8.5.1 Multi-level, Multi-Scale Segmentation and Smoothing Methods

Chapter Contents (Back)
Smoothing. Multi-level Segmentation. Hierarchical Segmentation. Multi-Scale. Segmentation, Multi-Level. Pyramid Structure.
See also Smoothing Techniques, Adaptive Smoothing.

Ranade, S., and Shneier, M.O.,
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SMC(11), No. 5, May 1981, pp. 373-376. BibRef 8105

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CVGIP( 39), No. 1, July 1987, pp. 102-115.
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Elsevier DOI Pyramid. Bimodal distributed image. BibRef 9312

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ICPR94(C:310-316).
IEEE DOI BibRef 9400

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SMC(12), No. 5, September/October 1982, pp. 611-622. This reports on more work on pixel linking (from BibRef 8209 UMD-TR 977, 989, 1025). The Eklundh and Rosenfeld paper is the primary reference. BibRef

Narayanan, K.A., Rosenfeld, A.,
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Silberberg, T.[Teresa], Peleg, S.[Shmuel], and Rosenfeld, A.,
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Eklundh, J.O., and Rosenfeld, A.,
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Eklundh, J.O., and Rosenfeld, A.,
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Pietikainen, M.[Matti], and Rosenfeld, A.[Azriel],
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SMC(11), No. 12, December 1981, pp. 822-825. This extends the pyramid linking method to using texture parameters for the linking decision.
See also Texture discrimination with multidimensional distributions of signed gray-level differences. BibRef 8112

Pietikainen, M.[Matti], and Rosenfeld, A.[Azriel],
Gray Level Pyramid Linking as an Aid in Texture Analysis,
SMC(12), 1982, pp. 422-429. BibRef 8200

Pietikainen, M.[Matti], Rosenfeld, A.[Azriel], and Davis, L.S.,
Experiments with Texture Classification Using Averages of Local Pattern Matches,
SMC(13), 1983, pp. 421-426. BibRef 8300

Pietikainen, M.[Matti],
Image Texture Analysis and Segmentation,
Acta Universiartis Ouluensis, Series C, Technica No. 21, Electornica No. 1. Univ. of OuluMarch 1982. Oulu, Finland. Evaluation, Texture. A comparative study and a look at Pyramid node linking from Pietikainen's texture work while at UMd. BibRef 8203

Burt, P.J.[Peter J.], Hong, T.H.[Tsai-Hong], and Rosenfeld, A.[Azriel],
Segmentation and Estimation of Image Region Properties Through Cooperative Hierarchical Computation,
SMC(11), No. 12, December 1981, pp. 802-809. This paper gives the primary definition of the linked pyramid method. Otherwise, little content. BibRef 8112

Pietikainen, M.[Matti], Rosenfeld, A.[Azriel], Walter, I.[Ingrid],
Split-and-Link Algorithms for Image Segmentation,
PR(15), No. 4, 1982, pp. 287-298.
Elsevier DOI An alternative to split and merge. The major difference is that the levels are overlapped so that each pixel can be the descendant of several (4) others. The linking is done to the one that looks most like yourself. This eliminates some of the problems in the initial splitting. BibRef 8200

Cibulskis, J.M., Dyer, C.R.,
An Analysis of Node Linking in Overlapped Pyramids,
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Baronti, S.[Stefano], Casini, A.[Andrea], Lotti, F.[Franco], Favaro, L., and Roberto, V.,
Variable Pyramid Structures for Image Segmentation,
CVGIP(49), No. 3, March 1990, pp. 346-356.
Elsevier DOI Various modifications to the earlier linked pyramid algorithms. BibRef 9003

Baronti, S.[Stefano], Casini, A.[Andrea], Lotti, F.[Franco], Porcinai, S.[Simone],
Segmentation of multispectral images of works of art through principal component analysis,
CIAP97(I: 14-21).
Springer DOI 9709
BibRef

Grosky, W.I., and Jain, R.C.,
A Pyramid-Based Approach to Segmentation Applied to Region Matching,
PAMI(8), No. 5, September 1986, pp. 639-650. BibRef 8609

Jain, R., Grosky, W.I.,
Hyper-Pyramids for Integration of Spatial Information,
SRMSF87(72-81). BibRef 8700

Antonisse, H.J.,
Image Segmentation in Pyramids,
CGIP(19), No. 4, August 1982, pp. 367-383.
Elsevier DOI Linked Pyramids: reduce with overlap, link to parent when average is closest, recompute the averages using only those linked, reset links, etc. until no change (at all levels). Limited to 4 classes (top of pyramid). Various "fixes" produce similar results (holes, etc. cause problems). Unforced linking - not forced to link to a parent (form new regions at higher resolutions). Link at low tolerance and cluster the small regions. BibRef 8208

