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), 1976, pp. 382-399. Survey, Segmentation. Segmentation, Survey. BibRef 7600

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), 1980, pp. 153-172. BibRef 8000

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

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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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. 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,
Online
WWW Version. Code, Segmentation. Matlab Code for segmentation and clustering. C code for segmentation. See also Normalized Cuts and Image Segmentation. BibRef 0000

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

Guigues, L.[Laurent], Le Men, H.[Herv�], 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.
WWW Version. 0308 BibRef
Earlier: ICIP00(Vol II: 96-99).
IEEE Abstract. IEEE Top Reference. 0008Define 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.[St�phane], 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.
WWW Version. 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. 0311Group 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.
WWW Version. 0410 BibRef
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 BibRef

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. 0307Analysis 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 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. 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).
WWW Version. 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 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 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.
WWW Version. 0510 BibRef

Grady, L.[Leo],
Random Walks for Image Segmentation,
PAMI(28), No. 11, November 2006, pp. 1768-1783.
WWW Version. 0609 BibRef
Earlier:
Multilabel Random Walker Image Segmentation Using Prior Models,
CVPR05(I: 763-770).
WWW Version. 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. 0609Image 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. 0706Partition; 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. 0709Region-growing segmentation; Natural color scenes; Color gradient networks 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 BibRef

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 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. 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 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. 0001Fuzzy 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 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.
WWW Version. 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. 0709Image 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.
WWW Version. 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).
WWW Version. 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).
WWW Version. 0705 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.
WWW Version. 0801adaptive spatial scale and an appropriate intensity-difference scale For object extraction and figure-ground. BibRef

Aptoula, E.[Erchan], Lef�vre, S.[S�bastien],
alpha-Trimmed lexicographical extrema for pseudo-morphological image analysis,
JVCIR(19), No. 3, April 2008, pp. 165-174.
WWW Version. 0803 BibRef
Earlier:
A Basin Morphology Approach to Colour Image Segmentation by Region Merging,
ACCV07(I: 935-944).
WWW Version. 0711Multivariate mathematical morphology; Vector ordering; Vectorial processing; Colour morphology 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. 0804Generation of an initial template for assisted photointerpretation including rationale and implementation with illustrated examples. BibRef

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

Nagahashi, T.[Tomoyuki], Fujiyoshi, H.[Hironobu], Kanade, T.[Takeo],
Image Segmentation Using Iterated Graph Cuts Based on Multi-scale Smoothing,
ACCV07(II: 806-816).
WWW Version. 0711 BibRef

G�mez, O.[Octavio], Gonz�lez, J.A.[Jes�s A.], Morales, E.F.[Eduardo F.],
Image Segmentation Using Automatic Seeded Region Growing and Instance-Based Learning,
CIARP07(192-201).
WWW Version. 0711 BibRef

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

di Ges�, V.[Vito], lo Bosco, G.[Giosu�],
Image Segmentation Based on Genetic Algorithms Combination,
CIAP05(352-359).
WWW Version. 0509 BibRef

Rohkohl, C.[Christopher], Engel, K.[Karin],
Efficient Image Segmentation Using Pairwise Pixel Similarities,
DAGM07(254-263).
WWW Version. 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).
WWW Version. 0710 BibRef

Alpert, S.[Sharon], Galun, M.[Meirav], Basri, R.[Ronen], Brandt, A.[Achi],
Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration,
CVPR07(1-8).
WWW Version. 0706 BibRef

Fahad, A.[Ahmed], Morris, T.[Tim],
A Faster Graph-Based Segmentation Algorithm with Statistical Region Merge,
ISVC06(II: 286-293).
WWW Version. 0611 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).
WWW Version. 0612 BibRef

Ghosh, S.[Susmita], Kothari, M.[Megha], Ghosh, A.[Ashish],
Aggregation Pheromone Density Based Image Segmentation,
ICCVGIP06(118-127).
WWW Version. 0612 BibRef

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

Cai, W.C.[Wen-Chao], Wu, J.[Jue], Chung, A.C.S.,
Shape-Based Image Segmentation Using Normalized Cuts,
ICIP06(1101-1104). 0610
WWW Version. 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).
WWW Version. 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).
WWW Version. 0609 BibRef

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

Zhang, L.[Lei], Wang, X.[Xun], Penwarden, N.[Nicholas], Ji, Q.[Qiang],
An Image Segmentation Framework Based on Patch Segmentation Fusion,
ICPR06(II: 187-190).
WWW Version. 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).
WWW Version. 0609 BibRef

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

Tu, Z.W.[Zhuo-Wen],
An Integrated Framework for Image Segmentation and Perceptual Grouping,
ICCV05(I: 670-677).
WWW Version. 0510Swendsen-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).
WWW Version. 0509 BibRef

Qiu, H.J.[Huai-Jun], Hancock, E.R.[Edwin R.],
Image Segmentation using Commute times,
BMVC05(xx-yy).
HTML Version. 0509 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).
WWW Version. 0711 BibRef

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

Haxhimusa, Y.[Yll], Ion, A.[Adrian], Kropatsch, W.G.[Walter G.],
Irregular Pyramid Segmentations with Stochastic Graph Decimation Strategies,
CIARP06(277-286).
WWW Version. 0611 BibRef
And:
Evaluating Hierarchical Graph-based Segmentation,
ICPR06(II: 195-198).
WWW Version. 0609 BibRef
And:
Considerations Regarding the Minimum Spanning Tree Pyramid Segmentation Method (Why Does it Always Find the Lady?),
SSPR06(182-190).
WWW Version. 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).
WWW Version. 0509 BibRef

Qiu, G.P.[Guo-Ping], Lam, K.M.[Kin-Man],
Pulling, Pushing, and Grouping for Image Segmentation,
ICIAR04(I: 65-73).
WWW Version. 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).
WWW Version. 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).
WWW Version. 0505 BibRef

Srinivasan, S.H.,
Small-world approximations in spectral segmentation,
ICPR04(II: 36-39).
WWW Version. 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).
WWW Version. 0211 BibRef

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

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

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

Yu, Z.Y.[Ze-Yun], Bajaj, C.[Chandrajit],
Normalized Gradient Vector Diffusion and Image Segmentation,
ECCV02(III: 517 ff.).
HTML Version. 0205initial 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 Abstract. IEEE Top Reference. 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 Abstract. IEEE Top Reference. 0108 BibRef
Earlier:
Unsupervised multistage segmentation using Markov random field and maximum entropy principle,
ICIP94(II: 192-196).
WWW Version. 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 Abstract. IEEE Top Reference. 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 Abstract. IEEE Top Reference. 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 Abstract. IEEE Top Reference. 0008 BibRef

Mathevet, S., Trassoudaine, L., Alizon, J., Checchin, P.,
Combination of Image Segmentation Into Regions,
SCIA99(Image Analysis II). BibRef 9900

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

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

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

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

Steudel, A., Glesner, M.,
Image coding with fuzzy region-growing segmentation,
ICIP96(II: 955-958).
WWW Version. 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).
WWW Version. 9510 BibRef

Brand, M.,
A short note on local region growing by pseudophysical simulation,
CVPR93(782-783).
IEEE Abstract. IEEE Top Reference. 0403 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
Watershed Algorithms, Watershed Segmentation .

Last update:May 8, 2008 at 19:01:47