8.5 Segmentation by Split and Merge Techniques, Hierarchical

Chapter Contents (Back)
Split and Merge. Segmentation, Split and Merge. Segmentation, Region Merging. Segmentation, Region Splitting. Quadtree.

Horowitz, S.L., and Pavlidis, T.,
Picture Segmentation by a Tree Traversal Algorithm,
JACM(23), No. 2, April 1976, pp. 368-388. Segmentation, Split and Merge. The standard split and merge basic reference. The basics are, split the image into regular shapes (quarters), if it is non-uniform recursively split the subimages. When no more splits, merge adjacent similar subimages, and merge those remaining that are too small. BibRef 7604

Horowitz, S.L., and Pavlidis, T.,
Picture Segmentation by a Directed Split and Merge Procedure,
ICPR74(424-433). BibRef 7400 CMetImAly77(101-11). Good early reference to the method. BibRef

Horowitz, S.L., and Pavlidis, T.,
A Graph-Theoretic Approach to Picture Processing,
CGIP(7), No. 2, April 1978, pp. 282-291.
WWW Version. BibRef 7804
Picture Processing by Graph Analysis,
CGPR75(125-129). BibRef

Tanimoto, S.L., and Pavlidis, T.,
The Editing of Picture Segmentations Using Local Analysis of Graphs,
CACM(20), No. 4, April 1977, pp. 223-229. BibRef 7704

Pavlidis, T., Tanimoto, S.L.,
Texture Identification by a Directed Split-and Merge Procedure,
CGPR75(201-203). BibRef 7500

Gupta, J.N., and Wintz, P.A.,
A Boundary Finding Algorithm and Its Applications,
CirSys(22), No. 4, April 1975, pp. 351-362. Segmentation, Texture. Merging on texture, use one or both of first or second order statistics. BibRef 7504

Lemkin, P.,
An Approach to Region Splitting,
CGIP(10), No. 3, July 1979, pp. 281-288.
WWW Version. BibRef 7907

Browning, J.D., and Tanimoto, S.L.,
Segmentation of Pictures into Regions with a Tile-by-Tile Method,
PR(15), No. 1, 1982, pp. 1-10.
WWW Version. Extension of split and merge to handle large images by looking at a small portion at one time. Grouping must allow for boundary conditions. BibRef 8200

Browning, J.D.,
A Method for Picture Segmentation by Parts: Split and Group with Linking,
PRAI-78(191). BibRef 7800

Chen, M.H., and Pavlidis, T.,
Image Seaming for Segmentation on Parallel Architecture,
PAMI(12), No. 6, June 1990, pp. 588-594.
IEEE via DOI Same problems as the above paper, but with architecture issues. BibRef 9006

Pavlidis, T., and Liow, Y.T.,
Integrating Region Growing and Edge Detection,
PAMI(12), No. 3, March 1990, pp. 225-233.
IEEE via DOI BibRef 9003
Earlier: CVPR88(208-214).
IEEE Abstract. Segmentation, Edges. Use edges in the merging portion of a split and merge segmentation algorithm. BibRef

Autonisse, H.J.,
Image Segmentation in Pyramids,
CGIP(19), No. 4, 1982, pp. 367-383. BibRef 8200

Spann, M., Wilson, R.,
A Quad-Tree Approach to Image Segmentation Which Combines Statistical and Spatial Information,
PR(18), No. 3-4, 1985, pp. 257-269.
WWW Version. BibRef 8500

Cheevasuvit, F., Maitre, H., and Vidal-Madjar, D.,
A Robust Method for Picture Segmentation Based on a Split-and-Merge Procedure,
CVGIP(34), No. 3, June 1986, pp. 268-281.
WWW Version. The aim is to get the consistent regions, the method is to segment all members of the sequence, reduce regions to an ellipse, and keep those regions whose ellipses are consistent. BibRef 8606

Laprade, R.H.,
Split-and-Merge Segmentation of Aerial Photographs,
CVGIP(44), No. 1, October 1988, pp. 77-86.
WWW Version. Lockheed work on segmentation. This uses a facet type representation of the resulting regions and the parameters are also used in the merge phase. BibRef 8810

