Beveridge, J.R.[J. Ross],
Griffith, J.S.[Joey S.],
Kohler, R.R.[Ralf R.],
Hanson, A.R., and
Riseman, E.M.,
Segmenting Images Using Localized Histograms and Region Merging,
IJCV(2), No. 3, January? 1989, pp. 311-352.
Springer DOI
BibRef
8901
And:
COINSTR 87-88. October 1987.
Segmentation, Systems.
Segmentation, Region Merging. This is the newest UMass segmentation ideas. The first step is to
divide the region into overlapping sectors and generate thresholds
based on their histograms. Adjacent thresholds are propagated if
necessary and these are merged. The results are good, but the
complexity is high. This uses a lot of the ideas from the earlier
segmentation work and changes things since the earlier ideas did
not work that well.
BibRef
Hanson, A.R., and
Riseman, E.M.,
Segmentation of Natural Scenes,
CVS78(xx-yy).
System: VISIONS.
BibRef
7800
Nagin, P.A.,
Hanson, A.R., and
Riseman, E.M.,
Studies in Global and Local Histogram Guided Relaxation Algorithms,
PAMI(4), No. 3, May 1982, pp. 263-277.
BibRef
8205
Earlier:
Region Extraction and Description Through Planning,
COINS-TR 77-8, May 1977.
BibRef
Earlier: A3, A2, A1:
Region Growing in Textured Outdoor Scenes,
UMass-TR-75C-3, February 1975.
Segmentation, Systems.
Segmentation.
Relaxation. Histogram guided is limited to initial threshold selection based on
significant peaks, which, when there is substantial overlap,
results in mixed assignments and in breaking up regions. All
assignments are made all at once. (n-tuple of probability values)
Relax to up date assignment vectors. Suppress very small regions.
Checks each region (multi-model) individually. No references to
Ohlander! A set of artificial image examples which should be
trivial using OPR-thing diagonal and lines may be hard. They also
introduce local segmentation (several (4) parts of the image and
combine results at borders, also use local results to improve
compatibility coefficients.
BibRef
Price, K.E.,
Image Segmentation: A Comment on 'Studies in Global
and Local Histogram-Guided Relaxation Algorithms',
PAMI(6), No. 2, March 1984, pp. 247-249.
BibRef
8403
USC Computer VisionDiscussion of some of the flaws in the named paper.
BibRef
Hanson, A.R.,
Riseman, E.M., and
Nagin, P.A.,
Authors' Reply,
PAMI(6), No. 2, March 1984, pp. 249.
BibRef
8403
Kohler, R.R.[Ralf R.],
A Segmentation System Based on Thresholding,
CGIP(15), No. 4, April 1981, pp. 319-338.
Elsevier DOI
Segmentation, Thresholds.
Segmentation, Edges. Edges which correspond to real boundaries tend to have high
contrast, so the optimum threshold is the one that detects more
high contrast edges and fewer low contrast edges.
BibRef
8104
Kohler, R.R.[Ralf R.],
Integrating Non-Semantic Knowledge into Image Segmentation Processes,
Ph.D.Thesis (CS), 1984,
BibRef
8400
COINS-TR-84-04, UMass.
Segmentation, Knowledge. Also discusses systems issues for linking Fortran and Lisp.
BibRef
Nagin, P.A.,
Studies in Image Segmentation Algorithms Based on
Histogram Clustering and Relaxation,
Ph.D.Thesis (CS), September 1979.
BibRef
7909
COINS-TR-79-15, UMass..
See also Studies in Global and Local Histogram Guided Relaxation Algorithms. for paper from this.
BibRef
Nagin, P.A.,
Kohler, R.R.,
Hanson, A.R., and
Riseman, E.M.,
Segmentation, Evaluation, and Natural Scenes,
PRIP79(515-522).
BibRef
7900
Prager, J.M.,
Extracting and Labeling Boundary Segments in Natural Scenes,
PAMI(2), No. 1, January 1980, pp. 16-26.
BibRef
8001
Earlier:
Add: A2, A3:
Hanson, A.R., and
Riseman, E.M.,
COINSTR 77-7, May 1977.
Relaxation. Determine whether the segments are menaingful.
BibRef
Prager, J.M.,
Segmentation of Static and Dynamic Scenes,
MITCS TR 79-7, May 1979.
BibRef
7905
Prager, J.M.,
Nagin, P.A.,
Kohler, R.R.,
Hanson, A.R., and
Riseman, E.M.,
Segmentation Processes in the VISIONS System,
IJCAI77(642-644).
BibRef
7700
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Segmentation by Thresholding, Quantization, or Relaxation .