramachandra], Srinivas, V.V.,
Regionalization of Watersheds: An Approach Based on Cluster Analysis,
Springer2008, ISBN: 978-1-4020-6851-5
WWW Link. Buy this book: Regionalization of Watersheds: An Approach Based on Cluster Analysis (Water Science and Technology Library) (Water Science and Technology Library) BibRef 0800

Hamarneh, G.[Ghassan], Li, X.X.[Xiao-Xing],
Watershed segmentation using prior shape and appearance knowledge,
IVC(27), No. 1-2, January 2009, pp. 59-68.
Elsevier DOI 0811
BibRef
Earlier: A2, A1:
Modeling Prior Shape and Appearance Knowledge in Watershed Segmentation,
CRV05(27-33).
IEEE DOI 0505
Watershed transformation; Prior shape knowledge; Segmentation; k-Means clustering Medical applications. BibRef

Rittner, L.[Leticia], Flores, F.C.[Franklin César], de Alencar Lotufo, R.[Roberto],
A tensorial framework for color images,
PRL(31), No. 4, 1 March 2010, pp. 277-296.
Elsevier DOI 1002
Color image; Gradient; Tensor; Mathematical morphology; Watershed transform; Segmentation BibRef

de Smet, P.[Patrick],
Optimized high speed pixel sorting and its application in watershed based image segmentation,
PR(43), No. 7, July 2010, pp. 2359-2366.
Elsevier DOI 1003
Pixel sorting; Watershed; Optimization BibRef

Audigier, R.[Romaric], de Alencar Lotufo, R.[Roberto],
Relationships between some watershed definitions and their tie-zone transforms,
IVC(28), No. 10, October 2010, pp. 1472-1482.
Elsevier DOI 1007
BibRef

Flores, F.C.[Franklin Cesar], de Alencar Lotufo, R.[Roberto],
Watershed from propagated markers: An interactive method to morphological object segmentation in image sequences,
IVC(28), No. 11, November 2010, pp. 1491-1514.
Elsevier DOI 1008
Morphological segmentation; Image sequences; Marker propagation; Motion estimation; Watershed from markers; Interactive segmentation BibRef

Li, D., Zhang, G.F.[Gui-Feng], Wu, Z.C.[Zhao-Cong], Yi, L.[Lina],
An Edge Embedded Marker-Based Watershed Algorithm for High Spatial Resolution Remote Sensing Image Segmentation,
IP(19), No. 10, October 2010, pp. 2781-2787.
IEEE DOI 1003
BibRef
Earlier: A2, A3, A4, Only:
Marker-based watershed segmentation embedded with edge information,
IASP10(375-380).
IEEE DOI 1004
BibRef

Yi, L.[Lina], Zhang, G.F.[Gui-Feng], Wu, Z.C.[Zhao-Cong],
A Scale-Synthesis Method for High Spatial Resolution Remote Sensing Image Segmentation,
GeoRS(50), No. 10, October 2012, pp. 4062-4070.
IEEE DOI 1210
BibRef

Derivaux, S.[Sébastien], Forestier, G.[Germain], Wemmert, C.[Cédric], Lefevre, S.,
Supervised image segmentation using watershed transform, fuzzy classification and evolutionary computation,
PRL(31), No. 15, 1 November 2010, pp. 2364-2374.
Elsevier DOI 1003
Supervised image segmentation; Watershed transform; Fuzzy classification; Genetic algorithm BibRef

Li, F.[Fang], Ng, M.K.[Michael K.], Zeng, T.Y.[Tie Yong], Shen, C.L.[Chun-Li],
A Multiphase Image Segmentation Method Based on Fuzzy Region Competition,
SIIMS(3), No. 3, 2010, pp. 277-299.
DOI Link multiphase; image segmentation; region competition; fuzzy membership function; alternative minimization BibRef 1000

Cai, X.H.[Xiao-Hao], Chan, R.[Raymond], Zeng, T.Y.[Tie-Yong],
A Two-Stage Image Segmentation Method Using a Convex Variant of the Mumford-Shah Model and Thresholding,
SIIMS(6), No. 1, 2013, pp. 368-390.
DOI Link 1304

See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Li, X.[Xu], Yang, X.P.[Xiao-Ping], Zeng, T.Y.[Tie-Yong],
A Three-Stage Variational Image Segmentation Framework Incorporating Intensity Inhomogeneity Information,
SIIMS(13), No. 3, 2020, pp. 1692-1715.
DOI Link 2010
BibRef

Cai, X.H.[Xiao-Hao],
Variational image segmentation model coupled with image restoration achievements,
PR(48), No. 6, 2015, pp. 2029-2042.
Elsevier DOI 1503
Image segmentation BibRef

