8.4 Segmentation by Region Growing Techniques

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Osman, H., Blostein, S.D.,
Probabilistic Winner-Take-All Segmentation of Images with Application to Ship Detection,
SMC-B(30), No. 3, June 2000, pp. 485-490.
IEEE Top Reference. 0006
BibRef

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
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.
Elsevier DOI 0304

See also Scale-Sets Image Analysis. BibRef

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

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

Lallich, S.[Stéphane], Muhlenbach, F.[Fabrice], Jolion, J.M.[Jean-Michel],
A test to control a region growing process within a hierarchical graph,
PR(36No. 10, October 2003, pp. 2201-2211.
Elsevier DOI 0308
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.
Elsevier DOI 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 0301
BibRef

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Carvalho, L.E., Mantelli Neto, S.L., Sobieranski, A.C., Comunello, E., von Wangenheim, A.,
Improving Graph-Based Image Segmentation Using Nonlinear Color Similarity Metrics,
IJIG(15), No. 04, 2015, pp. 1550018.
DOI Link 1509
BibRef

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

Udupa, J.K.[Jayaram K.], Samarasekera, S.,
Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation,
GMIP(58), No. 3, May 1996, pp. 246-261. 9606
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.
DOI Link Extension of previous theory for fuzzy connections. Each pair has a connectedness strength. The maximum of path strengths of minimum of affinities along each path is the only valid measure. 0110
BibRef

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

Zhuge, Y.[Ying], Udupa, J.K.[Jayaram K.], Saha, P.K.[Punam K.],
Vectorial scale-based fuzzy-connected image segmentation,
CVIU(101), No. 3, March 2006, pp. 177-193.
Elsevier DOI 0601
BibRef

Ciesielski, K.C.[Krzysztof Chris], Udupa, J.K.[Jayaram K.],
Affinity functions in fuzzy connectedness based image segmentation I: Equivalence of affinities,
CVIU(114), No. 1, January 2010, pp. 146-154.
Elsevier DOI 1001
Affinity; Fuzzy connectedness; Image segmentation; Equivalence of algorithms BibRef

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

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

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

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

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

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

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

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 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.
Elsevier DOI 0709
Image segmentation; Path strength; Path connectedness; Fuzzy connectedness Baed on strength of connection between each pair of points. 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 0711
Sea Ice. BibRef
Earlier:
Joint Image Segmentation and Interpretation Using Iterative Semantic Region Growing on SAR Sea Ice Imagery,
ICPR06(II: 223-226).
IEEE DOI 0609
BibRef
And:
Filament Preserving Segmentation for SAR Sea Ice Imagery Using a New Statistical Model,
ICPR06(IV: 849-852).
IEEE DOI 0609
BibRef
Earlier:
Combining Local and Global Features for Image Segmentation Using Iterative Classification and Region Merging,
CRV05(579-586).
IEEE DOI 0505
BibRef

Yang, X.Z.[Xue-Zhi], Clausi, D.A.[David A.],
SAR sea ice image segmentation using an edge-preserving region-based MRF,
ICIP09(1721-1724).
IEEE DOI 0911
BibRef
Earlier:
SAR Sea Ice Image Segmentation Based on Edge-preserving Watersheds,
CRV07(426-431).
IEEE DOI 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 0811
Iterative Region Growing using Semantics. BibRef

Qin, A.K., Clausi, D.A.[David A.],
Multivariate Image Segmentation Using Semantic Region Growing with Adaptive Edge Penalty,
IP(19), No. 8, August 2010, pp. 2157-2170.
IEEE DOI 1008
BibRef

Yu, P., Qin, A.K., Clausi, D.A.,
Unsupervised Polarimetric SAR Image Segmentation and Classification Using Region Growing With Edge Penalty,
GeoRS(50), No. 4, April 2012, pp. 1302-1317.
IEEE DOI 1204
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 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 Link. 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.
DOI Link 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.
DOI Link 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.
DOI Link 0905
BibRef
Earlier:
A fast hierarchical approach to image segmentation,
ICPR08(1-4).
IEEE DOI 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 0711
BibRef

Robles-Kelly, A.[Antonio],
Segmentation via Graph-Spectral Methods and Riemannian Geometry,
CAIP05(661).
Springer DOI 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 0906
BibRef
Earlier: A1, A2, A4, Only:
Aggregation Pheromone Density Based Image Segmentation,
ICCVGIP06(118-127).
Springer DOI 0612
Aggregation pheromone; Ant colony optimization; Clustering; Image segmentation BibRef

