8.4 Segmentation by Region Growing Techniques

Chapter Contents (Back)
Region Growing. Segmentation, Region Growing. Segmentation, Region Merging.

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).
WWW Version. 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., Tsuji, S.,
Application of Color Information to Visual Perception,
PR(3), No. 3, October 1971, pp. 307-318.
WWW Version. Color. Segmentation, Color. Color added to region growing. BibRef 7110

Pavlidis, T.,
Segmentation of Pictures and Maps Through Functional Approximation,
CGIP(1), No. 4, December 1972, pp. 360-372.
WWW Version. 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., and Yakimovsky, Y.,
Decision Theory and Artificial Intelligence: I. A Semantics Based Region Analyzer,
AI(5), No. 4, 1974, pp. 349-371.
WWW Version. 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., and Feldman, J.,
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., and Feldman, J.,
On the Recognition of Complex Structures: Computer Software Using AI Applied to Pattern Recognition,
ICPR74(345-353). BibRef 7400

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

Yakimovsky, Y.,
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.,
Region Growing: Childhood and Adolescence,
CGIP(5), No. 3, September 1976, pp. 382-399.
WWW Version. Survey, Segmentation. Segmentation, Survey. BibRef 7609

Freuder, E.C.,
Affinity: A Relative Approach to Region Finding,
CGIP(5), No. 2, June 1976, pp. 254-264.
WWW Version. BibRef 7606

Chen, P.C., and Pavlidis, T.,
Image Segmentation as an Estimation Problem,
CGIP(12), No. 2, February 1980, pp. 153-172.
WWW Version. 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.
WWW Version. Segmentation, Facet Model. Another use of the facet model, it can now segment. BibRef 8401

Pong, T.C.[Ting-Chuen], Shapiro, L.G.[Linda G.], and Haralick, R.M.[Robert M.],
A Facet Model Region Growing Algorithm,
PRIP81(279-284). BibRef 8100

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

Derin, H., Won, C.S.,
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.
WWW Version. 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 Abstract. IEEE Top Reference.
WWW Version. 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 Version. 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 Abstract. IEEE Top Reference.
WWW Version. 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 Abstract. IEEE Top Reference.
WWW Version. 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 Link 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.
WWW Version. 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 Abstract. IEEE Top Reference.
WWW Version. 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 Abstract. IEEE Top Reference. 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 Abstract. IEEE Top Reference. 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.
WWW Version. 0401
BibRef

Chiarello, E., Jolion, J.M., Amoros, C.,
Regions Growing with the Stochastic Pyramid: Application in Landscape Ecology,
PR(29), No. 1, January 1996, pp. 61-75.
WWW Version. 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., Borel, N.,
A Region Growing and Merging Algorithm to Color Segmentation,
PR(30), No. 7, July 1997, pp. 1191-1203.
WWW Version. 9707
BibRef
And: Correction: PR(30), No. 10, October 1997, pp. 1799-1800. BibRef

Kwok, S.H., Constantinides, A.G.,
A Fast Recursive Shortest Spanning Tree for Image Segmentation and Edge-Detection,
IP(6), No. 2, February 1997, pp. 328-332.
IEEE DOI Link 9703
BibRef

Kwok, S.H., Constantinides, A.G., Siu, W.C.,
An Efficient Recursive Shortest Spanning Tree Algorithm Using Linking Properties,
CirSysVideo(14), No. 6, June 2004, pp. 852-863.
IEEE Abstract. IEEE Top Reference. 0407
BibRef

Moghaddamzadeh, A., Bourbakis, N.,
A Fuzzy Region Growing Approach for Segmentation of Color Images,
PR(30), No. 6, June 1997, pp. 867-881.
WWW Version. 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 Abstract. IEEE Top Reference. 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.
HTML Version. 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 Link 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 Top Reference.
WWW Version. 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
BibRef

Shi, J.B.[Jian-Bo], Malik, J.[Jitendra],
Normalized Cuts and Image Segmentation,
PAMI(22), No. 8, August 2000, pp. 888-905.
IEEE Abstract. IEEE Top Reference.
WWW Version. Or:
Postscript Version. 0010
BibRef
Earlier: CVPR97(731-737).
IEEE Abstract. IEEE Top Reference.
WWW Version. 9704
Perceptual Grouping. Award, Longuet-Higgins. (Awarded 10 years later for contributions that withstood the test of time.) Arbitrary shape clusters.
Postscript Version. 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).
WWW Version. Global minimum for segmentation, using graph method. BibRef 9800

Cour, T., Yu, S., and Shi, J.,
Normalized cut image segmenation software,
Online2006.
WWW Version. 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 Link 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 Link 0110
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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
BibRef