Duan, Y.P.[Yu-Ping], Chang, H.B.[Hui-Bin], Huang, W.M.[Wei-Min], Zhou, J.Y.[Jia-Yin], Lu, Z.K.[Zhong-Kang], Wu, C.L.[Chun-Lin],
The L_0 Regularized Mumford-Shah Model for Bias Correction and Segmentation of Medical Images,
IP(24), No. 11, November 2015, pp. 3927-3938.
IEEE DOI 1509
BibRef
Earlier: A1, A2, A3, A4, Only:
Simultaneous bias correction and image segmentation via L0 regularized Mumford-Shah model,
ICIP14(6-40)
IEEE DOI 1502
image segmentation. Biomedical imaging
See also Optimal Approximations by Piecewise Smooth Functions and Variational Problems. BibRef

Li, Y.T.[Yu-Tong], Wu, C.L.[Chun-Lin], Duan, Y.P.[Yu-Ping],
The TVp Regularized Mumford-Shah Model for Image Labeling and Segmentation,
IP(29), 2020, pp. 7061-7075.
IEEE DOI 2007
BibRef
Earlier: A1, A3, Only:
Piecewise Smooth Segmentation with Sparse Prior,
ICIP18(2217-2221)
IEEE DOI 1809
Image segmentation, TV, Labeling, Minimization, Image edge detection, Additives, Numerical models, Image labeling, segmentation. Mathematical model, Brain modeling, Nonhomogeneous media, ADMM BibRef

Duan, Y.P.[Yu-Ping], Huang, W.M.[Wei-Min], Zhou, J.Y.[Jia-Yin], Chang, H.B.[Hui-Bin], Zeng, T.Y.[Tie-Yong],
A Two-Stage Image Segmentation Method Using Euler's Elastica Regularized Mumford-Shah Model,
ICPR14(118-123)
IEEE DOI 1412
Approximation methods BibRef

Suphalakshmi, A., Narendran, S., Anandhakumar, P.,
An improved fast watershed algorithm for image segmentation,
IJCVR(1), No. 3, 2010, pp. 251-260.
DOI Link 1102
BibRef

Najman, L.[Laurent],
On the Equivalence Between Hierarchical Segmentations and Ultrametric Watersheds,
JMIV(40), No. 3, July 2011, pp. 231-247.
WWW Link. 1103
BibRef
Earlier:
Ultrametric Watersheds,
ISMM09(181-192).
Springer DOI 0908
BibRef

Couprie, C.[Camille], Grady, L.[Leo], Najman, L.[Laurent], Talbot, H.[Hugues],
Power Watershed: A Unifying Graph-Based Optimization Framework,
PAMI(33), No. 7, July 2011, pp. 1384-1399.
IEEE DOI 1106
BibRef
Earlier:
Anisotropic diffusion using power watersheds,
ICIP10(4153-4156).
IEEE DOI 1009
BibRef
Earlier:
Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest,
ICCV09(731-738).
IEEE DOI 0909
Extend graph-based techniques includes graph cuts, random walk, shortest path. BibRef

Couprie, C.[Camille], Grady, L.[Leo], Talbot, H.[Hugues], Najman, L.[Laurent],
Combinatorial Continuous Maximum Flow,
SIIMS(4), No. 3, 2011, pp. 905-930.
WWW Link. 1110
BibRef

Corcoran, P.[Padraig], Winstanley, A.[Adam], Mooney, P.[Peter],
Complementary texture and intensity gradient estimation and fusion for watershed segmentation,
MVA(22), No. 6, November 2011, pp. 1027-1045.
WWW Link. 1110
BibRef

Bernander, K.B.[Karl B.], Gustavsson, K.[Kenneth], Selig, B.[Bettina], Sintorn, I.M.[Ida-Maria], Hendriks, C.L.L.[Cris L. Luengo],
Improving the stochastic watershed,
PRL(34), No. 9, July 2013, pp. 993-1000.
Elsevier DOI 1305
Mathematical morphology; Image segmentation; Random process; Stochastic watershed; Seeded watershed; Uniform grid BibRef

Selig, B.[Bettina], Malmberg, F.[Filip], Hendriks, C.L.L.[Cris L. Luengo],
Fast Evaluation of the Robust Stochastic Watershed,
ISMM15(705-716).
Springer DOI 1506
BibRef
Earlier: A2, A1, A3:
Exact Evaluation of Stochastic Watersheds: From Trees to General Graphs,
DGCI14(309-319).
Springer DOI 1410
BibRef

Singh, P.P.[Pankaj Pratap], Garg, R.D.,
A Hybrid Approach for Information Extraction from High Resolution Satellite Imagery,
IJIG(13), No. 2, April 2013, pp. 1340007.
DOI Link 1308
marker-controlled watershed transforms and a nonlinear derivative method. BibRef