Doherty, M.F., Bjorklund, C.M., and Noga, M.T.,
Split-Merge-merge: An Enhanced Segmentation Capability,
CVPR86(325-330). Add another merge step with more hueristics to eliminate the standard small region problems. BibRef 8600

Lee, C.H.[Chin-Hwa],
Recursive Region Splitting at Hierarchial Scope Views,
CVGIP(33), No. 2, February 1986, pp. 237-258.
WWW Version. BibRef 8602
Image Surface Approximation with Irregular Samples,
PAMI(11), No. 2, February 1989, pp. 206-212.
IEEE via DOI Segmentation, Multi-Level. This method combines the regular splitting, and the quad-tree data structure of the split and merge techniques with the general threshold based region extraction method of the recursive splitting techniques. The main problem being addressed is how to merge the regions generated in one branch of the quad-tree with those in spatially adjacent branches of the tree. This requires an analysis of regions that touch the boundaries of the quad-tree nodes to determine how they should extend or connect to regions in the other nodes. BibRef

Imao, K.[Kaoru], Watanabe, H.[Hideyuki],
Method of describing image information,
US_Patent4,944,023, Jul 24, 1990
WWW Version. BibRef 9007

Wu, X.,
Adaptive Split-and-Merge Segmentation Based on Piecewise Least-Square Approximation,
PAMI(15), No. 8, August 1993, pp. 808-815.
IEEE via DOI BibRef 9308

Doherty, M.F., Noga, M.T., and Bjorklund, C.M.,
Use of Compound Predicates in Split-and-Merge Segmentation,
Lockheed Palo Alto Research Labs, CVPR85(659-661). Add constant texture measures to the standard splitting criteria. BibRef 8500

Cohen, Jr., E.A.[Edgar A.],
Generalized Sloped Facet Models Useful in Multispectral Image Analysis,
CVGIP(32), No. 2, November 1985, pp. 171-190.
WWW Version. Segmentation, Facet Model. Seems to combine split and merge techniques with the facet model for analysis. BibRef 8511

Jarvis, R.A.,
Computer Image Segmentation: First Partitions Using Shared Near Neighbor Clustering,
TC(20), No. 9, September 1971, pp. 1025-1034. BibRef 7109
And: Purdue-TR-77-43, November 1977. BibRef
Computer Image Segmentation: Structured Merge Strategies,
Purdue-TR-77-44, November 1977. BibRef
And: (Similar title) Purdue-TR-75-45. Color. Bottom-up - image fragment conglomeration. Uses a variety of features and criteria to decide the merging of adjacent regions. Border count is one of them. Hard to predict the results analytically. Hypothesis concerning "low level" visual cohesion in intensity and color - excluding texture. (I.e., the region growing initialization step). (higher levels in TREE-75-44 and submitted for publication); neighborhood size, threshold of similarity rating; region grower initialization still same problem of using 8x8 elements as smallest element (no times given). (TC(20) is 1971, or is it TC(22)?) BibRef

Jarvis, R.A.,
Image Segmentation by Interactively Combining Line, Region, and Semantic Structure,
CGPR75(279-288). BibRef 7500

Jarvis, R.A.,
Shared Near Neighbor Maximal Spanning Trees for Cluster Analysis,
unknown location (UMd report?) Basic method: iteratively add the minimum link which adds a node to the tree. Add the least weight except when it causes a loop. Clustering: no unique optimal solution, any method gives different results on various non-linear transformations of measurement space. 7700

Khan, G.N., Gillies, D.F.,
Parallel-Hierarchical Image Partitioning and Region Extraction,
CVIP92(123-140). BibRef 9200

Wu, Z., and Leahy, R.,
An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation,
PAMI(15), No. 11, November 1993, pp. 1101-1113.
IEEE via DOI BibRef 9311

Wu, Z., Leahy, R.,
Image segmentation via edge contour finding: a graph theoretic approach,
IEEE via DOI 0403

Panjwani, D.K.[Dileep K.], Healey, G.,
Markov Random-Field Models for Unsupervised Segmentation of Textured Color Images,
PAMI(17), No. 10, October 1995, pp. 939-954.
IEEE via DOI Markov Random Field. Abstract:
HTML Version. BibRef 9510
Erratta for Rotated Figures,
PAMI(17), No. 11, November 1995, pp. 1128-1128.
IEEE Top Reference. BibRef
Results Using Random Field Models for the Segmentation of Color Images,
IEEE via DOI Segmentation, MRF. Color. Segmentation using split and merge type of algorithm. See also Analytical and Experimental Study of the Performance of Markov Random-Fields Applied to Textured Images Using Small Samples, An. BibRef

