8.4 Segmentation by Region Growing Techniques

Chapter Contents (Back)
Region Growing. Segmentation, Region Growing. Segmentation, Region Merging.
See also Superpixel Region Extraction, Region Growing.

Brice, C.R.[Claude R.], and Fennema, C.L.[Claude L.],
Scene Analysis Using Regions,
AI(1), No. 3-4, Fall 1970, pp. 205-226. BibRef 7000 CMetImAly77(79-100).
Elsevier DOI Segmentation, Region Growing. Segmentation, Edges. Recognize Blocks World. This paper was written when most researchers were concerned with analyzing scenes using edge representations. The line and region representation are combined by expanding the image by 2 in each direction so that image points have both indices odd. Boundaries are then formed by linking points in the grid where both indices are even. This method was designed to simplify the process of cutting regions, merging regions and determining the properties of regions as a whole. The basic region merging method given above is taken from this paper. An important criterion is that by merging two regions, the total boundary should be somewhat less than the total boundary length of the original regions. The second criterion is the strength of the boundaries between two regions. This paper also reports on using the regions for recognition of the block structures in the image. The work of Barrow and Popplestone (
See also Relational Descriptions in Picture Processing. ) is a special case of the region growing method of this one. BibRef

Yachida, M.[Masahiko], Tsuji, S.[Saburo],
Application of Color Information to Visual Perception,
PR(3), No. 3, October 1971, pp. 307-318.
Elsevier DOI Color. Segmentation, Color. Color added to region growing. BibRef 7110

Pavlidis, T.[Theodosios],
Segmentation of Pictures and Maps Through Functional Approximation,
CGIP(1), No. 4, December 1972, pp. 360-372.
Elsevier DOI A merging based segmentation algorithm. For technique applied to contours:
See also Waveform Segmentation Through Functional Approximation. BibRef 7212

Harlow, C.A.[Charles A.], and Esenbeis, S.A.,
An Analysis of Radiographic Images,
TC(22), No. 7, July 1973, pp. 678-689. BibRef 7307

Feldman, J.A.[Jerome A.], Yakimovsky, Y.[Yoram],
Decision Theory and Artificial Intelligence: I. A Semantics Based Region Analyzer,
AI(5), No. 4, 1974, pp. 349-371.
Elsevier DOI Segmentation, Knowledge. Relaxation. Probability. This paper, based on the thesis of Yakimovsky in 1973 (
See also Scene Analysis Using a Semantic Base for Region Growing. ), describes the the use of a probabilistic model for guiding region merging. The basic approach is to include the possible interpretation of the regions in the merging criteria. The interpretation (probability of a given interpretation) is based on the values measured in the image, and the context (i.e. sky can be adjacent to the hill side). The set of a priori probabilities must be given or derived for each new type of scene. BibRef 7400

Yakimovsky, Y.,
Boundary and Object Detection in Real World Images,
JACM(23), No. 4, October 1976, pp. 599-618. BibRef 7610
Earlier: IJCAI75(695-704). BibRef

Yakimovsky, Y.[Yoram], Feldman, J.A.[Jerome A.],
A Semantics-Based Decision Theory Region Analyzer,
IJCAI73(580-588). BibRef 7300
And: CMetImAly77(426-434). A strong model drives the region grower, merging is based on the identity and on the image level features. BibRef

Yakimovsky, Y.[Yoram], Feldman, J.A.[Jerome A.],
On the Recognition of Complex Structures: Computer Software Using AI Applied to Pattern Recognition,
ICPR74(345-353). BibRef 7400

Yakimovsky, Y.[Yoram],
Sequential Decision Based Edge Detection,
CGPR75(290-291). BibRef 7500

Yakimovsky, Y.[Yoram],
Scene Analysis Using a Semantic Base for Region Growing,
Ph.D.Thesis (CS), July 1973. BibRef 7307 Stanford AI-Memo 209. Segmentation, Model Based. Relaxation. A probabilistic model of the world is used to label various regions and to merge like labeled regions to get the final interpretation. BibRef

Zucker, S.W.[Steven W.],
Region Growing: Childhood and Adolescence,
CGIP(5), No. 3, September 1976, pp. 382-399.
Elsevier DOI Survey, Segmentation. Segmentation, Survey. BibRef 7609

Freuder, E.C.[Eugene C.],
Affinity: A Relative Approach to Region Finding,
CGIP(5), No. 2, June 1976, pp. 254-264.
Elsevier DOI For non-uniform surfaces where single threshold will not work. BibRef 7606

Chen, P.C., and Pavlidis, T.,
Image Segmentation as an Estimation Problem,
CGIP(12), No. 2, February 1980, pp. 153-172.
Elsevier DOI BibRef 8002

Lai, P.G., and Ehrich, R.W.,
Segmentation of Images with Incompletely Specified Regions,
SMC(9), 1979, pp. 864-868. BibRef 7900

Pong, T.C.[Ting-Chuen], Shapiro, L.G.[Linda G.], Watson, L.T.[Layne T.], and Haralick, R.M.[Robert M.],
Experiments in Segmentation Using a Facet Model Region Grower,
CVGIP(25), No. 1, January 1984, pp. 1-23.
Elsevier DOI Segmentation, Facet Model. BibRef 8401
Earlier: A1, A2, A4, Only:
A Facet Model Region Growing Algorithm,
PRIP81(279-284). Another use of the facet model, it can now segment.
See also Edge and Region Analysis for Digital Image Data.
See also Facet Model for Image Data, A. BibRef

Urquhart, R.[Roderick],
Graph Theoretical Clustering Based on Limited Neighbourhood Sets,
PR(15), No. 3, 1982, pp. 173-187.
Elsevier DOI Misses non-local properties. BibRef 8200

Derin, H.[Haluk], Won, C.S.[Chee-Sun],
A Parallel Image Segmentation Algorithm Using Relaxation with Varying Neighborhoods and Its Mapping to Array Processors,
CVGIP(40), No. 1, October 1987, pp. 54-78.
Elsevier DOI BibRef 8710

Besl, P.J., and Jain, R.C.[Ramesh C.],
Segmentation Through Variable-Order Surface Fitting,
PAMI(10), No. 2, March 1988, pp. 167-192.
IEEE DOI BibRef 8803
Earlier:
Segmentation Through Symbolic Surface Descriptions,
CVPR86(77-85). Segmentation, Range. Segmentation, Surfaces. Segmentation, 3-D Data. Surface Fitting. The system is intended for 3-D data, but was also applied to standard images. Find a seed region that is uniform and grow it by adding similar types of surfaces. BibRef

Besl, P.J., and Jain, R.C.,
Range Image Segmentation,
MVAAS88(XX-YY). Represent surfaces with bivariate functions and use in recognition. BibRef 8800

Monga, O.,
An Optimal Region Growing Algorithm for Image Segmentation,
PRAI(1), No. 4, December 1987, pp. 351-375. BibRef 8712

Gagalowicz, A., and Monga, O.,
A New Approach to Image Segmentation,
ICPR86(265-267). BibRef 8600

Gambotto, J.P.,
A Hierarchical Segmentation Algorithm,
ICPR86(951-953). BibRef 8600
Earlier: Add A2: Monga, O.,
A Parallel and Hierarchical Algorithm for Region Growing,
CVPR85(649-652). (ETCA) Start from single pixel regions, merge based on the average gray level in adjacent regions. Slow convergence. Sounds standard. BibRef

Kegelmeyer, Jr., W.P.[William P.],
A Minimal Error Region Merging Technique for Segmentation,
CVPR83(144-145). (Hughes-ES). Merge regions which would introduce the least error in gray values. BibRef 8300

Krakauer, L.J.,
Computer Analysis of Visual Properties of Curved Objects,
MIT Project MAC-TR-82, May 1971. BibRef 7105
And: MIT AI-TR-234. BibRef Ph.D.Thesis (EE).
WWW Link. Shape from Shading. Both shape from shading and region growing. Generate a tree based on a series of thresholds. BibRef

Adams, R., Bischof, L.,
Seeded Region Growing,
PAMI(16), No. 6, June 1994, pp. 641-647.
IEEE DOI BibRef 9406

Narendra, P.M., and Goldberg, M.,
Image Segmentation with Directed Trees,
PAMI(2), No. 2, March 1980, pp. 185-190. BibRef 8003
Earlier:
A Graph-Theoretic Approach to Image Segmentation,
PRIP77(248-256). BibRef

Snyder, W.E., and Cowart, A.E.,
An Iterative Approach to Region Growing Using Associative Memories,
PAMI(5), No. 3, May 1983, pp. 349-352. BibRef 8305
Earlier:
An Iterative Approach to Region Growing,
ICPR80(348-351). BibRef