Golodetz, S.M., Nicholls, C., Voiculescu, I.D., Cameron, S.A.,
Two tree-based methods for the waterfall,
PR(47), No. 10, 2014, pp. 3276-3292.
Elsevier DOI 1406
Image segmentation BibRef

Meyer, F.[Fernand],
Watersheds on weighted graphs,
PRL(47), No. 1, 2014, pp. 72-79.
Elsevier DOI 1408
Watershed BibRef

Brun, L.[Luc], Vautrot, P.[Philippe], Meyer, F.[Fernand],
Hierarchical Watersheds with Inter-pixel Boundaries,
ICIAR04(I: 840-847).
Springer DOI 0409
BibRef

Malmberg, F.[Filip], Luengo Hendriks, C.L.[Cris L.],
An efficient algorithm for exact evaluation of stochastic watersheds,
PRL(47), No. 1, 2014, pp. 80-84.
Elsevier DOI 1408
Mathematical morphology BibRef

Baldacci, F.[Fabien], Braquelaire, A.[Achille],
Oriented boundary graph: An efficient structuring model for segmentation of 3D images,
CVIU(143), No. 1, 2016, pp. 92-103.
Elsevier DOI 1601
3D segmentation
See also Oriented Boundary Graph: A Framework to Design and Implement 3D Segmentation Algorithms. BibRef

Baldacci, F.[Fabien],
An Unbiased and Intervoxel Watershed Algorithm for 3D Image Segmentation,
ICIAR12(I: 330-337).
Springer DOI 1206

See also Oriented Boundary Graph: A Framework to Design and Implement 3D Segmentation Algorithms. BibRef

Hodneland, E., Tai, X.C., Kalisch, H.,
PDE Based Algorithms for Smooth Watersheds,
MedImg(35), No. 4, April 2016, pp. 957-966.
IEEE DOI 1604
biomedical optical imaging BibRef

Michaelsen, E.[Eckart],
Self-organizing maps and Gestalt organization as components of an advanced system for remotely sensed data: An example with thermal hyper-spectra,
PRL(83, Part 2), No. 1, 2016, pp. 169-177.
Elsevier DOI 1609
Hyperspectral data BibRef

González-Betancourt, A.[Aniel], Rodríguez-Ribalta, P.[Patricia], Meneses-Marcel, A.[Alfredo], Sifontes-Rodríguez, S.[Sergio], Lorenzo-Ginori, J.V.[Juan Valentín], Orozco-Morales, R.[Rubén],
Automated marker identification using the Radon transform for watershed segmentation,
IET-IPR(11), No. 3, March 2017, pp. 183-189.
DOI Link 1703
BibRef

Chahine, C.[Chaza], Vachier-Lagorre, C.[Corinne], Chenoune, Y.[Yasmina], El Berbari, R.[Racha], El Fawal, Z.[Ziad], Petit, E.[Eric],
Information fusion for unsupervised image segmentation using stochastic watershed and Hessian matrix,
IET-IPR(12), No. 4, April 2018, pp. 525-531.
DOI Link 1804
BibRef

Chahine, C.[Chaza], El Berbari, R.[Racha], Vachier-Lagorre, C.[Corinne], Nakib, A., Petit, E.[Eric],
Evidence theory for image segmentation using information from stochastic Watershed and Hessian filtering,
WSSIP15(141-144)
IEEE DOI 1603
filtering theory BibRef

Kakhani, N.[Nafiseh], Mokhtarzade, M.[Mehdi], Zouj, M.J.V.[Muhammad Javad Valadan],
Classification of very high-resolution remote sensing images by applying a new edge-based marker-controlled watershed segmentation method,
SIViP(13), No. 7, October 2019, pp. 1319-1327.
Springer DOI 1911
BibRef

Maia, D.S.[Deise Santana], Cousty, J.[Jean], Najman, L.[Laurent], Perret, B.[Benjamin],
Properties of combinations of hierarchical watersheds,
PRL(128), 2019, pp. 513-520.
Elsevier DOI 1912
BibRef

Fehri, A.[Amin], Velasco-Forero, S.[Santiago], Meyer, F.[Fernand],
Combinatorial space of watershed hierarchies for image characterization,
PRL(129), 2020, pp. 41-47.
Elsevier DOI 2001
Mathematical morphology, Hierarchies, Gromov-Hausdorff distance, Combination of hierarchies BibRef

