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.[Steven L.], Pavlidis, T.[Theodosios],
A Graph-Theoretic Approach to Picture Processing,
CGIP(7), No. 2, April 1978, pp. 282-291.
Elsevier DOI BibRef 7804
Earlier:
Picture Processing by Graph Analysis,
CGPR75(125-129). Efficient data structures. 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.[Peter],
An Approach to Region Splitting,
CGIP(10), No. 3, July 1979, pp. 281-288.
Elsevier DOI BibRef 7907

Browning, J.D.[Jason D.], Tanimoto, S.L.[Steven L.],
Segmentation of Pictures into Regions with a Tile-by-Tile Method,
PR(15), No. 1, 1982, pp. 1-10.
Elsevier DOI 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 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 DOI BibRef 9003
Earlier: CVPR88(208-214).
IEEE DOI 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.
Elsevier DOI BibRef 8500

Cheevasuvit, F.[Fusak], Maitre, H.[Henri], Vidal-Madjar, D.[Daniel],
A Robust Method for Picture Segmentation Based on a Split-and-Merge Procedure,
CVGIP(34), No. 3, June 1986, pp. 268-281.
Elsevier DOI 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.[Robert H.],
Split-and-Merge Segmentation of Aerial Photographs,
CVGIP(44), No. 1, October 1988, pp. 77-86.
Elsevier DOI 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.
Elsevier DOI BibRef 8602
And:
Image Surface Approximation with Irregular Samples,
PAMI(11), No. 2, February 1989, pp. 206-212.
IEEE 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 Link. 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 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.
Elsevier DOI Segmentation, Facet Model. Seems to combine split and merge techniques with the facet model for analysis. BibRef 8511

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 DOI BibRef 9311

Wu, Z., Leahy, R.,
Image segmentation via edge contour finding: a graph theoretic approach,
CVPR92(613-619).
IEEE DOI 0403
BibRef

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 DOI Markov Random Field. Abstract:
HTML Version. BibRef 9510
And:
Erratta for Rotated Figures,
PAMI(17), No. 11, November 1995, pp. 1128-1128.
IEEE Top Reference. BibRef
Earlier:
Results Using Random Field Models for the Segmentation of Color Images,
ICCV95(714-719).
IEEE 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,
CVPR93(776-777).
IEEE DOI BibRef 9300

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

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 Link. 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
BibRef

Li, C.T.[Chang-Tsun], Chiao, R.[Randy],
Multiresolution genetic clustering algorithm for texture segmentation,
IVC(21), No. 11, October 2003, pp. 955-966.
Elsevier DOI 0310
BibRef
And:
Unsupervised texture segmentation using multiresolution hybrid genetic algorithm,
ICIP03(II: 1033-1036).
IEEE DOI 0312
BibRef

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 DOI 0009
BibRef

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.
Elsevier DOI 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 DOI 0508
BibRef

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 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 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 DOI 0602
BibRef
Earlier:
Faster graph-theoretic image processing via small-world and quadtree topologies,
CVPR04(II: 360-365).
IEEE 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 DOI 1001
BibRef
Earlier:
Computing Exact Discrete Minimal Surfaces: Extending and Solving the Shortest Path Problem in 3D with Application to Segmentation,
CVPR06(I: 69-78).
IEEE DOI 0606
Segmentation, Range. BibRef

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

El-Zehiry, N.Y.[Noha Youssry], Grady, L.[Leo],
Contrast Driven Elastica for Image Segmentation,
IP(25), No. 6, June 2016, pp. 2508-2518.
IEEE DOI 1605
BibRef
Earlier:
Fast global optimization of curvature,
CVPR10(3257-3264).
IEEE DOI 1006
combinatorial mathematics. 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.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI Evolutionary metaheuristics; Watershed; Region merging; Graph-based segmentation; Hierarchical social algorithm 0606
BibRef

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 DOI 0704
BibRef
Earlier:
Countering Oversegmentation in Partitioning-Based Connectivities,
ICIP05(III: 844-847).
IEEE DOI 0512
BibRef

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 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,
ISMM09(70-81).
Springer DOI 0908
BibRef

Moschini, U.[Ugo], Meijster, A.[Arnold], Wilkinson, M.H.F.[Michael H. F.],
A Hybrid Shared-Memory Parallel Max-Tree Algorithm for Extreme Dynamic-Range Images,
PAMI(40), No. 3, March 2018, pp. 513-526.
IEEE DOI 1802
Algorithm design and analysis, Buildings, Data structures, Dynamic range, Heuristic algorithms, Merging, Parallel algorithms, parallel algorithms BibRef