Beulieu, J.M.[Jean-Marie], and Goldberg, M.[Morris],
Hierarchy in Picture Segmentation: A Stepwise Optimization Approach,
PAMI(11), No. 2, February 1989, pp. 150-163.
IEEE DOI BibRef 8902
Earlier:
Step-Wise Optimization for Hierarchical Picture Segmentation,
CVPR83(59-64). (Ottawa) First break into basic regions (minimum approximation error is used to determine how/when to stop). Then merge only the best one first (rather than all that meet the criteria) until deciding to stop. Intermediate segmentations represent different levels of separation of the adjacent regions. BibRef

Chang, Y.L., Li, X.B.,
Adaptive Image Region-Growing,
IP(3), No. 6, November 1994, pp. 868-872.
IEEE DOI BibRef 9411

LaValle, S.M., Hutchinson, S.A.,
A Bayesian Framework for Constructing Probability-Distributions on the Space of Image Segmentations,
CVIU(61), No. 2, March 1995, pp. 203-230.
DOI Link BibRef 9503

LaValle, S.M., Hutchinson, S.A.,
A Bayesian Segmentation Methodology for Parametric Image-Models,
PAMI(17), No. 2, February 1995, pp. 211-217.
IEEE DOI Bayes Nets. BibRef 9502
And: UIUCBI-AI-RCV-93-06, 1993. BibRef
Earlier:
Bayesian Region Merging Probability for Parametric Image Models,
CVPR93(778-779).
IEEE DOI A good list of references for texture segmentation papers. In some sources listed as: Image Segmentation Using a Bayesian Region Merging Probability. BibRef

LaValle, S.M., Moroney, K.J., and Hutchinson, S.A.,
Agglomerative Clustering on Range Data with a Unified Probabilistic Merging Function and Termination Criterion,
CVPR93(798-799).
IEEE DOI BibRef 9300

Chang, Y.L.[Yian-Leng], Li, X.B.[Xiao-Bo],
Fast image region growing,
IVC(13), No. 7, September 1995, pp. 559-571.
Elsevier DOI 0401
Effect of merge criteria. BibRef

Chiarello, E.[Ernest], Jolion, J.M.[Jean-Michel], Amoros, C.[Claude],
Regions Growing with the Stochastic Pyramid: Application in Landscape Ecology,
PR(29), No. 1, January 1996, pp. 61-75.
Elsevier DOI BibRef 9601

Baraldi, A., Parmiggiani, F.,
Single Linkage Region Growing Algorithms Based on the Vector Degree of Match,
GeoRS(34), No. 1, January 1996, pp. 137-148.
IEEE Top Reference. BibRef 9601

Tremeau, A.[Alain], Borel, N.[Nathalie],
A Region Growing and Merging Algorithm to Color Segmentation,
PR(30), No. 7, July 1997, pp. 1191-1203.
Elsevier DOI 9707
BibRef
And: Correction: PR(30), No. 10, October 1997, pp. 1799-1800. BibRef

Moghaddamzadeh, A., Bourbakis, N.,
A Fuzzy Region Growing Approach for Segmentation of Color Images,
PR(30), No. 6, June 1997, pp. 867-881.
Elsevier DOI 9706
BibRef

Moghaddamzadeh, A., Goldman, D., Bourbakis, N.,
Fuzzy-Like Approach for Smoothing and Edge Detection in Color Images,
PRAI(12), No. 6, September 1998, pp. 801-816. BibRef 9809

Moghaddamzadeh, A., Bourbakis, N.,
A Fuzzy Approach for Smoothing and Edge Detection in Color Images,
SPIE(2421), 1995, pp. 90-102. BibRef 9500

Kamgar-Parsi, B.[Behrooz],
Object extraction in images,
US_Patent5,923,776, July 13, 1999.
HTML Version. Object extraction by region growing. BibRef 9907

Kamgar-Parsi, B., Kamgar-Parsi, B.,
Improved Image Thresholding for Object Extraction in IR Images,
ICIP01(I: 758-761).
IEEE DOI 0108
BibRef

Yuan, X., Goldman, D., Moghaddamzadeh, A., Bourbakis, N.,
Segmentation of Colour Images with Highlights and Shadows Using Fuzzy-like Reasoning,
PAA(4), No. 4 2001, pp. 272-282.
Springer DOI 0202
BibRef

Revol, C., Jourlin, M.,
A New Minimum-Variance Region Growing Algorithm For Image Segmentation,
PRL(18), No. 3, March 1997, pp. 249-258. 9706
BibRef

Thiran, J.P., Warscotte, V., Macq, B.,
A Queue-Based Region Growing Algorithm for Accurate Segmentation of Multidimensional Digital Images,
SP(60), No. 1, July 1997, pp. 1-10. 9709
BibRef

Mehnert, A.J.H., Jackway, P.T.,
An Improved Seeded Region Growing Algorithm,
PRL(18), No. 10, October 1997, pp. 1065-1071. 9802
BibRef

Crespo, J., Schafer, R.W., Serra, J., Gratin, C., Meyer, F.,
The Flat Zone Approach: A General Low-Level Region Merging Segmentation Method,
SP(62), No. 1, October 1997, pp. 37-60. 9801
BibRef

Hojjatoleslami, S.A., Kittler, J.V.,
Region Growing: A New Approach,
IP(7), No. 7, July 1998, pp. 1079-1084.
IEEE DOI 9807
BibRef
Earlier: TRUniv. Surry, 1995. BibRef

Coiras, E.[Enrique], Santa-Maria, J.[Javier], Miravet, C.[Carlos],
Hexadecagonal region growing,
PRL(19), No. 12, 30 October 1998, pp. 1111-1117. BibRef 9810

Rosin, P.L.,
Refining Region Estimates,
PRAI(12), No. 6, September 1998, pp. 841. BibRef 9809

Liu, J.M.[Ji-Ming], Tang, Y.Y.[Yuan Y.],
Adaptive Image Segmentation With Distributed Behavior-Based Agents,
PAMI(21), No. 6, June 1999, pp. 544-551.
IEEE Abstract.
IEEE DOI Image is a 2-D cellular representation where the agent tries to label homogeneous segments. (Region growing.)
See also Distributed Autonomous Agents For Chinese Document Image Segmentation. BibRef 9906

Lira, J., Frulla, L.A.,
An automated region growing algorithm for segmentation of texture regions in SAR images,
JRS(19), No. 18, December 1998, pp. 3595. BibRef 9812

Osman, H., Blostein, S.D.,
Probabilistic Winner-Take-All Segmentation of Images with Application to Ship Detection,
SMC-B(30), No. 3, June 2000, pp. 485-490.
IEEE Top Reference. 0006
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Shi, J.B.[Jian-Bo], Malik, J.[Jitendra],
Normalized Cuts and Image Segmentation,
PAMI(22), No. 8, August 2000, pp. 888-905.
IEEE DOI Or:
PS File. 0010
BibRef
Earlier: CVPR97(731-737).
IEEE DOI 9704
Perceptual Grouping. Award, Longuet-Higgins. (Awarded 10 years later for contributions that withstood the test of time.) Arbitrary shape clusters.
PS File. Perceptual grouping approach to segmentation. Find an optimal partition of the graph.
See also Normalized cut image segmenation software. BibRef

Shi, J.B.[Jian-Bo], Malik, J.[Jitendra],
Self-Inducing Relational Distance and its Application to Image Segmentation,
ECCV98(I: 528).
Springer DOI Global minimum for segmentation, using graph method. BibRef 9800

Cour, T., Yu, S., and Shi, J.,
Normalized cut image segmenation software,
Online2006.
WWW Link. Code, Segmentation. Code, Segmentation, C. Matlab Code for segmentation and clustering. C code for segmentation.
See also Normalized Cuts and Image Segmentation. BibRef 0600

Shi, J., Belongie, S.J., Leung, T., Malik, J.,
Image and video segmentation: the normalized cut framework,
ICIP98(I: 943-947).
IEEE DOI 9810
BibRef

Fan, J.P.[Jian-Ping], Yau, D.K.Y., Elmagarmid, A.K., Aref, W.G.,
Automatic image segmentation by integrating color-edge extraction and seeded region growing,
IP(10), No. 10, October 2001, pp. 1454-1466.
IEEE DOI 0110
BibRef