Halder, A.[Anindya], Ghosh, S.[Susmita], Ghosh, A.[Ashish],
Aggregation pheromone metaphor for semi-supervised classification,
PR(46), No. 8, August 2013, pp. 2239-2248.
Elsevier DOI 1304
Semi-supervised classification; Self-training; Ant colony; Aggregation pheromone 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 0909
BibRef

Vantaram, S.R.[Sreenath Rao], Saber, E.[Eli], Dianat, S.A.[Sohail A.], Shaw, M.[Mark], Bhaskar, R.[Ranjit],
An adaptive and progressive approach for efficient Gradient-based multiresolution color image segmentation,
ICIP09(2369-2372).
IEEE DOI 0911
Rochester IT. BibRef

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 0911
BibRef
Earlier:
A Basin Morphology Approach to Colour Image Segmentation by Region Merging,
ACCV07(I: 935-944).
Springer DOI 0711
Color image segmentation in the context of morphology.
See also alpha-Trimmed lexicographical extrema for pseudo-morphological image analysis. BibRef

Aptoula, E.[Erchan],
Remote Sensing Image Retrieval With Global Morphological Texture Descriptors,
GeoRS(52), No. 5, May 2014, pp. 3023-3034.
IEEE DOI 1403
Context BibRef

Aptoula, E., Courty, N., Lefèvre, S.[Sébastien],
An end-member based ordering relation for the morphological description of hyperspectral images,
ICIP14(5097-5101)
IEEE DOI 1502
Accuracy BibRef

Aptoula, E.[Erchan], Pham, M.T.[Minh-Tan], Lefèvre, S.[Sébastien],
Quasi-Flat Zones for Angular Data Simplification,
ISMM17(342-354).
Springer DOI 1706
BibRef

Aptoula, E.[Erhan], Weber, J.[Jonathan], Lefèvre, S.[Sébastien],
Vectorial Quasi-flat Zones for Color Image Simplification,
ISMM13(231-242).
Springer DOI 1305
BibRef

Bosilj, P.[Petra], Aptoula, E.[Erchan], Lefèvre, S.[Sébastien], Kijak, E.[Ewa],
Retrieval of Remote Sensing Images with Pattern Spectra Descriptors,
IJGI(5), No. 12, 2016, pp. 228.
DOI Link 1612
BibRef

Wu, J.[Jue], Cai, W.C.[Wen-Chao], Chung, A.C.S.[Albert C.S.],
POSIT: Part-based object segmentation without intensive training,
PR(43), No. 3, March 2010, pp. 676-684.
Elsevier DOI 1001
BibRef
Earlier: A2, A1, A3:
Shape-Based Image Segmentation Using Normalized Cuts,
ICIP06(1101-1104).
IEEE DOI 0610
Object segmentation; Training; Horse and cow segmentation; Part-based model BibRef

Shi, W., Liu, K., Huang, C.,
A Fuzzy-Topology-Based Area Object Extraction Method,
GeoRS(48), No. 1, January 2010, pp. 147-154.
IEEE DOI 1001
BibRef

Calderero, F.[Felipe], Marques, F.[Ferran],
Region Merging Techniques Using Information Theory Statistical Measures,
IP(19), No. 6, June 2010, pp. 1567-1586.
IEEE DOI 1006
BibRef
And:
Region merging parameter dependency as information diversity to create sparse hierarchies of partitions,
ICIP10(2237-2240).
IEEE DOI 1009
BibRef
Earlier:
General region merging approaches based on information theory statistical measures,
ICIP08(3016-3019).
IEEE DOI 0810
BibRef

Samsudin, N.A.[Noor A.], Bradley, A.P.[Andrew P.],
Nearest neighbour group-based classification,
PR(43), No. 10, October 2010, pp. 3458-3467.
Elsevier DOI 1007
BibRef
Earlier:
Group-based meta-classification,
ICPR08(1-4).
IEEE DOI 0812
Group-based classification; Nearest neighbour; Compound classification Label groups of homogeneous samples rather than single samples BibRef