Wilkinson, M.H.F.[Michael H.F.],
A fast component-tree algorithm for high dynamic-range images and second generation connectivity,
ICIP11(1021-1024).
IEEE DOI 1201
BibRef

Ouzounis, G.K.[Georgios K.],
An Efficient Algorithm for Computing Multi-scale Connectivity Measures,
ISMM09(307-319).
Springer DOI 0908
BibRef

Wilkinson, M.H.F.[Michael H.F.],
An Axiomatic Approach to Hyperconnectivity,
ISMM09(35-46).
Springer DOI 0908
BibRef

Wilkinson, M.H.F.[Michael H.F.],
Hyperconnectivity, Attribute-Space Connectivity and Path Openings: Theoretical Relationships,
ISMM09(47-58).
Springer DOI 0908
BibRef

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.
Elsevier DOI 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 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 DOI 0608
BibRef

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 DOI 0901
BibRef
Earlier: ECCV06(IV: 581-594).
Springer DOI 0608
BibRef

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 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

Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.,
Attention to Scale: Scale-Aware Semantic Image Segmentation,
CVPR16(3640-3649)
IEEE DOI 1612
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 DOI 1302
BibRef

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 DOI 1302
BibRef

Cao, X.R.[Xiao-Ran], Lai, C.C.[Chang-Cai], Wang, Y.F.[Yun-Fei], Liu, L.Z.[Ling-Zhi], Zheng, J.H.[Jian-Hua], He, Y.[Yun],
Short Distance Intra Coding Scheme for High Efficiency Video Coding,
IP(22), No. 2, February 2013, pp. 790-801.
IEEE DOI 1302
BibRef

Tech, G., Chen, Y., Muller, K., Ohm, J., Vetro, A., Wang, Y.,
Overview of the Multiview and 3D Extensions of High Efficiency Video Coding,
CirSysVideo(26), No. 1, January 2016, pp. 35-49.
IEEE DOI 1601
Decoding BibRef

Fu, G.[Gang], Zhao, H.R.[Hong-Rui], 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
BibRef

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 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 DOI 1310
BibRef
Earlier: A2, A1:
Optima on Hierarchies of Partitions,
ISMM13(147-158).
Springer DOI 1305
BibRef
And: A1, A2:
Scale Space Operators on Hierarchies of Segmentations,
SSVM13(331-342).
Springer DOI 1305
BibRef
And: A1, A2:
Ground Truth Energies for Hierarchies of Segmentations,
ISMM13(123-134).
Springer DOI 1305
Hierarchical segmentation BibRef

Kiran, B.R.[Bangalore Ravi], Serra, J.[Jean],
Braids of Partitions,
ISMM15(217-228).
Springer DOI 1506
BibRef

Serra, J.[Jean], Kiran, B.R.[Bangalore Ravi], Cousty, J.[Jean],
Hierarchies and Climbing Energies,
CIARP12(821-828).
Springer DOI 1209
BibRef
And: A2, A1, A3:
Climbing: A Unified Approach for Global Constraints on Hierarchical Segmentation,
Global12(III: 324-334).
Springer DOI 1210
BibRef

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 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 DOI 1407
Accuracy BibRef

Zhang, X.L.[Xue-Liang], Xiao, P.F.[Peng-Feng], Feng, X.Z.[Xue-Zhi], Wang, J.G.[Jian-Geng], Wang, Z.[Zuo],
Hybrid region merging method for segmentation of high-resolution remote sensing images,
PandRS(98), No. 1, 2014, pp. 19-28.
Elsevier DOI 1411
High-resolution remote sensing BibRef

Zhang, X.L.[Xue-Liang], Xiao, P.F.[Peng-Feng], Feng, X.Z.[Xue-Zhi], He, G.J.[Guang-Jun],
Another look on region merging procedure from seed region shift for high-resolution remote sensing image segmentation,
PandRS(148), 2019, pp. 197-207.
Elsevier DOI 1901
High-resolution remote sensing, Image segmentation, Region merging, Seed region, Geographic object-based image analysis BibRef

Zhang, X.L.[Xue-Liang], Feng, X.Z.[Xue-Zhi], Xiao, P.F.[Peng-Feng], He, G.J.[Guang-Jun], Zhu, L.J.[Liu-Jun],
Segmentation quality evaluation using region-based precision and recall measures for remote sensing images,
PandRS(102), No. 1, 2015, pp. 73-84.
Elsevier DOI 1503
High-spatial resolution remote sensing BibRef

Dai, L.Z.[Ling-Zheng], Yang, J.[Jian], Chen, L.[Liang], Li, J.X.[Jun-Xia],
Category-specific object segmentation via unsupervised discriminant shape,
PR(64), No. 1, 2017, pp. 202-214.
Elsevier DOI 1701
Object segmentation BibRef