Fan, J.P.[Jian-Ping], Zhu, X.Q.[Xing-Quan], Wu, L.D.[Li-De],
Automatic model-based semantic object extraction algorithm,
CirSysVideo(11), No. 10, October 2001, pp. 1073-1084.
IEEE Top Reference. 0110
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Guigues, L.[Laurent], Le Men, H.[Hervé], Cocquerez, J.P.[Jean-Pierre],
The hierarchy of the cocoons of a graph and its application to image segmentation,
PRL(24), No. 8, May 2003, pp. 1059-1066.
Elsevier DOI 0304

See also Scale-Sets Image Analysis. BibRef

Wan, S.Y.[Shu-Yen], Higgins, W.E.,
Symmetric Region Growing,
IP(12), No. 9, September 2003, pp. 1007-1015.
IEEE DOI 0308
BibRef
Earlier: ICIP00(Vol II: 96-99).
IEEE DOI 0008
Define criteria invariant to the starting seed regions. BibRef

Wan, S.Y., Nung, E.,
Seed-invariant Region Growing: Its Properties and Applications to 3-d Medical CT Images,
ICIP01(I: 710-713).
IEEE DOI 0108
BibRef

Lallich, S.[Stéphane], Muhlenbach, F.[Fabrice], Jolion, J.M.[Jean-Michel],
A test to control a region growing process within a hierarchical graph,
PR(36No. 10, October 2003, pp. 2201-2211.
Elsevier DOI 0308
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Brun, L.[Luc], Domenger, J.P.[Jean-Philippe], Mokhtari, M.[Myriam],
Incremental modifications of segmented image defined by discrete maps,
JVCIR(14), No. 3, September 2003, pp. 251-290.
Elsevier DOI 0308
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Veenman, C.J., Reinders, M.J.T., Backer, E.,
A cellular coevolutionary algorithm for image segmentation,
IP(12), No. 3, March 2003, pp. 304-316.
IEEE DOI 0301
BibRef

Cheng, S.C.,
Region-growing approach to colour segmentation using 3D clustering and relaxation labelling,
VISP(150), No. 4, August 2003, pp. 270-276.
IEEE Abstract. 0311
Group pixels into homogeneous regions by combining 3D clustering and relaxation labelling techniques. Each resulting small region is then merged to the region which is the nearest to it in terms of colour similarity and spatial proximity. BibRef

Montoya, M.G., Gil, C., and Garcia, I.,
The load unbalancing problem for region growing image segmentation algorithms,
PDS(63), 2003, pp. 387-395. Implementation for region growing. BibRef 0300

Chuang, C.H., Lie, W.N.,
A Downstream Algorithm Based on Extended Gradient Vector Flow Field for Object Segmentation,
IP(13), No. 10, October 2004, pp. 1379-1392.
IEEE DOI 0410
BibRef
Earlier:
Region Growing Based on Extended Gradient Vector Flow Field Model for Multiple Objects Segmentation,
ICIP01(III: 74-77).
IEEE DOI 0108
BibRef

Nock, R.[Richard], Nielsen, F.,
Statistical Region Merging,
PAMI(26), No. 11, November 2004, pp. 1452-1458.
IEEE Abstract. 0410
BibRef
Earlier:
On region merging: the statistical soundness of fast sorting, with applications,
CVPR03(II: 19-26).
IEEE DOI 0307
Analysis of merging in a particular order.
See also Semi-supervised statistical region refinement for color image segmentation. BibRef

Nielsen, F., Nock, R.,
Consensus Region Merging for Image Segmentation,
ACPR13(325-329)
IEEE DOI 1408
image resolution BibRef

Fiorio, C.[Christophe], Mas, A.[Andre],
A Sharp Concentration-Based Adaptive Segmentation Algorithm,
ISVC10(II: 85-96).
Springer DOI 1011
BibRef

Fiorio, C., Nock, R.,
A Concentration-Based Adaptive Approach to Region Merging of Optimal Time and Space Complexities,
BMVC00(xx-yy).
PDF File. 0009
BibRef

Fiorio, C.,
Sorted Region Merging to Maximize Test Reliability,
ICIP00(Vol I: 808-811).
IEEE DOI 0008
BibRef

Barbu, A., Zhu, S.C.[Song-Chun],
Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities,
PAMI(27), No. 8, August 2005, pp. 1239-1253.
IEEE Abstract. 0506
BibRef
Earlier:
Multigrid and Multi-Level Swendsen-Wang Cuts for Hierarchic Graph Partition,
CVPR04(II: 731-738).
IEEE DOI 0408
BibRef
Earlier:
Graph partition by Swendsen-Wang cuts,
ICCV03(320-327).
IEEE DOI 0311
BibRef

Fan, J.P.[Jian-Ping], Zeng, G.H.[Gui-Hua], Body, M.[Mathurin], Hacid, M.S.[Mohand-Said],
Seeded region growing: an extensive and comparative study,
PRL(26), No. 8, June 2005, pp. 1139-1156.
Elsevier DOI 0506
BibRef

Shih, F.Y.[Frank Y.], Cheng, S.X.[Shou-Xian],
Automatic seeded region growing for color image segmentation,
IVC(23), No. 10, 20 September 2005, pp. 877-886.
Elsevier DOI 0509
BibRef

Kim, C.[Changick],
Segmenting a low-depth-of-field image using morphological filters and region merging,
IP(14), No. 10, October 2005, pp. 1503-1511.
IEEE DOI 0510
BibRef

Grady, L.[Leo],
Random Walks for Image Segmentation,
PAMI(28), No. 11, November 2006, pp. 1768-1783.
IEEE DOI 0609
BibRef
Earlier:
Multilabel Random Walker Image Segmentation Using Prior Models,
CVPR05(I: 763-770).
IEEE DOI 0507

See also Isoperimetric Graph Partitioning for Image Segmentation. Interactive Segmentation. Start with small number of user labeled pixels. Determine probability a random walk will get from unlabeled to labeled. BibRef

Dupuis, A.[Arnaud], Vasseur, P.[Pascal],
Image segmentation by cue selection and integration,
IVC(24), No. 10, 1 October 2006, pp. 1053-1064.
Elsevier DOI 0609
Image partitioning; Affinity matrices; Cue selection; Integration; PCA Segmentation as graph partitioning, pixel similarity the link. PCA at each iteration to determine affinity. BibRef

Brunner, D.[Dominik], Soille, P.[Pierre],
Iterative area filtering of multichannel images,
IVC(25), No. 8, 1 August 2007, pp. 1352-1364.
Elsevier DOI 0706
Partition; Image simplification; Quasi-flat zone; Seeded region growing; Mathematical morphology; Area filter; Connected operator; Multispectral BibRef

von Wangenheim, A.[Aldo], Bertoldi, R.F.[Rafael F.], Abdala, D.D.[Daniel D.], Richter, M.M.[Michael M.],
Color image segmentation guided by a color gradient network,
PRL(28), No. 13, 1 October 2007, pp. 1795-1803.
Elsevier DOI 0709
Region-growing segmentation; Natural color scenes; Color gradient networks BibRef

von Wangenheim, A.[Aldo], Bertoldi, R.F.[Rafael F.], Abdala, D.D.[Daniel D.], Sobieranski, A.C., Coser, L., Jiang, X., Richter, M.M., Priese, L., Schmitt, F.,
Color image segmentation using an enhanced Gradient Network Method,
PRL(30), No. 15, 1 November 2009, pp. 1404-1412.
Elsevier DOI 0910
Color image segmentation; Region-growing; Outdoors scenes; Gradient Network Method BibRef

Carvalho, L.E., Mantelli Neto, S.L., von Wangenheim, A., Sobieranski, A.C., Coser, L., Comunello, E.,
Hybrid Color Segmentation Method Using a Customized Nonlinear Similarity Function,
IJIG(14), No. 1-2, 2014, pp. 1450005.
DOI Link 1406
BibRef

Carvalho, L.E., Mantelli Neto, S.L., Sobieranski, A.C., Comunello, E., von Wangenheim, A.,
Improving Graph-Based Image Segmentation Using Nonlinear Color Similarity Metrics,
IJIG(15), No. 04, 2015, pp. 1550018.
DOI Link 1509
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Udupa, J.K.[Jayaram K.], Ajjanagadde, V.G.[Venkatramana G.],
Boundary and Object Labelling in Three-Dimensional Images,
CVGIP(51), No. 3, September 1990, pp. 355-369.
Elsevier DOI Generate the surfaces from slices. BibRef 9009