Panjwani, D.K., and Healey, G.,
Unsupervised Segmentation of Textured Color Images Using Markov Random Field Models,
IEEE via DOI BibRef 9300

Panjwani, D.K., and Healey, G.,
Selecting Neighbors in Random Field Models for Color Images,
ICIP94(II: 56-60).
IEEE via DOI 9411
HTML Version. BibRef

Fiorio, C., and Gustedt, J.,
Two Linear Time Union-Find Strategies for Image Processing,
TCS(A: 154), No. 2, 1996, pp. 165-181. ON2 algorithm. BibRef 9600

Xu, Y., Uberbacher, E.C.,
2D Image Segmentation Using Minimum Spanning-Trees,
IVC(15), No. 1, January 1997, pp. 47-57.
WWW Version. 9702

de Queiroz, R.L.[Ricardo L.], Bozdagi, G.[Gozde],
Using encoding cost data for segmentation of compressed image sequences,
US_Patent6,058,210, May 2, 2000
WWW Version. Processing compressed data. BibRef 0005

Li, C.T.,
Multiresolution image segmentation integrating Gibbs sampler and region merging algorithm,
SP(83), No. 1, January 2003, pp. 67-78.
HTML Version. 0211

Li, C.T.[Chang-Tsun], Chiao, R.[Randy],
Multiresolution genetic clustering algorithm for texture segmentation,
IVC(21), No. 11, October 2003, pp. 955-966.
WWW Version. 0310

Li, C.T.[Chang-Tsun], Chiao, R.,
Unsupervised texture segmentation using multiresolution hybrid genetic algorithm,
ICIP03(II: 1033-1036).
IEEE via DOI 0312

Storkey, A.J.[Amos J.], Williams, C.K.I.[Christopher K.I.],
Image modeling with position-encoding dynamic trees,
PAMI(25), No. 7, July 2003, pp. 859-871.
IEEE Abstract. 0307
Tree descriptions for segmentation. BibRef

Adams, N.J., Storkey, A.J., Ghahramani, Z., Williams, C.K.I.,
MFDTs: Mean Field Dynamic Trees,
ICPR00(Vol III: 147-150).
IEEE via DOI 0009

Chung, K.L.[Kuo-Liang], Huang, H.L.[Hsu-Lien], Lu, H.I.[Hsueh-I],
Efficient region segmentation on compressed gray images using quadtree and shading representation,
PR(37), No. 8, August 2004, pp. 1591-1605.
WWW Version. 0407
Segment the compressed image, in split-merge tree framework. Compares to See also Two Linear Time Union-Find Strategies for Image Processing. BibRef

Chung, R.H.Y.[Ronald H.Y.], Yung, N.H.C.[Nelson H.C.], Cheung, P.Y.S.[Paul Y.S.],
An Efficient Parameterless Quadrilateral-Based Image Segmentation Method,
PAMI(27), No. 9, September 2005, pp. 1446-1458.
IEEE via DOI 0508

Zhu, S.S.[Shan-Shan], Yung, N.H.C.[Nelson H.C.],
Sub-scene segmentation using constraints based on Gestalt principles,
JVCIR(25), No. 5, 2014, pp. 994-1005.
Elsevier via DOI 1406
Unsupervised image segmentation BibRef

Zhu, S.S.[Shan-Shan], Yung, N.H.C.[Nelson H. C.],
Improve scene categorization via sub-scene recognition,
MVA(25), No. 6, 2014, pp. 1561-1572.
Springer via DOI 1408
Use spatial information, similar objects with different arrangements. BibRef

Grady, L.[Leo], Schwartz, E.L.[Eric L.],
Isoperimetric Graph Partitioning for Image Segmentation,
PAMI(28), No. 3, March 2006, pp. 469-475.
IEEE via DOI 0602
Faster graph-theoretic image processing via small-world and quadtree topologies,
CVPR04(II: 360-365).
IEEE via DOI 0408
Segmentation approach. See also Random Walks for Image Segmentation. BibRef

