8 2-D Region Segmentation Techniques, Snakes, Active Contours

8.1 Segmentation, Survey and General Topics

Chapter Contents (Back)
Survey, Segmentation. Segmentation, Survey.

Riseman, E.M., and Arbib, M.A.,
Computational Techniques in the Visual Segmentation of Static Scenes,
CGIP(6), No. 3, June 1977, pp. 221-276.
Elsevier DOI Survey, Segmentation. Segmentation, Survey. Segmentation, Color. Color Segmentation. Relaxation. Relaxation, Edges. Use of knowledge about scene in analysis (i.e., segmentation.); requires new structures for each type of scene (but [apparently] people don't need this.); usually boundaries are visible in intensity, but color is important; texture - hierarchical approach; boundaries; relaxation; region formation - growing, clusters, both?; labeled/unlabeled drawing; Ohlander; 2-D histogram as equivalent to 1-D histogram and not clearly explained, but...; result: combine many algorithms, redundant information/ representations (cones!), pool of features, (general system rather than specific). BibRef 7706

Fu, K.S., Mui, J.K.,
A survey on Image Segmentation,
PR(13), No. 1, 1981, pp. 3-16.
WWW Link. Survey, Segmentation. Segmentation, Survey. Since little is known about how to measure segmentation, no comments on how well an algorithm works. BibRef 8100

Haralick, R.M., and Shapiro, L.G.,
Image Segmentation Techniques,
CVGIP(29), No. 1, January 1985, pp. 100-132.
WWW Link. (Then at Machine Vision Intl.) Evaluation, Segmentation. Survey, Segmentation. Segmentation, Criteria. A survey of a large number of segmentation methods. Techniques include spatial clustering, thresholding, region growing, split and merge. Examples are given of various methods. Criteria for a good segmentation: uniform and homogeneous with respect to some feature. Adjacent regions should have significantly different values (w.r.t. same feature). Region interiors should be simple, not ragged, and spatially accurate. BibRef 8501

di Zenzo, S.,
Advances in Image Segmentation,
IVC(1), No. 4, November 1983, pp. 196-210.
WWW Link. BibRef 8311

Nevatia, R.,
Image Segmentation,
HPRIP86(215-231). BibRef 8600 USC Computer Vision Survey, Segmentation. Segmentation, Survey. BibRef

Zucker, S.W.,
Algorithms for Image Segmentation,
DIPA77(169-183). Segmentation, Algorithms. BibRef 7700

Sklansky, J.,
Image Segmentation and Feature Extraction,
SMC(8), No. 4, 1978, pp. 237-247.
IEEE DOI BibRef 7800

Rosenfeld, A., and Davis, L.S.,
Image Segmentation and Image Models,
PIEEE(67), No. 5, May 1979, pp. 764-772. BibRef 7905

Kanade, T.[Takeo],
Region Segmentation: Signal vs. Semantics,
CGIP(13), No. 4, August 1980, pp. 279-297.
WWW Link. BibRef 8008
Earlier: ICPR78(95-105). Segmentation, Knowledge. BibRef

Mitiche, A.[Amar], Aggarwal, J.K.,
Image Segmentation by Conventional and Information-Integrating Techniques: A Synopsis,
IVC(3), No. 2, May 1985, pp. 50-62.
WWW Link. Survey, Segmentation. Survey type article that discusses segmentation in terms of combining information from several frames (mostly just general segmentation). BibRef 8505

Cooper, M.C.[Martin C.],
The Tractability of Segmentation and Scene Analysis,
IJCV(30), No. 1, October 1998, pp. 27-42.
DOI Link Computational complexity of segmentation. NP. Region merging with global minimum for segmentation. BibRef 9810

Kerfoot, I.B., Bresler, Y.,
Theoretical Analysis of Multispectral Image Segmentation Criteria,
IP(8), No. 6, June 1999, pp. 798-820.
IEEE DOI BibRef 9906

Serra, J.[Jean],
A Lattice Approach to Image Segmentation,
JMIV(24), No. 1, January 2006, pp. 83-130.
Springer DOI 0605

Serra, J.[Jean],
Connective segmentation,

Serra, J.,
Image segmentation,
ICIP03(I: 345-348).

Zhang, Y.J.[Yu-Jin], (Ed.)
Advances in Image and Video Segmentation,
IRM Press2006 ISBN: 1-59140-753-2.
WWW Link. Evaluation, Segmentation. Includes discussion of algorithms and evaluations. BibRef 0600

Dufourd, J.F.[Jean-Francois],
Design and formal proof of a new optimal image segmentation program with hypermaps,
PR(40), No. 11, November 2007, pp. 2974-2993.
WWW Link. 0707
Image segmentation; Hypermaps; Formal specification; Coq system; Computer-aided correctness proof BibRef

Paragios, N.[Nikos],
Guest Editorial: Special Issue on Variational and Level Set Methods in Computer Vision,
IJCV(50), No. 3, December 2002, pp. 235-235.
DOI Link 0211

Wang, Q., Li, S.Y.,
Database of human segmented images and its application in boundary detection,
IET-IPR(6), No. 3, 2012, pp. 222-229.
DOI Link 1204
Dataset, Segmentation. BibRef

Cremers, D.[Daniel],
Optimal solutions for semantic image decomposition,
IVC(30), No. 8, August 2012, pp. 476-477.
Elsevier DOI 1209
Opinion paper; Optimization; Efficient algorithms; Convexity; Semantic labeling BibRef

Lücking, A.[Andy], Ptock, S.[Sebastian], Bergmann, K.[Kirsten],
Assessing Agreement on Segmentations by Means of Staccato, the Segmentation Agreement Calculator according to Thomann,
Springer DOI 1211

Grady, L.[Leo], Jolly, M.P.[Marie-Pierre], Seitz, A.[Aaron],
Segmentation from a box,
User draws a box around the region. Study whether this should work by human tests. BibRef

Viitaniemi, V.[Ville], Laaksonen, J.T.[Jorma T.],
Techniques for Image Classification, Object Detection and Object Segmentation,
Springer DOI 0809

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Comparison and Evaluation of Different Techniques, Segmentation Evaluation, Benchmarks .

Last update:Jun 24, 2017 at 21:08:28