Udupa, J.K.[Jayaram K.], Samarasekera, S.,
Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation,
GMIP(58), No. 3, May 1996, pp. 246-261. 9606
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Saha, P.K.[Punam K.], Udupa, J.K.[Jayaram K.],
Fuzzy Connected Object Delineation: Axiomatic Path Strength Definition and the Case of Multiple Seeds,
CVIU(83), No. 3, September 2001, pp. 275-295.
DOI Link Extension of previous theory for fuzzy connections. Each pair has a connectedness strength. The maximum of path strengths of minimum of affinities along each path is the only valid measure. 0110
BibRef

Saha, P.K.[Punam K.], Udupa, J.K.[Jayaram K.], Odhner, D.[Dewey],
Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation,
CVIU(77), No. 2, February 2000, pp. 145-174.
DOI Link 0003
BibRef

Zhuge, Y.[Ying], Udupa, J.K.[Jayaram K.], Saha, P.K.[Punam K.],
Vectorial scale-based fuzzy-connected image segmentation,
CVIU(101), No. 3, March 2006, pp. 177-193.
Elsevier DOI 0601
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Ciesielski, K.C.[Krzysztof Chris], Udupa, J.K.[Jayaram K.],
Affinity functions in fuzzy connectedness based image segmentation I: Equivalence of affinities,
CVIU(114), No. 1, January 2010, pp. 146-154.
Elsevier DOI 1001
Affinity; Fuzzy connectedness; Image segmentation; Equivalence of algorithms BibRef

Ciesielski, K.C.[Krzysztof Chris], Udupa, J.K.[Jayaram K.],
Affinity functions in fuzzy connectedness based image segmentation II: Defining and recognizing truly novel affinities,
CVIU(114), No. 1, January 2010, pp. 155-166.
Elsevier DOI 1001
Affinity; Fuzzy connectedness; Image segmentation; Equivalence of algorithms BibRef

Ciesielski, K.C.[Krzysztof Chris], Udupa, J.K.[Jayaram K.],
A framework for comparing different image segmentation methods and its use in studying equivalences between level set and fuzzy connectedness frameworks,
CVIU(115), No. 6, June 2011, pp. 721-734.
Elsevier DOI 1104
Segmentation; Delineation; Algorithm equivalence; Convergence; Level sets; Fuzzy connectedness; Medical images BibRef

Zhuge, Y.[Ying], Udupa, J.K.[Jayaram K.],
Intensity standardization simplifies brain MR image segmentation,
CVIU(113), No. 10, October 2009, pp. 1095-1103.
Elsevier DOI 0910
Inhomogeneity correction; Standardization; Fuzzy connectedness; Brain image segmentation; MRI BibRef

Saha, P.K.[Punam K.], Udupa, J.K.[Jayaram K.],
Relative Fuzzy Connectedness among Multiple Objects: Theory, Algorithms, and Applications in Image Segmentation,
CVIU(82), No. 1, April 2001, pp. 42-56.
DOI Link 0001
Fuzzy connectedness: assign strength to every path between every pair of elements. BibRef

Udupa, J.K.[Jayaram K.], Saha, P.K.[Punam K.], de Alencar Lotufo, R.[Roberto],
Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation,
PAMI(24), No. 11, November 2002, pp. 1485-1500.
IEEE Abstract. 0211

See also Disclaimer: Relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. BibRef

Editors, T.[The],
Disclaimer: 'Relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation',
PAMI(26), No. 2, February 2004, pp. 287-287.
See also Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation.
See also Multiseeded Segmentation Using Fuzzy Connectedness.
IEEE Abstract. 0402
BibRef

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SAR Sea-Ice Image Analysis Based on Iterative Region Growing Using Semantics,
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Joint Image Segmentation and Interpretation Using Iterative Semantic Region Growing on SAR Sea Ice Imagery,
ICPR06(II: 223-226).
IEEE DOI 0609
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Filament Preserving Segmentation for SAR Sea Ice Imagery Using a New Statistical Model,
ICPR06(IV: 849-852).
IEEE DOI 0609
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Combining Local and Global Features for Image Segmentation Using Iterative Classification and Region Merging,
CRV05(579-586).
IEEE DOI 0505
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SAR sea ice image segmentation using an edge-preserving region-based MRF,
ICIP09(1721-1724).
IEEE DOI 0911
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SAR Sea Ice Image Segmentation Based on Edge-preserving Watersheds,
CRV07(426-431).
IEEE DOI 0705
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Yu, Q.Y.[Qi-Yao], Clausi, D.A.[David A.],
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IEEE DOI 1008
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Yu, P., Qin, A.K., Clausi, D.A.,
Unsupervised Polarimetric SAR Image Segmentation and Classification Using Region Growing With Edge Penalty,
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IET-CV(2), No. 4, December 2008, pp. 193-207.
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A fast hierarchical approach to image segmentation,
ICPR08(1-4).
IEEE DOI 0812
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Lu, F.F.[Fang-Fang], Fu, Z.Y.[Zhou-Yu], Robles-Kelly, A.[Antonio],
Efficient Graph Cuts for Multiclass Interactive Image Segmentation,
ACCV07(II: 134-144).
Springer DOI 0711
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Springer DOI 0509
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IEEE DOI 0911
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A Basin Morphology Approach to Colour Image Segmentation by Region Merging,
ACCV07(I: 935-944).
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Aptoula, E., Courty, N., Lefčvre, S.[Sébastien],
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Region Merging Techniques Using Information Theory Statistical Measures,
IP(19), No. 6, June 2010, pp. 1567-1586.
IEEE DOI 1006
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ICIP10(2237-2240).
IEEE DOI 1009
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General region merging approaches based on information theory statistical measures,
ICIP08(3016-3019).
IEEE DOI 0810
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Elsevier DOI 1007
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IEEE DOI 0812
Group-based classification; Nearest neighbour; Compound classification Label groups of homogeneous samples rather than single samples BibRef

Zhang, L.[Lei], Ji, Q.A.[Qi-Ang],
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IEEE DOI 1007
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Zhang, L.[Lei], Wang, X.[Xun], Penwarden, N.[Nicholas], Ji, Q.A.[Qi-Ang],
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Integration of multiple contextual information for image segmentation using a Bayesian Network,
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Cheng, M.M.[Ming-Ming], Zhang, G.X.[Guo-Xin],
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Peng, B.[Bo], Zhang, L.[Lei], Zhang, D.[David], Yang, J.[Jian],
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Fast Detection of Curved Edges at Low SNR,
CVPR16(213-221)
IEEE DOI 1612
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Wang, Y.Q.[Yi-Qing], Trouvé, A.[Alain], Amit, Y.[Yali], Nadler, B.[Boaz],
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Springer DOI 1710
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Springer DOI 1009
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Mendoza, C.S.[Carlos S.], Acha, B.[Begońa], Serrano, C.[Carmen], Gómez-Cía, T.[Tomás],
Fast parameter-free region growing segmentation with application to surgical planning,
MVA(23), No. 1, January 2012, pp. 165-177.
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Chen, J.J.[Jiann-Jone], Su, C.R.[Chun-Rong], Grimson, W.E.L., Liu, J.L.[Jun-Lin], Shiue, D.H.[De-Hui],
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IP(21), No. 2, February 2012, pp. 828-843.
IEEE DOI 1201
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Reconfigurable Peer-to-Peer network Image Retrieval,
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Tilton, J.C., Tarabalka, Y., Montesano, P.M., Gofman, E.,
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GeoRS(50), No. 11, November 2012, pp. 4454-4467.
IEEE DOI 1210
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Dawoud, A.[Amer], Netchaev, A.[Anton],
Fusion of visual cues of intensity and texture in Markov random fields image segmentation,
IET-CV(6), No. 6, 2012, pp. 603-609.
DOI Link 1301
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Fusion of Edge Information in Markov Random Fields Region Growing Image Segmentation,
ICIAR10(I: 96-104).
Springer DOI 1006
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Han, Y.[Yu], Feng, X.C.[Xiang-Chu], Baciu, G.[George],
Variational and PCA based natural image segmentation,
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Elsevier DOI 1303
Image segmentation; Principal component analysis; Region competition; Variable splitting; Iterative reweighting BibRef

Han, Y.[Yu], Feng, X.C.[Xiang-Chu], Baciu, G.[George],
Local joint entropy based non-rigid multimodality image registration,
PRL(34), No. 12, 1 September 2013, pp. 1405-1415.
Elsevier DOI 1306
Image registration; Non-rigid; Weighted Horn regularization; Variational differential; Alternative minimization; AOS algorithm BibRef

Weber, J.[Jonathan], Lefčvre, S.[Sébastien],
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Elsevier DOI 1303
Quasi-flat zones; Mathematical Morphology; Quasi-flat zones filtering; Image segmentation; Image simplification; Interactive segmentation; Video segmentation; Oversegmentation reduction BibRef