Braham, Y.[Yosra], Elloumi, Y.[Yaroub], Akil, M.[Mohamed], Bedoui, M.H.[Mohamed Hedi],
Parallel computation of Watershed Transform in weighted graphs on shared memory machines,
RealTimeIP(17), No. 3, June 2020, pp. 527-542.
Springer DOI 2006
BibRef

Maia, D.S.[Deise Santana], Cousty, J.[Jean], Najman, L.[Laurent], Perret, B.[Benjamin],
Characterization of Graph-Based Hierarchical Watersheds: Theory and Algorithms,
JMIV(62), No. 5, June 2020, pp. 627-658.
Springer DOI 2007
BibRef

Yuan, Y., Zhu, Z., Yu, H., Zhang, W.,
Watershed-Based Superpixels With Global and Local Boundary Marching,
IP(29), 2020, pp. 7375-7388.
IEEE DOI 2007
Image color analysis, Image segmentation, Optimization, Real-time systems, Education, Clustering algorithms, Merging, boundary marching BibRef

Wolf, S.[Steffen], Bailoni, A.[Alberto], Pape, C.[Constantin], Rahaman, N.[Nasim], Kreshuk, A.[Anna], Köthe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning,
PAMI(43), No. 10, October 2021, pp. 3724-3738.
IEEE DOI 2109
BibRef
Earlier: A1, A2, A3, A4, A5, A6, A7:
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning,
ECCV18(II: 571-587).
Springer DOI 1810
Clustering algorithms, Partitioning algorithms, Image segmentation, Image edge detection, Merging, Correlation, convolutional neural networks BibRef

Wolf, S.[Steffen], Li, Y.Y.[Yu-Yan], Pape, C.[Constantin], Bailoni, A.[Alberto], Kreshuk, A.[Anna], Hamprecht, F.A.[Fred A.],
The Semantic Mutex Watershed for Efficient Bottom-up Semantic Instance Segmentation,
ECCV20(VI:208-224).
Springer DOI 2011
BibRef

Cerrone, L.[Lorenzo], Zeilmann, A.[Alexander], Hamprecht, F.A.[Fred A.],
End-To-End Learned Random Walker for Seeded Image Segmentation,
CVPR19(12551-12560).
IEEE DOI 2002
BibRef

Wolf, S., Schott, L., Köthe, U.[Ullrich], Hamprecht, F.A.[Fred A.],
Learned Watershed: End-to-End Learning of Seeded Segmentation,
ICCV17(2030-2038)
IEEE DOI 1802
computerised tomography, image segmentation, learning (artificial intelligence), medical image processing, Training BibRef

Xia, J.H.[Jian-Hua], Zhang, J.B.[Jin-Bing], Wang, Y.[Yang], Han, L.X.[Li-Xin], Yan, H.[Hong],
WC-KNNG-PC: Watershed clustering based on k-nearest-neighbor graph and Pauta Criterion,
PR(121), 2022, pp. 108177.
Elsevier DOI 2109
Watershed clustering, -nearest neighbor graph (KNNG), Pauta criterion, Shared nearest neighbor (SNN) BibRef

Chabardès, T.[Théodore], Dokládal, P.[Petr], Faessel, M.[Matthieu], Bilodeau, M.[Michel],
A Parallel, O(n) Algorithm for an Unbiased, Thin Watershed,
IPOL(12), 2022, pp. 50-71.
DOI Link 2204
Code, Watershed. BibRef
Earlier:
A parallel, O(N) algorithm for unbiased, thin watershed,
ICIP16(2569-2573)
IEEE DOI 1610
Computer architecture BibRef

Soor, S.[Sampriti], Sagar, B.S.D.[B. S. Daya],
Segmentation of Multi-Band Images Using Watershed Arcs,
SPLetters(29), 2022, pp. 2407-2411.
IEEE DOI 2212
Image segmentation, Image edge detection, Costs, Merging, Target recognition, Pattern recognition, Object detection, region merging BibRef

Mahmoudi, R.[Ramzi],
Ventricular segmentation and modeling using topological watershed transformation and harmonic state model,
IJIST(33), No. 2, 2023, pp. 680-700.
DOI Link 2303
cardiac, harmonic model, Kalman filter, modeling, MRI, right ventricle, segmentation, topological, watershed BibRef

Lebon, Q.[Quentin], Lefèvre, J.[Josselin], Cousty, J.[Jean], Perret, B.[Benjamin],
Interactive Segmentation with Incremental Watershed Cuts,
CIARP23(I:189-200).
Springer DOI 2312
BibRef

Figliuzzi, B.[Bruno], Chang, K.W.[Kai-Wen], Faessel, M.[Matthieu],
Hierarchical Segmentation Based Upon Multi-resolution Approximations and the Watershed Transform,
ISMM17(185-195).
Springer DOI 1706
BibRef