Grady, L.[Leo],
Minimal Surfaces Extend Shortest Path Segmentation Methods to 3D,
PAMI(32), No. 2, February 2010, pp. 321-334.
IEEE via DOI 1001
Computing Exact Discrete Minimal Surfaces: Extending and Solving the Shortest Path Problem in 3D with Application to Segmentation,
CVPR06(I: 69-78).
IEEE via DOI 0606
Segmentation, Range. BibRef

Grady, L.[Leo],
Fast, Quality, Segmentation of Large Volumes: Isoperimetric Distance Trees,
ECCV06(III: 449-462).
Springer via DOI 0608

El-Zehiry, N.Y.[Noha Youssry], Grady, L.[Leo],
Fast global optimization of curvature,
IEEE via DOI 1006
Regularization that minimized curvature. BibRef

Kumar, S., Ong, S.H., Ranganath, S., Ong, T.C., Chew, F.T.,
A rule-based approach for robust clump splitting,
PR(39), No. 6, June 2006, pp. 1088-1098.
WWW Version. 0604
Concavity analysis; Overlapping objects; Clump splitting BibRef

Pichel, J.C.[Juan C.], Singh, D.E.[David E.], Rivera, F.F.[Francisco F.],
Image segmentation based on merging of sub-optimal segmentations,
PRL(27), No. 10, 15 July 2006, pp. 1105-1116.
WWW Version. 0606
Region-merging heuristic; Segmentation evaluation BibRef

Duarte, A.[Abraham], Sánchez, Á.[Ángel], Fernández, F.[Felipe], Montemayor, A.S.[Antonio S.],
Improving image segmentation quality through effective region merging using a hierarchical social metaheuristic,
PRL(27), No. 11, August 2006, pp. 1239-1251.
WWW Version. Evolutionary metaheuristics; Watershed; Region merging; Graph-based segmentation; Hierarchical social algorithm 0606

Ouzounis, G.K.[Georgios K.], Wilkinson, M.H.F.[Michael H.F.],
Mask-Based Second-Generation Connectivity and Attribute Filters,
PAMI(29), No. 6, June 2007, pp. 990-1004.
IEEE via DOI 0704
Countering Oversegmentation in Partitioning-Based Connectivities,
ICIP05(III: 844-847).
IEEE via DOI 0512

Ouzounis, G.K.[Georgios K.], Wilkinson, M.H.F.[Michael H. F.],
Hyperconnected Attribute Filters Based on k-Flat Zones,
PAMI(33), No. 2, February 2011, pp. 224-239.
IEEE via DOI 1101
Attribute filtering. Supress background detail, keep internal details. BibRef

Kiwanuka, F.N.[Fred N.], Ouzounis, G.K.[Georgios K.], Wilkinson, M.H.F.[Michael H. F.],
Surface-Area-Based Attribute Filtering in 3D,
Springer via DOI 0908

Wilkinson, M.H.F.[Michael H.F.],
A fast component-tree algorithm for high dynamic-range images and second generation connectivity,
IEEE via DOI 1201

Ouzounis, G.K.[Georgios K.],
An Efficient Algorithm for Computing Multi-scale Connectivity Measures,
Springer via DOI 0908

Wilkinson, M.H.F.[Michael H.F.],
An Axiomatic Approach to Hyperconnectivity,
Springer via DOI 0908

Wilkinson, M.H.F.[Michael H.F.],
Hyperconnectivity, Attribute-Space Connectivity and Path Openings: Theoretical Relationships,
Springer via DOI 0908

Wu, Y.T.[Yi-Ta], Shih, F.Y.[Frank Y.], Shi, J.Z.[Jia-Zheng], Wu, Y.T.[Yih-Tyng],
A top-down region dividing approach for image segmentation,
PR(41), No. 6, June 2008, pp. 1948-1960.
WWW Version. 0802
Image segmentation; Feature-based segmentation; Spatial-based segmentation; Watershed; Medical image analysis BibRef