Priego, B.[Blanca], Souto, D.[Daniel], Bellas, F.[Francisco], Duro, R.J.[Richard J.],
Hyperspectral image segmentation through evolved cellular automata,
PRL(34), No. 14, 2013, pp. 1648-1658.
Elsevier DOI 1308
Hyperspectral imaging BibRef

Sáez, A.[Aurora], Serrano, C.[Carmen], Acha, B.[Begońa],
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Kalinin, P.[Pavel], Sirota, A.[Aleksandr],
A graph based approach to hierarchical image over-segmentation,
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Segmentation BibRef

Zhao, Q.P.[Qin-Pei], Shi, Y.[Yang], Liu, Q.[Qin], Fränti, P.[Pasi],
A grid-growing clustering algorithm for geo-spatial data,
PRL(53), No. 1, 2015, pp. 77-84.
Elsevier DOI 1502
Apply region growing ideas to clustering. Grid-based clustering BibRef

García, J.F.G.[Juan F. García], Venegas-Andraca, S.E.[Salvador E.],
Region-based approach for the spectral clustering Nyström approximation with an application to burn depth assessment,
MVA(26), No. 2-3, April 2015, pp. 353-368.
Springer DOI 1504
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Fan, M.J.[Min-Jie], Lee, T.C.M.,
Variants of seeded region growing,
IET-IPR(9), No. 6, 2015, pp. 478-485.
DOI Link 1507
image segmentation BibRef

Lassalle, P., Inglada, J., Michel, J., Grizonnet, M., Malik, J.,
A Scalable Tile-Based Framework for Region-Merging Segmentation,
GeoRS(53), No. 10, October 2015, pp. 5473-5485.
IEEE DOI 1509
image segmentation BibRef

Rzeszutek, R.[Richard], Androutsos, D.[Dimitrios],
Propagating sparse labels through edge-aware filters,
SIViP(9), No. 1 Supp, December 2015, pp. 17-24.
Springer DOI 1601
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Maggiori, E.[Emmanuel], Tarabalka, Y.[Yuliya], Charpiat, G.[Guillaume],
Optimizing Partition Trees for Multi-Object Segmentation with Shape Prior,
BMVC15(xx-yy).
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Baltaxe, M.[Michael], Meer, P.[Peter], Lindenbaum, M.[Michael],
Local Variation as a Statistical Hypothesis Test,
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Li, Q.W.[Qian-Wen], Wei, Z.H.[Zhi-Hua], Zhao, C.R.[Cai-Rong],
Optimized Automatic Seeded Region Growing Algorithm with Application to ROI Extraction,
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Zhou, D.G.[Dong-Guo], Shao, Y.H.[Yan-Hua],
Region growing for image segmentation using an extended PCNN model,
IET-IPR(12), No. 5, May 2018, pp. 729-737.
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Gemme, L.[Laura], Dellepiane, S.G.[Silvana G.],
An Automatic Data-Driven Method for SAR Image Segmentation in Sea Surface Analysis,
GeoRS(56), No. 5, May 2018, pp. 2633-2646.
IEEE DOI 1805
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A New Graph-Based Method for Automatic Segmentation,
CIAP15(I:601-611).
Springer DOI 1511
Cost function, Image segmentation, Marine vehicles, Oils, Radar imaging, Robustness, Synthetic aperture radar, unsupervised BibRef

Coliban, R.M.[Radu-Mihai], Ivanovici, M.[Mihai],
Reducing the oversegmentation induced by quasi-flat zones for multivariate images,
JVCIR(53), 2018, pp. 281-293.
Elsevier DOI 1805
Quasi-flat zones, Mathematical morphology, Color image segmentation, Hyperspectral pixel classification, EDICS: 4.4 morphological image analysis BibRef

Li, C.M.[Cheng-Ming], Yin, Y.[Yong], Liu, X.L.[Xiao-Li], Wu, P.[Pengda],
An Automated Processing Method for Agglomeration Areas,
IJGI(7), No. 6, 2018, pp. xx-yy.
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Some of the same issues as merging regions. BibRef

Tang, H.[Hong], Zhai, X.J.[Xue-Jun], Huang, W.[Wei],
Edge Dependent Chinese Restaurant Process for Very High Resolution (VHR) Satellite Image Over-Segmentation,
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Su, T.F.[Teng-Fei],
Scale-variable region-merging for high resolution remote sensing image segmentation,
PandRS(147), 2019, pp. 319-334.
Elsevier DOI 1901
High resolution remote sensing imagery, Image segmentation, Region merging, Scale-variable BibRef

Su, T.F.[Teng-Fei], Liu, T.X.[Ting-Xi], Zhang, S.W.[Sheng-Wei], Qu, Z.Y.[Zhong-Yi], Li, R.P.[Rui-Ping],
Machine learning-assisted region merging for remote sensing image segmentation,
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Elsevier DOI 2009
Random forest, Machine learning, Region merging, Merging criterion, Image segmentation, Remote sensing BibRef

Dekavalla, M.[Maria], Argialas, D.[Demetre],
A Region Merging Segmentation with Local Scale Parameters: Applications to Spectral and Elevation Data,
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Fasquel, J.B.[Jean-Baptiste], Delanoue, N.[Nicolas],
Approach for sequential image interpretation using a priori binary perceptual topological and photometric knowledge and k-means-based segmentation,
JOSA-A(35), No. 6, June 2018, pp. 936-945.
DOI Link 1912
Digital image processing, Image analysis, Pattern recognition, Anisotropic diffusion, Image analysis, Image processing, Reconstruction algorithms BibRef

Fasquel, J.B.[Jean-Baptiste], Delanoue, N.[Nicolas],
A Graph Based Image Interpretation Method Using A Priori Qualitative Inclusion and Photometric Relationships,
PAMI(41), No. 5, May 2019, pp. 1043-1055.
IEEE DOI 1904
Recover regions from initial oversegmentation. Liver, Photometry, Image edge detection, Tumors, Uncertainty, Databases, Analytical models, Image interpretation, photometric relationships BibRef

Sun, F.[Fengdong], Li, W.H.[Wen-Hui],
Saliency guided deep network for weakly-supervised image segmentation,
PRL(120), 2019, pp. 62-68.
Elsevier DOI 1904
Weakly-supervised segmentation, Seeded region growing, Saliency guidance BibRef

Ni, H.[Huan], Niu, X.N.[Xiao-Nan],
Agglomerative oversegmentation using dual similarity and entropy rate,
PR(93), 2019, pp. 324-336.
Elsevier DOI 1906
Oversegmentation, Agglomerative algorithm, Entropy rate, Remote sensing BibRef

Perret, B.[Benjamin], Cousty, J.[Jean], Guimarăes, S.J.F.[Silvio Jamil Ferzoli], Kenmochi, Y.[Yukiko], Najman, L.[Laurent],
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PRL(128), 2019, pp. 433-439.
Elsevier DOI 1912
Hierarchy of partitions, Attribute, Segmentation, Hierarchical clustering BibRef

Adăo, M.M.[Milena M.], Guimarăes, S.J.F.[Silvio Jamil F.], Patrocínio, Jr., Z.K.G.[Zenilton K.G.],
Learning to realign hierarchy for image segmentation,
PRL(133), 2020, pp. 287-294.
Elsevier DOI 2005
Hierarchical image segmentation, Alignment of hierarchy, Regression, Random forest, Neural network BibRef

Khan, Z.[Zubair], Yang, J.[Jie],
Bottom-Up Unsupervised Image Segmentation Using FC-Dense U-Net Based Deep Representation Clustering and Multidimensional Feature Fusion Based Region Merging,
IVC(94), 2020, pp. 103871.
Elsevier DOI 2003
Unsupervised image segmentation, Deep learning, Feature fusion, Region merging, Image processing BibRef