Lifshitz, L.M., and Pizer, S.M.,
A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema,
PAMI(12), No. 6, June 1990, pp. 529-540.
IEEE DOI Different smoothing levels, linked between levels, applied to medical image analysis. BibRef 9006

Morse, B.S.[Bryan S.], Pizer, S.M.[Stephen M.], Liu, A.[Alan],
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Elsevier DOI BibRef 9407

Haring, S., Viergever, M.A., Kok, J.N.,
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IVC(12), No. 6, July-August 1994, pp. 339-344.
Elsevier DOI BibRef 9407

Liu, J.Q., Yang, Y.H.,
Multiresolution Color Image Segmentation,
PAMI(16), No. 7, July 1994, pp. 689-700.
IEEE DOI Segmentation, Color. Evaluation, Segmentation. Segmentation, Evaluation. Introduced an evaluation function for segmentation that rewards fewer regions, larger (i.e. similar sizes) regions, and uniform regions. SQRT(Regions)*Sum Over Regions of (difference from mean squared divided by SQRT(region size). Use neighborhoods. BibRef 9407

Chen, Y.S., Yen, H.T., Hsu, W.H.,
Compression of Color Image via the Technique of Surface Fitting,
GMIP(56), No. 3, May 1994, pp. 272-279. Compression, Color. Segmentation, Color. BibRef 9405
Earlier:
Color image coding by using the technique of surface fitting,
ICPR92(III:37-40).
IEEE DOI 9208
BibRef

Williams, L.C.[Leon C.],
Automatic image segmentation with smoothing,
US_Patent5,327,262, July 5, 1994.
WWW Link. BibRef 9407

Nacken, P.F.M.[Peter F.M.],
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PR(28), No. 6, June 1995, pp. 907-920.
Elsevier DOI BibRef 9506

Bister, M., Cornelis, J., Rosenfeld, A.,
A Critical View of Pyramid Segmentation Algorithms,
PRL(11), 1990, pp. 605-617. BibRef 9000

Bajcsy, P.[Peter], Ahuja, N.[Narendra],
Location Based and Density Based Hierarchical Clustering Using Similarity Analysis,
PAMI(20), No. 9, September 1998, pp. 1011-1015.
IEEE DOI 9809
BibRef
Earlier:
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DARPA97(541-546). BibRef

Bajcsy, P.[Peter], Ahuja, N.[Narendra],
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Brandtberg, T.[Tomas], Walter, F.[Fredrik],
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Brandtberg, T.,
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Guichard, F.[Frederic], and Morel, J.M.[Jean-Michel],
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Teixeira, M.D.[Marta D.], Leite, N.J.[Neucimar J.],
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ICIP99(27AP1). Not in proceedings. BibRef 9900

Polo, G.[Guilherme], Leite, N.J.[Neucimar J.],
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ISMM13(377-389).
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Prewer, D.[David], Kitchen, L.[Les],
Soft image segmentation by weighted linked pyramid,
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Cheng, H.[Hui], Bouman, C.A.,
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BibRef
Earlier:
Trainable context model for multiscale segmentation,
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Petrovic, A., Escoda, O.D., Vandergheynst, P.,
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de Andrade, M.C.[Marcos Carneiro],
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O'Callaghan, R.J., Bull, D.R.,
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IP(14), No. 1, January 2005, pp. 49-62.
IEEE DOI 0501
BibRef

Erikson, M.[Mats],
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BibRef

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BibRef
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HTML Version. 0509
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Ilea, D.E.[Dana Elena], Whelan, P.F.[Paul F.],
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IEEE DOI 0809
BibRef
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IEEE DOI 0709
Smooth before segmentation. BibRef

Ilea, D.E.[Dana E.], Whelan, P.F.[Paul F.],
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Colour-texture integration; Image segmentation; Feature extraction; Databases; Evaluation measures BibRef

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Subakan, Ö.N.[Özlem N.], Vemuri, B.C.[Baba C.],
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IJCV(91), No. 3, February 2011, pp. 233-250.
WWW Link. 1102
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Quaternion-Based Color Image Smoothing Using a Spatially Varying Kernel,
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Springer DOI 0908
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Subakan, O.N.[Ozlem N.], Jian, B.[Bing], Vemuri, B.C.[Baba C.], Vallejos, C.E.[C. Eduardo],
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Scale-space theory; Scale-space tree; Object segmentation; Object detection; Feature stability; Feature vector; Blob's lifetime; Blob attributes BibRef

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