Stewart, L.[Liam], He, X.M.[Xu-Ming], Zemel, R.S.[Richard S.],
Learning Flexible Features for Conditional Random Fields,
PAMI(30), No. 8, August 2008, pp. 1415-1426.
IEEE via DOI 0806
hierarchical models. BibRef

He, X.M.[Xu-Ming], Zemel, R.S.[Richard S.], Ray, D.[Debajyoti],
Learning and Incorporating Top-Down Cues in Image Segmentation,
ECCV06(I: 338-351).
Springer via DOI 0608

Levin, A.[Anat], Weiss, Y.[Yair],
Learning to Combine Bottom-Up and Top-Down Segmentation,
IJCV(81), No. 1, January 2009, pp. xx-yy.
Springer via DOI 0901
Earlier: ECCV06(IV: 581-594).
Springer via DOI 0608

Zhu, L.L.[Long Leo], Chen, Y.H.[Yuan-Hao], Lin, Y.[Yuan], Lin, C.X.[Chen-Xi], Yuille, A.L.[Alan L.],
Recursive Segmentation and Recognition Templates for Image Parsing,
PAMI(34), No. 2, February 2012, pp. 359-371.
IEEE via DOI 1112
Hirarchical image model. Segment and recognize at multiple levels. See also Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing. See also Max Margin Learning of Hierarchical Configural Deformable Templates (HCDTs) for Efficient Object Parsing and Pose Estimation. See also Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation, and Recognition Using Knowledge Propagation. BibRef

Helle, P., Oudin, S., Bross, B., Marpe, D., Bici, M.O., Ugur, K., Jung, J., Clare, G., Wiegand, T.,
Block Merging for Quadtree-Based Partitioning in HEVC,
CirSysVideo(22), No. 12, December 2012, pp. 1720-1731.
IEEE via DOI 1302

Yuan, Y., Kim, I.K., Zheng, X., Liu, L., Cao, X., Lee, S., Cheon, M.S., Lee, T., He, Y., Park, J.H.,
Quadtree Based Nonsquare Block Structure for Inter Frame Coding in High Efficiency Video Coding,
CirSysVideo(22), No. 12, December 2012, pp. 1707-1719.
IEEE via DOI 1302

Cao, X., Lai, C., Wang, Y., Liu, L., Zheng, J., He, Y.,
Short Distance Intra Coding Scheme for High Efficiency Video Coding,
IP(22), No. 2, February 2013, pp. 790-801.
IEEE via DOI 1302

Fu, G.[Gang], Zhao, H.[Hongrui], Li, C.[Cong], Shi, L.[Limei],
Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique,
RS(5), No. 7, 2013, pp. 3259-3279.
DOI Link 1308

Nadernejad, E.[Ehsan], Sharifzadeh, S.[Sara],
A new method for image segmentation based on Fuzzy C-means algorithm on pixonal images formed by bilateral filtering,
SIViP(7), No. 5, September 2013, pp. 855-863.
Springer via DOI 1309
Fuzzy C-mean. eliminates the unnecessary details of the image. BibRef

Kiran, B.R.[Bangalore Ravi], Serra, J.[Jean],
Global-local optimizations by hierarchical cuts and climbing energies,
PR(47), No. 1, 2014, pp. 12-24.
Elsevier via DOI 1310
Earlier: A2, A1:
Optima on Hierarchies of Partitions,
Springer via DOI 1305
And: A1, A2:
Scale Space Operators on Hierarchies of Segmentations,
Springer via DOI 1305
And: A1, A2:
Ground Truth Energies for Hierarchies of Segmentations,
Springer via DOI 1305
Hierarchical segmentation BibRef

Serra, J.[Jean], Kiran, B.R.[Bangalore Ravi], Cousty, J.[Jean],
Hierarchies and Climbing Energies,
Springer via DOI 1209
And: A2, A1, A3:
Climbing: A Unified Approach for Global Constraints on Hierarchical Segmentation,
Global12(III: 324-334).
Springer via DOI 1210

Kiran, B.R.[Bangalore Ravi], Serra, J.[Jean],
Fusion of ground truths and hierarchies of segmentations,
PRL(47), No. 1, 2014, pp. 63-71.
Elsevier via DOI 1408
Hierarchical segmentation BibRef