Zhang, L.[Lei], Ji, Q.A.[Qi-Ang],
Image Segmentation with a Unified Graphical Model,
PAMI(32), No. 8, August 2010, pp. 1406-1425.
IEEE DOI 1007
Both causal and noncausal relationships among random variables. Conditional Random Field model, multilayer Bayesian Network. BibRef

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

Zhang, L.[Lei], Ji, Q.A.[Qi-Ang],
A Bayesian Network Model for Automatic and Interactive Image Segmentation,
IP(20), No. 9, September 2011, pp. 2582-2593.
IEEE DOI 1109
BibRef

Zhang, L.[Lei], Ji, Q.A.[Qi-Ang],
A multiscale hybrid model exploiting heterogeneous contextual relationships for image segmentation,
CVPR09(2828-2835).
IEEE DOI 0906
BibRef
Earlier:
Integration of multiple contextual information for image segmentation using a Bayesian Network,
SLAM08(1-6).
IEEE DOI 0806
BibRef

Cheng, M.M.[Ming-Ming], Zhang, G.X.[Guo-Xin],
Connectedness of Random Walk Segmentation,
PAMI(33), No. 1, January 2011, pp. 200-202.
IEEE DOI 1011

See also Random Walks for Image Segmentation. Prior conclusions regarding connectedness may not be true. BibRef

Peng, B.[Bo], Zhang, L.[Lei], Zhang, D.[David], Yang, J.[Jian],
Image segmentation by iterated region merging with localized graph cuts,
PR(44), No. 10-11, October-November 2011, pp. 2527-2538.
Elsevier DOI 1101
BibRef
Earlier: A1, A2, A4, Only:
Iterated Graph Cuts for Image Segmentation,
ACCV09(II: 677-686).
Springer DOI 0909
Image segmentation; Graph cuts; Region merging BibRef

Peng, B.[Bo], Zhang, L.[Lei], Zhang, D.[David],
A survey of graph theoretical approaches to image segmentation,
PR(46), No. 3, March 2013, pp. 1020-1038.
Elsevier DOI 1212
Survey, Segmentation. Image segmentation; Graph theoretical methods; Minimal spanning tree; Graph cut BibRef

Peng, B.[Bo], Zhang, L.[Lei], Zhang, D.[David],
Automatic Image Segmentation by Dynamic Region Merging,
IP(20), No. 12, December 2011, pp. 3592-3605.
IEEE DOI 1112
BibRef

Alpert, S.[Sharon], Galun, M.[Meirav], Brandt, A.[Achi], Basri, R.[Ronen],
Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration,
PAMI(34), No. 2, February 2012, pp. 315-327.
IEEE DOI 1112
BibRef
Earlier: A1, A2, A4, A3: CVPR07(1-8).
IEEE DOI 0706
Bottom-up proces, incrementally merge pixels. Probabilistic test to merge adjacent regions. Linear in number of pixels. BibRef

Ofir, N., Galun, M.[Meirav], Nadler, B.[Boaz], Basri, R.[Ronen],
Fast Detection of Curved Edges at Low SNR,
CVPR16(213-221)
IEEE DOI 1612
BibRef

Wang, Y.Q.[Yi-Qing], Trouvé, A.[Alain], Amit, Y.[Yali], Nadler, B.[Boaz],
Detecting Curved Edges in Noisy Images in Sublinear Time,
JMIV(59), No. 3, November 2017, pp. 373-393.
Springer DOI 1710
BibRef

Alpert, S.[Sharon], Galun, M.[Meirav], Nadler, B.[Boaz], Basri, R.[Ronen],
Detecting Faint Curved Edges in Noisy Images,
ECCV10(IV: 750-763).
Springer DOI 1009
BibRef

Mendoza, C.S.[Carlos S.], Acha, B.[Begoña], Serrano, C.[Carmen], Gómez-Cía, T.[Tomás],
Fast parameter-free region growing segmentation with application to surgical planning,
MVA(23), No. 1, January 2012, pp. 165-177.
WWW Link. 1201
BibRef

Chen, J.J.[Jiann-Jone], Su, C.R.[Chun-Rong], Grimson, W.E.L., Liu, J.L.[Jun-Lin], Shiue, D.H.[De-Hui],
Object Segmentation of Database Images by Dual Multiscale Morphological Reconstructions and Retrieval Applications,
IP(21), No. 2, February 2012, pp. 828-843.
IEEE DOI 1201
BibRef
Earlier: A2, A1, A4, A5, Only:
Reconfigurable Peer-to-Peer network Image Retrieval,
VCIP11(1-4).
IEEE DOI 1201
Define the background to extract the object. BibRef