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.
WWW Version. 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 Link 0308
BibRef
Earlier: ICIP00(Vol II: 96-99).
IEEE Abstract. IEEE Top Reference. 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 Abstract. IEEE Top Reference. 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.
WWW Version. 0308
BibRef

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.
WWW Version. 0308
BibRef

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 Link 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. IEEE Top Reference. 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 Link 0410
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Earlier:
Region Growing Based on Extended Gradient Vector Flow Field Model for Multiple Objects Segmentation,
ICIP01(III: 74-77).
IEEE Abstract. IEEE Top Reference. 0108
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Nock, R.[Richard], Nielsen, F.,
Statistical Region Merging,
PAMI(26), No. 11, November 2004, pp. 1452-1458.
IEEE Abstract. IEEE Top Reference. 0410
BibRef
Earlier:
On region merging: the statistical soundness of fast sorting, with applications,
CVPR03(II: 19-26).
IEEE Abstract. IEEE Top Reference. 0307
Analysis of merging in a particular order. See also Semi-supervised statistical region refinement for color image segmentation. BibRef

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

Fiorio, C.,
Sorted Region Merging to Maximize Test Reliability,
ICIP00(Vol I: 808-811).
IEEE Abstract. IEEE Top Reference. 0008
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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. IEEE Top Reference. 0506
BibRef

Barbu, A., Zhu, S.C.[Song-Chun],
Multigrid and Multi-Level Swendsen-Wang Cuts for Hierarchic Graph Partition,
CVPR04(II: 731-738).
IEEE Abstract. IEEE Top Reference. 0408
BibRef
Earlier:
Graph partition by Swendsen-Wang cuts,
ICCV03(320-327).
IEEE DOI Link 0311
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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.
WWW Version. 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.
WWW Version. 0509
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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 Link 0510
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Grady, L.[Leo],
Random Walks for Image Segmentation,
PAMI(28), No. 11, November 2006, pp. 1768-1783.
IEEE DOI Link 0609
BibRef
Earlier:
Multilabel Random Walker Image Segmentation Using Prior Models,
CVPR05(I: 763-770).
IEEE DOI Link 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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., 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 Link
WWW Version. 0910
Color image segmentation; Region-growing; Outdoors scenes; Gradient Network Method BibRef

Udupa, J.K., and Ajjanagadde, V.G.,
Boundary and Object Labelling in Three-Dimensional Images,
CVGIP(51), No. 3, September 1990, pp. 355-369.
WWW Version. Generate the surfaces from slices. BibRef 9009

Udupa, J.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.
WWW Version. 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
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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. 0003

WWW Version. 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.
WWW Version. 0601
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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 Link
WWW Version. 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.
WWW Version. 0001
Fuzzy connectedness: assign strength to every path between every pair of elements. BibRef

Udupa, J.K.[Jayaram K.], Saha, P.K.[Punam K.], Lotufo, R.A.[Roberto A.],
Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation,
PAMI(24), No. 11, November 2002, pp. 1485-1500.
IEEE Abstract. IEEE Top Reference. 0211
See also Disclaimer: Relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. BibRef

Udupa, J.K.[Jayaram K.], Saha, P.K.[Punam K.],
Fuzzy connectedness and image segmentation,
PIEEE(91), No. 10, October 2003, pp. 1649-1669.
IEEE DOI Link 0310
BibRef

Ciesielski, K.C.[Krzysztof Chris], Udupa, J.K.[Jayaram K.], Saha, P.K.[Punam K.], Zhuge, Y.[Ying],
Iterative relative fuzzy connectedness for multiple objects with multiple seeds,
CVIU(107), No. 3, September 2007, pp. 160-182.
WWW Version. 0709
Image segmentation; Path strength; Path connectedness; Fuzzy connectedness Baed on strength of connection between each pair of points. 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. IEEE Top Reference. 0402
BibRef

Yu, Q.Y.[Qi-Yao], Clausi, D.A.[David A.],
SAR Sea-Ice Image Analysis Based on Iterative Region Growing Using Semantics,
GeoRS(45), No. 12, December 2007, pp. 3919-3931.
IEEE DOI Link 0711
BibRef
Earlier:
Joint Image Segmentation and Interpretation Using Iterative Semantic Region Growing on SAR Sea Ice Imagery,
ICPR06(II: 223-226).
WWW Version. 0609
BibRef
And:
Filament Preserving Segmentation for SAR Sea Ice Imagery Using a New Statistical Model,
ICPR06(IV: 849-852).
WWW Version. 0609
BibRef
Earlier:
Combining Local and Global Features for Image Segmentation Using Iterative Classification and Region Merging,
CRV05(579-586).
IEEE DOI Link 0505
BibRef