Dai, L.Z.[Ling-Zheng], Li, J.X.[Jun-Xia], Ding, J.D.[Jun-Di], Yang, J.[Jian],
CCTA-based region-wise segmentation,
ICPR12(238-241).
WWW Link. 1302
Connected Coherence Tree Algorithm. Oversegment, then merge. BibRef

Shi, J.[Jiao], Lei, Y.[Yu], Wu, J.J.[Jia-Ji], Paul, A.[Anand], Kim, M.[Mucheol], Jeon, G.G.[Gwang-Gil],
Uncertain clustering algorithms based on rough and fuzzy sets for real-time image segmentation,
RealTimeIP(13), No. 3, September 2017, pp. 645-663.
Springer DOI 1710
BibRef

Xing, J., Hu, W., Ai, H., Yan, S.,
FatRegion: A Fast Adaptive Tree-Structured Region Extraction Approach,
CirSysVideo(28), No. 3, March 2018, pp. 601-615.
IEEE DOI 1804
Feature extraction, Image edge detection, Image segmentation, Merging, Object tracking, Pipelines, Time complexity, superpixel BibRef

Roy, P.[Pradipta], Biswas, P.K.[Prabir Kumar],
A parallel LEGION algorithm and cell-based architecture for real time split and merge video segmentation,
RealTimeIP(15), No. 2, August 2018, pp. 363-387.
WWW Link. 1808
BibRef

Zhang, Y.H.[Yu-Han], Wang, X.[Xi], Tan, H.S.[Hai-Shu], Xu, C.[Chang], Ma, X.[Xu], Xu, T.F.[Ting-Fa],
Region Merging Method for Remote Sensing Spectral Image Aided by Inter-Segment and Boundary Homogeneities,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Dao, P.D.[Phuong D.], Mantripragada, K.[Kiran], He, Y.H.[Yu-Hong], Qureshi, F.Z.[Faisal Z.],
Improving hyperspectral image segmentation by applying inverse noise weighting and outlier removal for optimal scale selection,
PandRS(171), 2021, pp. 348-366.
Elsevier DOI 2012
Inverse noise weighting, Outlier detection, Optimal scale selection, Image segmentation, Hyperspectral image classification BibRef

Wang, X.F.[Xiao-Feng], Wang, Y.[Yan], Lei, J.J.[Jin-Jin], Li, B.[Bin], Wang, Q.[Qin], Xue, J.R.[Jian-Ru],
Coarse-to-fine-grained method for image splicing region detection,
PR(122), 2022, pp. 108347.
Elsevier DOI 2112
Image splicing detection, CFA interpolation algorithm, Forensics features, Texture strength features, Edges smoothing BibRef

ur Rehman, A.[Atiq], Belhaouari, S.B.[Samir Brahim],
Divide well to merge better: A novel clustering algorithm,
PR(122), 2022, pp. 108305.
Elsevier DOI 2112
Clustering, Data projection, Joint probability density estimation, Non-parametric techniques BibRef

Zheng, Y.P.[Yun-Ping], Yang, B.[Bowen], Sarem, M.[Mudar],
FNRegion: A fast NAM-based region extraction algorithm,
IET-IPR(17), No. 14, 2023, pp. 4076-4088.
DOI Link 2312
image processing, image representation
See also FatRegion: A Fast Adaptive Tree-Structured Region Extraction Approach. BibRef

Qiang, Z.[Zhe], Ma, J.W.[Jin-Wen], Wu, D.[Di],
Split-and-merge model selection of mixtures of Gaussian processes with RJMCMC,
PR(157), 2025, pp. 110913.
Elsevier DOI 2409
Mixture of Gaussian processes, Model selection, Curve clustering, Penalized prior RJMCMC, Full Bayesian inference BibRef

Park, C.[Chanhyeok], Sung, M.[Minhyuk],
Split, Merge, and Refine: Fitting Tight Bounding Boxes via Over-Segmentation and Iterative Search,
3DV24(1468-1477)
IEEE DOI 2408
Sensitivity, Shape, Search methods, Merging, Semantics, Training data, Bounding Boxes, Iterative Search, MCTS, 3D Modeling, 3D Segmentation BibRef

Murshed, M.[Manzur], Teng, S.W.[Shyh Wei], Lu, G.J.[Guo-Jun],
Cuboid Segmentation for Effective Image Retrieval,
DICTA17(1-8)
IEEE DOI 1804
Rectangle partitions. content-based retrieval, image colour analysis, image retrieval, image segmentation, indexing, police data processing, Indexing BibRef