Zhao, Q.H.[Quan-Hua], Zhang, H.Y.[Hong-Yun], Wang, G.H.[Guang-Hui], Li, Y.[Yu],
Irregular Tessellation and Statistical Modeling Based Regionalized Segmentation for SAR Intensity Image,
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Song, Y.Y.[Yang-Yang], Peng, G.H.[Guo-Hua],
Fast two-stage segmentation model for images with intensity inhomogeneity,
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Shrivastava, N.[Neeraj], Bharti, J.[Jyoti],
Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review,
IJIG(20), No. 3, July 2020, pp. 2050018.
DOI Link 2008
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Chen, F.[Fang], Wang, N.[Ning], Yu, B.[Bo], Qin, Y.C.[Yu-Chu], Wang, L.[Lei],
A Strategy of Parallel Seed-Based Image Segmentation Algorithms for Handling Massive Image Tiles over the Spark Platform,
RS(13), No. 10, 2021, pp. xx-yy.
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Wang, H.Y.[Hao-Yu], Shen, Z.F.[Zhan-Feng], Zhang, Z.H.[Zi-Han], Xu, Z.[Zeyu], Li, S.[Shuo], Jiao, S.H.[Shu-Hui], Lei, Y.T.[Ya-Ting],
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RS(13), No. 14, 2021, pp. xx-yy.
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Jiao, J.J.[Jian-Jun], Wang, X.P.[Xiao-Peng], Zhang, J.P.[Jung-Ping], Wang, Q.S.[Qing-Sheng],
Salient region growing based on Gaussian pyramid,
IET-IPR(15), No. 13, 2021, pp. 3142-3152.
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Baleghi, Y.[Yasser], Rousseau, D.[David],
An analytical proof on suitability of Cauchy-Schwarz Divergence as the aggregation criterion in Region Growing Algorithm,
IVC(115), 2021, pp. 104312.
Elsevier DOI 2110
Region Growing Algorithm, Image segmentation, Cauchy-Schwarz divergence, Aggregation criterion BibRef

Xu, Y.H.[Yong-Hao], Ghamisi, P.[Pedram],
Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes With Point-Level Annotations,
IP(31), 2022, pp. 5038-5051.
IEEE DOI 2208
Code, Segmentation.
WWW Link. Annotations, Image segmentation, Semantics, Training, Remote sensing, Knowledge transfer, Predictive models, Semantic segmentation, remote sensing BibRef


Hwang, J.J.[Jyh-Jing], Yu, S.X.[Stella X.], Shi, J.B.[Jian-Bo], Collins, M.[Maxwell], Yang, T.J.[Tien-Ju], Zhang, X.[Xiao], Chen, L.C.[Liang-Chieh],
SegSort: Segmentation by Discriminative Sorting of Segments,
ICCV19(7333-7343)
IEEE DOI 2004
deep learning for segmentation on regions, not single pixels. image classification, image representation, image segmentation, nearest neighbour methods, neural nets, pattern clustering, Machine learning BibRef

Ding, H.H.[Heng-Hui], Jiang, X.D.[Xu-Dong], Shuai, B.[Bing], Liu, A.Q.[Ai Qun], Wang, G.[Gang],
Context Contrasted Feature and Gated Multi-scale Aggregation for Scene Segmentation,
CVPR18(2393-2402)
IEEE DOI 1812
Image segmentation, Logic gates, Context modeling, Aggregates, Context-aware services, Labeling, Task analysis BibRef

Birodkar, V.[Vighnesh], Lu, Z.C.[Zhi-Chao], Li, S.[Siyang], Rathod, V.[Vivek], Huang, J.[Jonathan],
The surprising impact of mask-head architecture on novel class segmentation,
ICCV21(6995-7005)
IEEE DOI 2203
Training, Protocols, Codes, Crops, Computer architecture, Detectors, Segmentation, grouping and shape, Detection and localization in 2D and 3D BibRef

Remez, T.[Tal], Huang, J.[Jonathan], Brown, M.[Matthew],
Learning to Segment via Cut-and-Paste,
ECCV18(VII: 39-54).
Springer DOI 1810
BibRef

Nguyen, T.K.[Thanh-Khoa], Coustaty, M.[Mickael], Guillaume, J.L.[Jean-Loup],
An Efficient Agglomerative Algorithm Cooperating with Louvain Method for Implementing Image Segmentation,
ACIVS18(150-162).
Springer DOI 1810
BibRef

Baghi, A., Karami, A.,
SAR image segmentation using region growing and spectral cluster,
IPRIA17(229-232)
IEEE DOI 1712
image segmentation, radar imaging, synthetic aperture radar, SAR image segmentation, region growing, spectral cluster, Spectral Cluster BibRef

Mathieu, B.[Bérengčre], Crouzil, A.[Alain], Puel, J.B.[Jean Baptiste],
Oversegmentation Methods: A New Evaluation,
IbPRIA17(185-193).
Springer DOI 1706
BibRef

Mathieu, B.[Bérengčre], Crouzil, A.[Alain], Puel, J.B.[Jean Baptiste],
ASARI: A New Adaptive Oversegmentation Method,
IbPRIA17(194-202).
Springer DOI 1706
BibRef

Bian, A.[Ang], Scherzinger, A.[Aaron], Jiang, X.Y.[Xiao-Yi],
An Enhanced Multi-label Random Walk for Biomedical Image Segmentation Using Statistical Seed Generation,
ACIVS17(748-760).
Springer DOI 1712
BibRef

Bian, A.[Ang], Jiang, X.Y.[Xiao-Yi],
T-Test Based Adaptive Random Walk Segmentation Under Multiplicative Speckle Noise Model,
MCBMIIA16(II: 570-582).
Springer DOI 1704
BibRef
And:
Statistical Modeling Based Adaptive Parameter Setting for Random Walk Segmentation,
ACIVS16(698-710).
Springer DOI 1611
BibRef

Allaouil, A.E., Nasri, M., Merzougui, M., Mirhisse, J.,
Evolutionary Algorithm for Segmentation of Medical Images by Region Growing,
CGiV16(119-124)
IEEE DOI 1608
evolutionary computation BibRef

Chaibou, M.S., Kalti, K., Solaiman, B., Mahjoub, M.A.,
A Combined Approach Based on Fuzzy Classification and Contextual Region Growing to Image Segmentation,
CGiV16(172-177)
IEEE DOI 1608
fuzzy set theory BibRef

Fida, E., Baber, J., Bakhtyar, M., Iqbal, M.J.,
Automatic Image Segmentation Based on Maximal Similarity Based Region Merging,
DICTA15(1-8)
IEEE DOI 1603
computer vision BibRef

Gupta, G.[Gaurav], Psarrou, A.[Alexandra],
Adaptive-Threshold Region Merging via Path Scanning,
ICPR14(948-953)
IEEE DOI 1412
Bismuth BibRef

Fan, H.Q.[Hao-Qi], Li, H.[Han],
Segment-Forest for Segmentation,
ICPR14(990-995)
IEEE DOI 1412
Adaptation models BibRef

Ma, W.[Wei], Liu, J.[Jing], Duan, L.J.[Li-Juan], Zhang, X.Y.[Xin-Yong],
Image Segmentation with Automatically Balanced Constraints,
ACPR13(557-561)
IEEE DOI 1408
graph theory BibRef

Mirghasemi, S., Rayudu, R., Zhang, M.J.[Meng-Jie],
A new image segmentation algorithm based on modified seeded region growing and particle swarm optimization,
IVCNZ13(382-387)
IEEE DOI 1402
image colour analysis BibRef

Li, X.[Xiang], Jin, L.H.[Liang-Hai], Song, E.[Enmin], Li, L.[Lei],
Full-range affinities for graph-based segmentation,
ICIP13(4084-4087)
IEEE DOI 1402
Graph-based segmentation;affinity learning BibRef

Weiss, D.[David], Taskar, B.[Ben],
SCALPEL: Segmentation Cascades with Localized Priors and Efficient Learning,
CVPR13(2035-2042)
IEEE DOI 1309
Region merging cues with high-level knowledge. BibRef

Liu, X.B.[Xiao-Bai], Lin, L.[Liang], Yuille, A.L.[Alan L.],
Robust Region Grouping via Internal Patch Statistics,
CVPR13(1931-1938)
IEEE DOI 1309
BibRef

Ren, Z.[Zhile], Shakhnarovich, G.[Gregory],
Image Segmentation by Cascaded Region Agglomeration,
CVPR13(2011-2018)
IEEE DOI 1309
Image segmentation BibRef

Wang, Z.[Zehan], Wolz, R.[Robin], Tong, T.[Tong], Rueckert, D.[Daniel],
Spatially Aware Patch-Based Segmentation (SAPS): An Alternative Patch-Based Segmentation Framework,
MCVM12(93-103).
Springer DOI 1305
BibRef

Elyor, K.[Kodirov], Lee, G.[Guee_Sang],
Automatic object segmentation using mean shift and growcut,
FCV13(184-189).
IEEE DOI 1304
First mean shift, then merge with grocut. BibRef

Chen, X.H.[Xue-Hong], Chen, J.[Jin], Yamaguchi, Y.S.[Yasu-Shi],
Soft image segmentation model,
CVRS12(90-93).
IEEE DOI 1302
bottom-up region merging BibRef