Tilton, J.C., Tarabalka, Y., Montesano, P.M., Gofman, E.,
Best Merge Region-Growing Segmentation With Integrated Nonadjacent Region Object Aggregation,
GeoRS(50), No. 11, November 2012, pp. 4454-4467.
IEEE DOI 1210
BibRef

Dawoud, A.[Amer], Netchaev, A.[Anton],
Fusion of visual cues of intensity and texture in Markov random fields image segmentation,
IET-CV(6), No. 6, 2012, pp. 603-609.
DOI Link 1301
BibRef
Earlier:
Fusion of Edge Information in Markov Random Fields Region Growing Image Segmentation,
ICIAR10(I: 96-104).
Springer DOI 1006
BibRef

Han, Y.[Yu], Feng, X.C.[Xiang-Chu], Baciu, G.[George],
Variational and PCA based natural image segmentation,
PR(46), No. 7, July 2013, pp. 1971-1984.
Elsevier DOI 1303
Image segmentation; Principal component analysis; Region competition; Variable splitting; Iterative reweighting BibRef

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

Weber, J.[Jonathan], Lefèvre, S.[Sébastien],
Fast quasi-flat zones filtering using area threshold and region merging,
JVCIR(24), No. 3, April 2013, pp. 397-409.
Elsevier DOI 1303
Quasi-flat zones; Mathematical Morphology; Quasi-flat zones filtering; Image segmentation; Image simplification; Interactive segmentation; Video segmentation; Oversegmentation reduction BibRef

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

Sáez, A.[Aurora], Serrano, C.[Carmen], Acha, B.[Begoña],
Normalized Cut optimization based on color perception findings: A comparative study,
MVA(25), No. 7, October 2014, pp. 1813-1823.
WWW Link. 1410

Springer DOI For color segmentation.
See also Normalized Cuts and Image Segmentation. BibRef

Kalinin, P.[Pavel], Sirota, A.[Aleksandr],
A graph based approach to hierarchical image over-segmentation,
CVIU(130), No. 1, 2015, pp. 80-86.
Elsevier DOI 1411
Segmentation BibRef

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

García, J.F.G.[Juan F. García], Venegas-Andraca, S.E.[Salvador E.],
Region-based approach for the spectral clustering Nyström approximation with an application to burn depth assessment,
MVA(26), No. 2-3, April 2015, pp. 353-368.
Springer DOI 1504
BibRef

Fan, M.J.[Min-Jie], Lee, T.C.M.,
Variants of seeded region growing,
IET-IPR(9), No. 6, 2015, pp. 478-485.
DOI Link 1507
image segmentation BibRef

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

Rzeszutek, R.[Richard], Androutsos, D.[Dimitrios],
Propagating sparse labels through edge-aware filters,
SIViP(9), No. 1 Supp, December 2015, pp. 17-24.
Springer DOI 1601
BibRef

Maggiori, E.[Emmanuel], Tarabalka, Y.[Yuliya], Charpiat, G.[Guillaume],
Optimizing Partition Trees for Multi-Object Segmentation with Shape Prior,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Baltaxe, M.[Michael], Meer, P.[Peter], Lindenbaum, M.[Michael],
Local Variation as a Statistical Hypothesis Test,
IJCV(117), No. 2, April 2016, pp. 131-141.
Springer DOI 1604
Oversegmentation. BibRef

Li, Q.W.[Qian-Wen], Wei, Z.H.[Zhi-Hua], Zhao, C.R.[Cai-Rong],
Optimized Automatic Seeded Region Growing Algorithm with Application to ROI Extraction,
IJIG(17), No. 04, 2017, pp. 1750024.
DOI Link 1711
BibRef

Zhou, D.G.[Dong-Guo], Shao, Y.H.[Yan-Hua],
Region growing for image segmentation using an extended PCNN model,
IET-IPR(12), No. 5, May 2018, pp. 729-737.
DOI Link 1804
BibRef

