Ohlander, R.[Ronald],
Price, K.E.[Keith E.],
Reddy, D.R.[D. Raj],
Picture Segmentation Using a Recursive Region Splitting Method,
CGIP(8), No. 3, December 1978, pp. 313-333.
Elsevier DOI
PDF File.
Color.
Segmentation, Systems.
Segmentation, Histogram.
Segmentation, Ohlander.
Segmentation, Region Splitting.
Select a region, generate histograms, choose the threshold based on
the best separated peaks, repeat until uniform regions or too
small. Various modifications are added to make it work for large
and gray-level images.
BibRef
7812
Price, K.E.,
Segmentation,
PRIP79(512-514).
BibRef
7900
USC Computer Vision
BibRef
Ohlander, R.[Ronald],
Analysis of Natural Scenes,
Ph.D.Thesis (CS), 1975,
BibRef
7500
CMU-CS-TR-April, 1975.
Original for the above paper.
BibRef
Shafer, S.A.[Steven A.],
MOOSE Users' Manual Implementation Guide Evaluation,
TR IfI-HH-B-70/80, Bericht 70,
Univ. HamburgApril 1980.
Evaluation, Segmentation.
Segmentation, Evaluation. An implementation of the
Ohlander segmentation by Shafer when visiting U of Hamburg.
BibRef
8004
Shafer, S.A., and
Kanade, T.,
Recursive Region Segmentation by Analysis of Histograms,
ICASSP82(1166-1171).
Segmentation, Systems.
Phoenix.
System: Phoenix.
HTML Version.
See also Phoenix Image Segmentation System: Description and Evaluation, The. After implementing a version of the Ohlander segmentation
technique, Shafer proposed and implemented a variation that used
the type of regions generated by the various possible threshold to
determine the optimal threshold. This method applied all
reasonable thresholds, as determined by analyzing the histograms,
and chose the set of regions which were the most compact and had
the clearest borders. This is based on the observation that,
often, several histograms have peaks that correspond to the same
regions, but one may give a more precise split than another even
when its peak is not as clear according to the given criteria.
BibRef
8200
Laws, K.I.,
The Phoenix Image Segmentation System: Description and Evaluation,
SRI AICenter-TN 289, December 1982.
Evaluation, Segmentation.
System: Phoenix.
Phoenix.
Segmentation, Evaluation.
BibRef
8212
Laws, K.I.,
On the Evaluation of Scene Analysis Algorithms,
DARPA83(148-155).
Segmentation, Evaluation.
BibRef
8300
Bhanu, B.[Bir], and
Lee, S.K.[Sung-Kee],
Genetic Learning for Adaptive Image Segmentation,
Hingham, MA:
KluwerAcademic Press, 1994.
ISBN 0-7923-9491-7, 296pp.
WWW Link.
Genetic Algorithms.
Segmentation, Learning. Complete description of segmentation system
BibRef
9400
Bhanu, B.[Bir],
Lee, S.K.[Sung-Kee],
Ming, J.C.[John C.],
Adaptive image segmentation system,
US_Patent5,048,095, Sep 10, 1991
WWW Link.
BibRef
9109
Bhanu, B.,
Lee, S.,
Das, S.,
Adaptive Image Segmentation Using Genetic And Hybrid Search Methods,
AeroSys(31), No. 4, October 1995, pp. 1268-1291.
BibRef
9510
Bhanu, B.,
Lee, S., and
Ming, J.,
Adaptive Image Segmentation Using a Genetic Algorithm,
SMC(25), No. 12, December 1995, pp. 1543-1567.
BibRef
9512
Earlier:
DARPA89(1043-1055).
BibRef
And:
Self-Optimizing Control System for Adaptive Image Segmentation,
DARPA90(583-596).
BibRef
Earlier: A1, A3 and A2:
Adaptive Image Segmentation,
Draftversion.
BibRef
Earlier:
Closed-Loop Adaptive Image Segmentation,
CVPR91(734-735).
IEEE DOI Given a known (hand) segmentation, vary the parameters of the
Phoenix segmentation system (from the IU Testbed (
See also Phoenix Image Segmentation System: Description and Evaluation, The. ))
to get the best
fit. The program learns the parameter values through a genetic
algorithm (mutation or exchange) rather than a complete search.
BibRef
Bhanu, B.[Bir],
Bao, X.[Xin],
Ping, J.[Jing],
Reinforcement learning Integrated Image Segmentation
and Object Recognition,
DARPA97(1145-1154).
BibRef
9700
Bhanu, B.[Bir],
Lee, S.K.[Sung-Kee], and
Das, S.[Subhodev],
Adaptive Image Segmentation Using
Multi-Objective Evaluation and Hybrid Search Methods,
AAAI-MLCV93University of California, Riverside.
BibRef
9300
Bhanu, B.[Bir],
Fonder, S.[Stephanie],
Learning Based Interactive Image Segmentation,
ICPR00(Vol I: 299-302).
IEEE DOI
0009
BibRef
Bhanu, B., and
Faugeras, O.D.,
Segmentation of Images Having Unimodal Distributions,
PAMI(4), No. 4, July 1982, pp. 408-419.
BibRef
8207
And:
Correction:
PAMI(4), No. 6, November 1982, pp. 689.
Segmentation, Unimodal.
BibRef
Bhanu, B.[Bir],
Parvin, B.A.[Bahram A.],
Segmentation of Natural Scenes,
PR(20), No. 5, 1987, pp. 487-496.
Elsevier DOI
BibRef
8700
Earlier:
Segmentation of Images Using a Relaxation Technique,
CVPR83(151-153).
Segmentation, Relaxation.
BibRef
Ohta, Y.[Yu_Ichi],
Kanade, T.[Takeo], and
Sakai, T.[Toshiyuki],
Color Information for Region Segmentation,
CGIP(13), No. 3, July 1980, pp. 222-241.
Elsevier DOI
BibRef
8007
And:
A Production System for Region Analysis,
IJCAI79(684-686).
Segmentation, Color.
Color, Transforms. Optimal transform generation,
uses
See also Picture Segmentation Using a Recursive Region Splitting Method. for the basic method.
Computed the eigenvectors for a number of sample images to find
optimal weights for the transforms for segmentation.
The first is close to just an average of the 3 ((R+G+B)/3).
The others are: I2=(R-B)/2 (or (B-R)/2); and I3=(2G -R -B)/4.
This linear transform generates results similar to dynamic Karhunev Loeve
transformation of RGB.
BibRef
Ohta, Y.[YuIchi],
Knowledge-Based Interpretation of Outdoor Natural Color Scenes,
Morgan Kaufmann1985.
BibRef
8500
Bookversion of the thesis?
BibRef
Ohta, Y.[YuIchi],
A Region-Oriented Image-Analysis System by Computer,
Ph.D.Thesis (Info. Sci.), March 1980.
BibRef
8003
Kyoto Univ.
Model Based Recognition. A more complete report than the segmentation paper above.
BibRef
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Complete Systems Derived from the Univ. Massachusetts Work .