Han, Y.H.[Ya-Hong], Wu, F.[Fei], Shao, J.[Jian], Tian, Q.[Qi], Zhuang, Y.T.[Yue-Ting],
Graph-guided sparse reconstruction for region tagging,
CVPR12(2981-2988).
IEEE DOI 1208
BibRef

Eigen, D.[David], Fergus, R.[Rob],
Nonparametric image parsing using adaptive neighbor sets,
CVPR12(2799-2806).
IEEE DOI 1208
BibRef

Sellaouti, A.[Aymen], Jaâfra, Y.[Yasmina], Hamouda, A.[Atef],
Meta-learning for Adaptive Image Segmentation,
ICIAR14(I: 187-197).
Springer DOI 1410
BibRef

Sellaouti, A.[Aymen], Hamouda, A.[Atef], Deruyver, A.[Aline], Wemmert, C.[Cédric],
Hierarchical Classification-Based Region Growing (HCBRG): A Collaborative Approach for Object Segmentation and Classification,
ICIAR12(I: 51-60).
Springer DOI 1206
BibRef

Doggaz, N.[Narjes], Ferjani, I.[Imene],
Image Segmentation Using Normalized Cuts and Efficient Graph-Based Segmentation,
CIAP11(II: 229-240).
Springer DOI 1109
BibRef

Yamamoto, A.[Akifumi], Fujiwara, T.[Takayuki], Hashimoto, M.[Manabu], Funahashi, T.[Takuma], Koshimizu, H.[Hiroyasu],
A proposal of The Rareness Measure of pixel blocks and its application to region extraction,
FCV11(1-2).
IEEE DOI 1102
BibRef

Dutta, T., Dogra, D.P., Jana, B.,
Object Extraction Using Novel Region Merging and Multidimensional Features,
PSIVT10(356-361).
IEEE DOI 1011
Extract regions with characteristics of the desired objects first. BibRef

He, L.[Lulu], Pappas, T.N.[Thrasyvoulos N.],
An adaptive clustering and chrominance-based merging approach for image segmentation and abstraction,
ICIP10(241-244).
IEEE DOI 1009
BibRef

Artan, Y.[Yusuf], Yetik, I.S.[Imam Samil],
Improved random walker algorithm for image segmentation,
Southwest10(89-92).
IEEE DOI 1005
BibRef

Feng, W.[Wei], Xie, L.[Lei], Liu, Z.Q.[Zhi-Qiang],
Multicue Graph Mincut for Image Segmentation,
ACCV09(II: 707-717).
Springer DOI 0909
BibRef

Chen, G., Meng, X.[Xin], Hu, T., Guo, X.Y., Liu, L.X.[Li-Xiong], Zhang, H.Y.[Hai-Ying],
A multiphase region-based framework for image segmentation based on least square method,
ICIP09(4009-4012).
IEEE DOI 0911
BibRef

Revol-Muller, C.[Chantal], Grenier, T.[Thomas], Li, T.[Ting], Benoit-Cattin, H.[Hugues],
Feature space region growing,
ICIP12(2585-2588).
IEEE DOI 1302
BibRef

Revol-Muller, C.[Chantal], Rose, J.L.[Jean-Löic], Pacureanu, A., Peyrin, F., Odet, C.,
Shape prior in Variational Region Growing,
IPTA12(116-120)
IEEE DOI 1503
computerised tomography BibRef

Rose, J.L.[Jean-Löic], Revol-Muller, C.[Chantal], Charpigny, D.[Delphine], Odet, C.[Christophe],
Shape prior criterion based on Tchebichef moments in variational region growing,
ICIP09(1081-1084).
IEEE DOI 0911
BibRef

Gomez-Lopera, J.F., Luque-Escamilla, P.L., Martinez-Aroza, J., Roman-Roldan, R., Cabrerizo-Vilchez, M.A., Rodriguez-Valverde, M.A., Montes-Ruiz-Cabello, F.J.,
Entropic segmentation by region growing and merging for drop shape analysis,
LNLA09(98-103).
IEEE DOI 0908
BibRef

Crisp, D.J.,
Improved Data Structures for Fast Region Merging Segmentation Using a Mumford-Shah Energy Functional,
DICTA08(586-592).
IEEE DOI 0812

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

Franek, L.[Lucas], Jiang, X.Y.[Xiao-Yi],
An Instability Problem of Region Growing Segmentation Algorithms and Its Set Median Solution,
ISVC09(II: 737-746).
Springer DOI 0911
BibRef

Xiao, R.[Ru], Wu, J.[Jian], Wu, J.H.[Jian-Hua],
A New Medical Segmentation Method Based on Voronoi Diagrams and Region Growing,
CISP09(1-4).
IEEE DOI 0910
BibRef

Cheng, Y.M.[Yong-Mei], Wu, Y.R.[Yan-Ru], Yang, L.H.[Li-Hua], Zhao, C.H.[Chun-Hui], Zhang, S.W.[Shao-Wu],
Natural Object Recognition Using the Combination of Gaussian Model and Region Growing,
CISP09(1-5).
IEEE DOI 0910
BibRef

Pérez-Carrasco, J.A.[Jose-Antonio], Acha-Pińero, B.[Begońa], Serrano-Gotarredona, C.[Carmen], Gevers, T.[Theo],
Reflectance-Based Segmentation Using Photometric and Illumination Invariants,
ICIAR14(I: 179-186).
Springer DOI 1410
BibRef

Wassenberg, J.[Jan], Middelmann, W.[Wolfgang], Sanders, P.[Peter],
An Efficient Parallel Algorithm for Graph-Based Image Segmentation,
CAIP09(1003-1010).
Springer DOI 0909
BibRef

Shetty, S.[Sanketh], Ahuja, N.[Narendra],
Supervised and Unsupervised Clustering with Probabilistic Shift,
ECCV10(V: 644-657).
Springer DOI 1009
BibRef
Earlier:
A uniformity criterion and algorithm for data clustering,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Rysavy, S.[Steven], Flores, A.[Arturo], Enciso, R.[Reyes], Okada, K.[Kazunori],
Classifiability criteria for refining of random walks segmentation,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Jia, Y.Q.[Yang-Qing], Zhang, C.S.[Chang-Shui],
Learning distance metric for semi-supervised image segmentation,
ICIP08(3204-3207).
IEEE DOI 0810
BibRef

Kumar, N.[Neeraj], Zhang, L.[Li], Nayar, S.K.[Shree K.],
What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?,
ECCV08(II: 364-378).
Springer DOI 0810
Really comparing patches, less segmentation. BibRef

Kim, T.H.[Tae Hoon], Lee, K.M.[Kyoung Mu], Lee, S.U.[Sang Uk],
Generative Image Segmentation Using Random Walks with Restart,
ECCV08(III: 264-275).
Springer DOI 0810

See also Edge-Preserving Colorization Using Data-Driven Random Walks with Restart. BibRef

Prasad, L.[Lakshman], Swaminarayan, S.[Sriram],
Hierarchical image segmentation by polygon grouping,
Tensor08(1-8).
IEEE DOI 0806
BibRef

Haunert, J.H.[Jan-Henrik],
A Formal Model and Mixed-Integer Program for Area Aggregation in Map Generalization,
PIA07(161).
PDF File. 0711
Aggregation of small regions when scale of the map is reduced. BibRef

Gómez, O.[Octavio], González, J.A.[Jesús A.], Morales, E.F.[Eduardo F.],
Image Segmentation Using Automatic Seeded Region Growing and Instance-Based Learning,
CIARP07(192-201).
Springer DOI 0711
BibRef

Torsello, A.[Andrea], di Gesu, M.[Marco], Pelillo, M.[Marcello],
Integrating Boundary Information in Pairwise Segmentation,
CIAP07(23-28).
IEEE DOI 0709
Integrate boundary information to evaluate similar regions. BibRef

di Gesů, V.[Vito], lo Bosco, G.[Giosuč],
Image Segmentation Based on Genetic Algorithms Combination,
CIAP05(352-359).
Springer DOI 0509
BibRef

Rohkohl, C.[Christopher], Engel, K.[Karin],
Efficient Image Segmentation Using Pairwise Pixel Similarities,
DAGM07(254-263).
Springer DOI 0709
BibRef

Galun, M.[Meirav], Basri, R.[Ronen], Brandt, A.[Achi],
Multiscale Edge Detection and Fiber Enhancement Using Differences of Oriented Means,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Bagon, S.[Shai], Brostovski, O.[Ori], Galun, M.[Meirav], Irani, M.[Michal],
Detecting and sketching the common,
CVPR10(33-40).
IEEE DOI 1006
BibRef