Gemme, L.[Laura], Dellepiane, S.G.[Silvana G.],
An Automatic Data-Driven Method for SAR Image Segmentation in Sea Surface Analysis,
GeoRS(56), No. 5, May 2018, pp. 2633-2646.
IEEE DOI 1805
BibRef
Earlier:
A New Graph-Based Method for Automatic Segmentation,
CIAP15(I:601-611).
Springer DOI 1511
Cost function, Image segmentation, Marine vehicles, Oils, Radar imaging, Robustness, Synthetic aperture radar, unsupervised BibRef

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

Li, C.M.[Cheng-Ming], Yin, Y.[Yong], Liu, X.L.[Xiao-Li], Wu, P.[Pengda],
An Automated Processing Method for Agglomeration Areas,
IJGI(7), No. 6, 2018, pp. xx-yy.
DOI Link 1806
Some of the same issues as merging regions. BibRef

Tang, H.[Hong], Zhai, X.J.[Xue-Jun], Huang, W.[Wei],
Edge Dependent Chinese Restaurant Process for Very High Resolution (VHR) Satellite Image Over-Segmentation,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Su, T.F.[Teng-Fei],
Scale-variable region-merging for high resolution remote sensing image segmentation,
PandRS(147), 2019, pp. 319-334.
Elsevier DOI 1901
High resolution remote sensing imagery, Image segmentation, Region merging, Scale-variable BibRef

Su, T.F.[Teng-Fei], Liu, T.X.[Ting-Xi], Zhang, S.W.[Sheng-Wei], Qu, Z.Y.[Zhong-Yi], Li, R.P.[Rui-Ping],
Machine learning-assisted region merging for remote sensing image segmentation,
PandRS(168), 2020, pp. 89 - 123.
Elsevier DOI 2009
Random forest, Machine learning, Region merging, Merging criterion, Image segmentation, Remote sensing BibRef

Dekavalla, M.[Maria], Argialas, D.[Demetre],
A Region Merging Segmentation with Local Scale Parameters: Applications to Spectral and Elevation Data,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Fasquel, J.B.[Jean-Baptiste], Delanoue, N.[Nicolas],
Approach for sequential image interpretation using a priori binary perceptual topological and photometric knowledge and k-means-based segmentation,
JOSA-A(35), No. 6, June 2018, pp. 936-945.
DOI Link 1912
Digital image processing, Image analysis, Anisotropic diffusion, Image analysis, Image processing, Reconstruction algorithms BibRef

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

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

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

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

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

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

Zhao, Q.H.[Quan-Hua], Zhang, H.Y.[Hong-Yun], Wang, G.H.[Guang-Hui], Li, Y.[Yu],
Irregular Tessellation and Statistical Modeling Based Regionalized Segmentation for SAR Intensity Image,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Song, Y.Y.[Yang-Yang], Peng, G.H.[Guo-Hua],
Fast two-stage segmentation model for images with intensity inhomogeneity,
VC(36), No. 6, June 2020, pp. 1189-1202.
WWW Link. 2005
BibRef

Shrivastava, N.[Neeraj], Bharti, J.[Jyoti],
Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review,
IJIG(20), No. 3, July 2020, pp. 2050018.
DOI Link 2008
BibRef

Chen, F.[Fang], Wang, N.[Ning], Yu, B.[Bo], Qin, Y.C.[Yu-Chu], Wang, L.[Lei],
A Strategy of Parallel Seed-Based Image Segmentation Algorithms for Handling Massive Image Tiles over the Spark Platform,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Wang, H.Y.[Hao-Yu], Shen, Z.F.[Zhan-Feng], Zhang, Z.H.[Zi-Han], Xu, Z.[Zeyu], Li, S.[Shuo], Jiao, S.H.[Shu-Hui], Lei, Y.T.[Ya-Ting],
Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Jiao, J.J.[Jian-Jun], Wang, X.P.[Xiao-Peng], Zhang, J.P.[Jung-Ping], Wang, Q.S.[Qing-Sheng],
Salient region growing based on Gaussian pyramid,
IET-IPR(15), No. 13, 2021, pp. 3142-3152.
DOI Link 2110
BibRef

Baleghi, Y.[Yasser], Rousseau, D.[David],
An analytical proof on suitability of Cauchy-Schwarz Divergence as the aggregation criterion in Region Growing Algorithm,
IVC(115), 2021, pp. 104312.
Elsevier DOI 2110
Region Growing Algorithm, Image segmentation, Cauchy-Schwarz divergence, Aggregation criterion BibRef

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

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

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