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).
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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 Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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 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. 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 Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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
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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
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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
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Fiorio, C.,
Sorted Region Merging to Maximize Test Reliability,
ICIP00(Vol I: 808-811).
IEEE DOI 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. 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.
WWW Link. 0506
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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 Link. 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 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.
WWW Link. 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 Link. 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.
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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,
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DOI Link 1406
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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.
WWW Link. Generate the surfaces from slices. BibRef 9009

Udupa, J.K.[Jayaram K.], Samarasekera, S.,
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GMIP(58), No. 3, May 1996, pp. 246-261. 9606
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Saha, P.K.[Punam K.], Udupa, J.K.[Jayaram K.],
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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
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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.
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Zhuge, Y.[Ying], Udupa, J.K.[Jayaram K.], Saha, P.K.[Punam K.],
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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,
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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],
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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
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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
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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-Piñero, B.[Begoña], 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

Skurikhin, A.N.,
Patch-Based Image Segmentation of Satellite Imagery Using Minimum Spanning Tree Construction,
GEOBIA10(xx-yy).
PDF File. 1007
BibRef

Skurikhin, A.N.[Alexei N.],
Proximity Graphs Based Multi-scale Image Segmentation,
ISVC08(I: 298-307).
Springer DOI 0812
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
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Haunert, J.H.[Jan-Henrik],
A Formal Model and Mixed-Integer Program for Area Aggregation in Map Generalization,
PIA07(161).
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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 Gesù, V.[Vito], lo Bosco, G.[Giosuè],
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
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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
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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

Zhang, F.[Fan], Qiu, H.J.[Huai-Jun], Hancock, E.R.[Edwin R.],
Evolving Spanning Trees Using the Heat Equation,
CAIP05(272).
Springer DOI 0509
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

Haxhimusa, Y.[Yll], Ion, A.[Adrian], Kropatsch, W.G.[Walter G.],
Irregular Pyramid Segmentations with Stochastic Graph Decimation Strategies,
CIARP06(277-286).
Springer DOI 0611
BibRef
And:
Evaluating Hierarchical Graph-based Segmentation,
ICPR06(II: 195-198).
IEEE DOI 0609
BibRef
And: A2, A3, A1:
Considerations Regarding the Minimum Spanning Tree Pyramid Segmentation Method (Why Does it Always Find the Lady?),
SSPR06(182-190).
Springer DOI 0608
BibRef

Haxhimusa, Y.[Yll], Ion, A.[Adrian], Kropatsch, W.G.[Walter G.], Illetschko, T.[Thomas],
Evaluating Minimum Spanning Tree Based Segmentation Algorithms,
CAIP05(579).
Springer DOI 0509
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
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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).
WWW Link. 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:Dec 7, 2017 at 17:23:10