Fahad, A.[Ahmed], Morris, T.[Tim],
A Faster Graph-Based Segmentation Algorithm with Statistical Region Merge,
ISVC06(II: 286-293).
Springer DOI 0611
BibRef

Tan, Z.G.[Zhi-Gang], Yung, N.H.C.[Nelson H.C.],
Image segmentation towards natural clusters,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Tan, Z.G.[Zhi-Gang], He, X.C.[Xiao-Chen], Yung, N.H.C.[Nelson H.C.],
A Novel Merging Criterion Incorporating Boundary Smoothness and Region Homogeneity for Image Segmentation,
PSIVT06(238-247).
Springer DOI 0612
BibRef

Gofman, E.,
Developing an Efficient Region Growing Engine for Image Segmentation,
ICIP06(2413-2416).
IEEE DOI 0610
BibRef

de Bock, J.[Johan], Pires, R.[Rui], de Smet, P.[Patrick], Philips, W.[Wilfried],
A Fast Dynamic Border Linking Algorithm for Region Merging,
ACIVS06(232-241).
Springer DOI 0609
BibRef

He, Y.[Yuan], Luo, Y.P.[Yu-Pin], Hu, D.C.[Dong-Cheng],
Seeded Region Merging Based on Gradient Vector Flow for Image Segmentation,
ACIVS06(846-854).
Springer DOI 0609
BibRef

Li, Z.R.[Zhan-Rong], Zhang, J.Q.[Jian-Qing],
Image Segmentation Based on Inscribed circle,
ICPR06(II: 247-250).
IEEE DOI 0609
BibRef

Monay, F.[Florent], Quelhas, P.[Pedro], Odobez, J.M.[Jean-Marc], Gatica-Perez, D.[Daniel],
Integrating Co-Occurrence and Spatial Contexts on Patch Based Scene Segmentation,
BP06(14).
IEEE DOI 0609
BibRef

Micušík, B.[Branislav], Hanbury, A.[Allan],
Automatic Image Segmentation by Positioning a Seed,
ECCV06(II: 468-480).
Springer DOI 0608
BibRef
And:
Template patch driven image segmentation,
BMVC06(II:819).
PDF File. 0609
BibRef
Earlier:
Steerable Semi-automatic Segmentation of Textured Images,
SCIA05(35-44).
Springer DOI 0506
BibRef

Tu, Z.W.[Zhuo-Wen],
An Integrated Framework for Image Segmentation and Perceptual Grouping,
ICCV05(I: 670-677).
IEEE DOI 0510
Swendsen-Wang cut algorithm for segmentation. Grouping by belief propogation. BibRef

Qiu, H.J.[Huai-Jun], Hancock, E.R.[Edwin R.],
Image Segmentation using Commute times,
BMVC05(xx-yy).
HTML Version. 0509
BibRef

Gerstmayer, M.[Michael], Haxhimusa, Y.[Yll], Kropatsch, W.G.[Walter G.],
Hierarchical Interactive Image Segmentation Using Irregular Pyramids,
GbRPR11(245-254).
Springer DOI 1105
BibRef

Qiu, G.P.[Guo-Ping], Lam, K.M.[Kin-Man],
Pulling, Pushing, and Grouping for Image Segmentation,
ICIAR04(I: 65-73).
Springer DOI 0409
BibRef

Wan, S.Y.[Shu-Yen], Chen, J.T.[Jung-Tai], Yeh, S.H.[Shu-Hung],
Efficient fuzzy-connectedness segmentation using symmetric convolution and adaptive thresholding,
ICIP04(II: 905-908).
IEEE DOI 0505
BibRef

Loo, P.K.[Poh Kok], Tan, C.L.[Chew Lim],
Adaptive Region Growing Color Segmentation for Text Using Irregular Pyramid,
DAS04(264-275).
Springer DOI 0505
BibRef

Srinivasan, S.H.,
Small-world approximations in spectral segmentation,
ICPR04(II: 36-39).
IEEE DOI 0409
BibRef

Roggero, M.[Marco],
Object Segmentation with Region Growing and Principal Component Analysis,
PCV02(A: 289). 0305
BibRef

Minagawa, A., Uda, K., Tagawa, N.,
Region extraction based on belief propagation for gaussian model,
ICPR02(II: 507-510).
IEEE DOI 0211
BibRef

Rydberg, A., Borgefors, G.,
Feature based merging of application specific regions,
CIAP01(56-62).
IEEE DOI 0210
BibRef

Ouerhani, N.[Nabil], Archip, N.[Neculai], Hügli, H.[Heinz], Erard, P.J.[Pierre-Jean],
Visual Attention Guided Seed Selection for Color Image Segmentation,
CAIP01(630 ff.).
Springer DOI 0210
BibRef

Yu, Z.Y.[Ze-Yun], Bajaj, C.,
Image segmentation using gradient vector diffusion and region merging,
ICPR02(II: 941-944).
IEEE DOI 0211
BibRef

Yu, Z.Y.[Ze-Yun], Bajaj, C.[Chandrajit],
Normalized Gradient Vector Diffusion and Image Segmentation,
ECCV02(III: 517 ff.).
Springer DOI 0205
initial segmentation using Normalized Gradient Vector Diffusion and region merging based on Region Adjacency Graph.
See also segmentation-free approach for skeletonization of gray-scale images via anisotropic vector diffusion, A. BibRef

Yu, Z.Y.[Ze-Yun], Bajaj, C.,
Anisotropic vector diffusion in image smoothing,
ICIP02(I: 828-831).
IEEE DOI 0210
BibRef

Lee, S.H.[Sang-Hoon], Crawford, M.M.,
Unsupervised Classification Using Spatial Region Growing Segmentation and Fuzzy Training,
ICIP01(I: 770-773).
IEEE DOI 0108
BibRef
Earlier:
Unsupervised multistage segmentation using Markov random field and maximum entropy principle,
ICIP94(II: 192-196).
IEEE DOI 9411
BibRef

Ikonomakis, N., Plataniotis, K.N., Venetsanopoulos, A.N.,
Unsupervised Seed Determination for a Region-based Color Image Segmentation Scheme,
ICIP00(Vol I: 537-540).
IEEE DOI 0008
BibRef

Fontaine, M., Macaire, L., Postaire, J.G.,
Image Segmentation Based on an Original Multiscale Analysis of the Pixel Connectivity Properties,
ICIP00(Vol I: 804-807).
IEEE DOI 0008
BibRef

Sato, M., Lakare, S., Wan, M., Kaufman, A.,
A Gradient Magnitude Based Region Growing Algorithm for Accurate Segmentation,
ICIP00(Vol III: 448-451).
IEEE DOI 0008
BibRef

Tomori, Z.[Zoltan], Marcin, J.[Jozef], Vilim, P.[Peter],
Pyramidal Seeded Region Growing Algorithm and Its Use in Image Segmentation,
CAIP99(395-402).
Springer DOI 9909
BibRef

Ji, S., Park, H.W.,
Image segmentation of color image based on region coherency,
ICIP98(I: 80-83).
IEEE DOI 9810
BibRef

Cuisenaire, O.[Olivier],
Region growing Euclidean distance transforms,
CIAP97(I: 263-270).
Springer DOI 9709
BibRef

Cuisenaire, O., and Macq, B.,
Applications of the Region Growing Euclidean Distance Transform: Anisotropy and Skeletons,
ICIP97(I: 200-203).
IEEE DOI BibRef 9700

Steudel, A., Glesner, M.,
Image coding with fuzzy region-growing segmentation,
ICIP96(II: 955-958).
IEEE DOI 9610
BibRef

Weber, J.[Joseph],
Scene Partitioning via Statistic-Based Region Growing,
SPIE(2421), February 1995, pp. 161-172. BibRef 9502

Shimbashi, T., Kokubo, Y., Shirota, N.,
Region segmentation using edge based circle growing,
ICIP95(III: 65-68).
IEEE DOI 9510
BibRef

Brand, M.,
A short note on local region growing by pseudophysical simulation,
CVPR93(782-783).
IEEE DOI 0403
BibRef

Yu, Y.,
Segmentation coding using edge detection and region merging,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Badii, F., Jayawardena, J.,
Region Growing and Global Labeling in Image Analysis,
ICPR84(656-659). BibRef 8400

Ichikawa, T.,
Hierarchical Smoothing of Grey Tone Images with Adaptive Region Merging Capability,
ICPR80(831-834). BibRef 8000

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Superpixel Region Extraction, Region Growing .


Last update:Mar 16, 2024 at 20:36:19