Wang, M., Li, R.,
Segmentation of High Spatial Resolution Remote Sensing Imagery Based on Hard-Boundary Constraint and Two-Stage Merging,
GeoRS(52), No. 9, September 2014, pp. 5712-2725.
IEEE via DOI 1407
Accuracy BibRef

Zhang, X.L.[Xue-Liang], Xiao, P.[Pengfeng], Feng, X.Z.[Xue-Zhi], Wang, J.[Jiangeng], Wang, Z.[Zuo],
Hybrid region merging method for segmentation of high-resolution remote sensing images,
PandRS(98), No. 1, 2014, pp. 19-28.
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High-resolution remote sensing BibRef

Fu, K.[Keren], Gong, C.[Chen], Yun, Y.[Yixiao], Li, Y.J.[Yi-Jun], Gu, I.Y.H.[Irene Yu-Hua], Yang, J.[Jie], Yu, J.Y.[Jing-Yi],
Adaptive Multi-Level Region Merging for Salient Object Detection,
HTML Version. 1410

Štarha, P.[Pavel], Druckmüllerová, H.[Hana],
Decomposition of a Bunch of Objects in Digital Images,
Springer via DOI 1405
Decomposing overlapping objects. BibRef

Mirghasemi, S., Rayudu, R., Zhang, M.J.[Meng-Jie],
A feature-based region growing-merging approach to color image segmentation,
IEEE via DOI 1402
image colour analysis BibRef

Dragon, R.[Ralf], Ostermann, J.[Jörn], Van Gool, L.J.[Luc J.],
Robust Realtime Motion-Split-And-Merge for Motion Segmentation,
Springer via DOI 1311

Slimene, A.[Alya], Zagrouba, E.[Ezzeddine],
Kernel Maximum Mean Discrepancy for Region Merging Approach,
Springer via DOI 1311

Dai, L.Z.[Ling-Zheng], Li, J.X.[Jun-Xia], Ding, J.[Jundi], Yang, J.[Jian],
CCTA-based region-wise segmentation,
WWW Version. 1302
Connected Coherence Tree Algorithm. Oversegment, then merge. BibRef

Zankl, G.[Georg], Haxhimusa, Y.[Yll], Ion, A.[Adrian],
Interactive Labeling of Image Segmentation Hierarchies,
Springer via DOI 1209

Tong, B.[Biao], Li, J.M.[Jun-Ming],
Influence of shape parameters on optimal scale selection in multi-resolution segmentation,
IEEE via DOI 1112

Happ, P.N., Ferreira, R.S., Bentes, C., Costa, G.A.O.P., Feitosa, R.Q.,
Multiresolution Segmentation: A Parallel Approach for High Resolution Image Segmentation in Multicore Architectures,
PDF Link. 1007

de Carvalho, M.A.G.[Marco Antonio Garcia], Branco Ferreira, A.C.[Anselmo Castelo], Costa, A.L.[André Luis],
Image Segmentation Using Quadtree-Based Similarity Graph and Normalized Cut,
Springer via DOI 1011

Stojmenovic, M.[Milos], Solis-Montero, A.[Andres], Nayak, A.[Amiya],
Co-parent selection for fast region merging in pyramidal image segmentation,
IEEE via DOI 1007

Zhang, R.G.[Rong-Guo], Xiao, B.H.[Bai-Hua], Wang, C.H.[Chun-Heng],
Oversegment an image to get the candidate detection windows,
IEEE via DOI 0911

Atsumi, M.[Masayasu],
Attention-Based Segmentation on an Image Pyramid Sequence,
Springer via DOI 0810

Zeng, G.[Gang], Van Gool, L.J.[Luc J.],
Multi-label image segmentation via point-wise repetition,
IEEE via DOI 0806

Baldacci, F.[Fabien], Braquelaire, A.[Achille], Damiand, G.[Guillaume],
3D Topological Map Extraction from Oriented Boundary Graph,
Springer via DOI 0905

Baldacci, F.[Fabien], Braquelaire, A.[Achille], Domenger, J.P.[Jean-Philippe],
Oriented Boundary Graph: A Framework to Design and Implement 3D Segmentation Algorithms,
IEEE via DOI 1008
See also Unbiased and Intervoxel Watershed Algorithm for 3D Image Segmentation, An. BibRef