Yang, X.Z.[Xue-Zhi], Clausi, D.A.[David A.],
SAR Sea Ice Image Segmentation Based on Edge-preserving Watersheds,
CRV07(426-431).
IEEE DOI Link 0705
BibRef

Yu, Q.Y.[Qi-Yao], Clausi, D.A.[David A.],
IRGS: Image Segmentation Using Edge Penalties and Region Growing,
PAMI(30), No. 12, December 2008, pp. 2126-2139.
IEEE DOI Link 0811
Iterative Region Growing using Semantics. BibRef

Ding, J., Ma, R., Chen, S.,
A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation,
IP(17), No. 2, February 2008, pp. 204-216.
IEEE DOI Link 0801
adaptive spatial scale and an appropriate intensity-difference scale For object extraction and figure-ground. BibRef

Castilla, G.[Guillermo], Hay, G.G.[Geoffrey G.], Ruiz-Gallardo, J.R.[Jose R.],
Size-constrained Region Merging (SCRM): An Automated Delineation Tool for Assisted Photointerpretation,
PhEngRS(74), No. 4, April 2008, pp. 409-420.
WWW Version. 0804
Generation of an initial template for assisted photointerpretation including rationale and implementation with illustrated examples. BibRef

Chan, D.Y.[Din-Yuen], Lin, C.H.[Chih-Hsueh], Hsieh, W.S.[Wen-Shyong],
Image Segmentation with Fast Wavelet-Based Color Segmenting and Directional Region Growing,
IEICE(E88-D), No. 10, October 2005, pp. 2249-2259.
WWW Version. 0510
BibRef

Regentova, E.[Emma], Yao, D.S.[Dong-Sheng], Latifi, S.[Shahram], Zheng, J.[Jun],
Image Segmentation Using Ncut In The Wavelet Domain,
IJIG(6), No. 4, October 2006, pp. 569-582. 0610
BibRef

Garduño, E.[Edgar], Herman, G.T.[Gabor T.],
Parallel fuzzy segmentation of multiple objects,
IJIST(18), No. 5-6, 2008, pp. 336-344.
WWW Version. 0804
Segmentation with fuzzy connectedness. BibRef

Fu, Z.Y.[Zhou-Yu], Robles-Kelly, A.[Antonio],
A quadratic programming approach to image labelling,
IET-CV(2), No. 4, December 2008, pp. 193-207.
WWW Version. 0905
BibRef
Earlier:
A fast hierarchical approach to image segmentation,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef

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 Link 0711
BibRef

Robles-Kelly, A.[Antonio],
Segmentation via Graph-Spectral Methods and Riemannian Geometry,
CAIP05(661).
Springer DOI Link 0509
BibRef

Ghosh, S.[Susmita], Kothari, M.[Megha], Halder, A.[Anindya], Ghosh, A.[Ashish],
Use of aggregation pheromone density for image segmentation,
PRL(30), No. 10, 15 July 2009, pp. 939-949.
Elsevier DOI Link
WWW Version. 0906
BibRef
Earlier: A1, A2, A4, Only:
Aggregation Pheromone Density Based Image Segmentation,
ICCVGIP06(118-127).
Springer DOI Link 0612
Aggregation pheromone; Ant colony optimization; Clustering; Image segmentation BibRef

Garcia Ugarriza, L., Saber, E., Vantaram, S.R., Amuso, V., Shaw, M., Bhaskar, R.,
Automatic Image Segmentation by Dynamic Region Growth and Multiresolution Merging,
IP(18), No. 10, October 2009, pp. 2275-2288.
IEEE DOI Link 0909
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Aptoula, E.[Erchan], Lefèvre, S.[Sébastien],
Morphological Description of Color Images for Content-Based Image Retrieval,
IP(18), No. 11, November 2009, pp. 2505-2517.
IEEE DOI Link 0911
BibRef
Earlier:
A Basin Morphology Approach to Colour Image Segmentation by Region Merging,
ACCV07(I: 935-944).
Springer DOI Link 0711
Color image segmentation in the context of morphology. See also alpha-Trimmed lexicographical extrema for pseudo-morphological image analysis. BibRef

Mendoza, C.S.[Carlos S.], Acha, B.[Begoña], Serrano, C.[Carmen], Gómez-Cía, T.[Tomás],
Self-assessed Contrast-Maximizing Adaptive Region Growing,
ACIVS09(652-663).
Springer DOI Link 0909
BibRef

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