Baldacci, F.[Fabien], Braquelaire, A.[Achille], Desbarats, P.[Pascal], Domenger, J.P.[Jean-Philippe],
3D Image Topological Structuring with an Oriented Boundary Graph for Split and Merge Segmentation,
Springer via DOI 0804

Blanton, W.B.[W. Brendan], Barner, K.E.[Kenneth E.],
Texture-Based Infrared Image Segmentation by Combined Merging and Partitioning,
ICIP07(II: 45-48).
IEEE via DOI 0709

Tehami, S.[Samy], Bigand, A.[André], Colot, O.[Olivier],
Color Image Segmentation Based on Type-2 Fuzzy Sets and Region Merging,
Springer via DOI 0708

Micusik, B.[Banislav], Pajdla, T.[Tomas],
Multi-label image segmentation via max-sum solver,
IEEE via DOI 0706

Radhakrishnan, M.L., Su, S.L.,
Dead-End Elimination as a Heuristic for Min-Cut Image Segmentation,
IEEE via DOI 0610

Zhan, Y.W.[Yao-Wen], Wang, W.Q.A.[Wei-Qi-Ang], Gao, W.[Wen],
A Robust Split-and-Merge Text Segmentation Approach for Images,
ICPR06(II: 1002-1005).
IEEE via DOI 0609

Varma, M.[Manik], Ray, D.[Debajyoti],
Learning The Discriminative Power-Invariance Trade-Off,
IEEE via DOI 0710

Estrada, F.J., Jepson, A.D., Chennubhotla, C.,
Spectral Embedding and Min Cut for Image Segmentation,
HTML Version. 0508

Tolliver, D.A.[David A.], Collins, R.T.[Robert T.], Baker, S.[Simon],
Multilevel Spectral Partitioning for Efficient Image Segmentation and Tracking,
WACV05(I: 414-420).
IEEE via DOI 0502
Normalized cut image segmentation. BibRef

Merigot, A.,
Revisiting image splitting,
IEEE Abstract. 0310

Kaplan, L.M., Oh, S.M., Yoon, Y.S., McClellan, J.H.,
Target Detection Features for Pruned Quadtree Image Formation,
CVBVS01(xx-yy). 0110

Borges, G.A., Aldon, M.J.,
A Split-and-merge Segmentation Algorithm for Line Extraction in 2-d Range Images,
ICPR00(Vol I: 441-444).
IEEE via DOI 0009

Yang, H.S.[Hee Soo], Lee, S.U.[Sang Uk],
Split-and-merge segmentation employing thresholding technique,
ICIP97(I: 239-242).
IEEE via DOI 9710

Basman, A.[Antranig], Lasenby, J.[Joan], Cipolla, R.[Roberto],
Efficient region segmentation through 'creep-and-merge',
CIAP97(I: 223-230).
WWW Version. 9709

Gevers, T., Smeulders, A.W.M.,
Combining Region Splitting and Edge Detection Through Guided Delaunay Image Subdivision,
IEEE via DOI 9704

Kropatsch, W.G., Ben-Yacoub, S.,
A Revision of Pyramid Segmentation,
ICPR96(II: 477-481).
IEEE via DOI 9608
(Technical Univ. Vienna, A) BibRef

Kropatsch, W.G., Ben-Yacoub, S.,
A Universal Pyramid Segmentation Algorithm,
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Gevers, T., Kajcovski, V.K.,
Image Segmentation by Directed Region Subdivision,
IEEE via DOI BibRef 9400

Schutte, K.,
Region Growing with Planar Facets,
SCIA93(719-725). BibRef 9300

Kalvin, A., Peleg, S., Hummel, R.,
Pyramid Segmentation in 2D and 3D Images Using Local Optimization,
ICPR88(I: 276-278).
IEEE via DOI 8811

Parvin, B.A.,
A Split and Merge Algorithm for Segmentation of Natural Scenes,
ICPR84(294-296). BibRef 8400

Gerbrands, J.J., Backer, E.,
Split-and-Merge Segmentation of SLAR Imagery: Segmentation Consistency,
ICPR84(284-286). BibRef 8400

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Multi-level, Multi-Scale Segmentation and Smoothing Methods .

Last update:Dec 1, 2014 at 14:00:53