Prewitt, J.M.S.,
Object Enhancement and Extraction,
PPP70(75-149).
Segmentation, Histogram.
Segmentation, Three Classes.
This early histogram based approach was applied to images of cells
to find the three types of regions. Two peaks occurred in the
histogram (the third region is the brightest areas) which leads to
two threshold settings to extract the regions. Also has Prewitt
operator for edge detection.
BibRef
7000
Prewitt, J.M.S., and
Mendelsohn, M.L.,
The Analysis of Cell Images,
Annals NY Acad. Sci(128), 1966, pp. 1035-1053.
BibRef
6600
Wu, S.C.,
Prewitt, J.M.S., and
Lehman, J.,
To Extract a Connected Object of Arbitrary Shape from its Background by
Decision Tree Method,
PRIP78(352-353).
BibRef
7800
Hennis, R.B.,
The IBM 1975 Optical Page Reader, Part I: System Design,
IBMRD(12), September 1968, pp. 346-353.
BibRef
6809
Bartz, M.R.,
The IBM 1975 Optical Page Reader, Part II: Video Thresholding,
IBMRD(12), September 1968.
BibRef
6809
And:
CMetImAly77(59-68).
Segmentation, Binarization.
Recognize Characters.
OCR. The optical page reader was designed to recognize input in multiple
fonts, this paper reports on the system used to find the individual
characters. The basic threshold is given as a function of the
average contrast over a specified area. This value is adjusted
(locally) to optimize the line widths of the characters and to
eliminate spatial noise near a character. This early system works
on very simple, well-defined scenes, but shows the capabilities of a
thresholding system.
BibRef
Chow, C.K., and
Kaneko, T.,
Boundary Detection of Radiographic Images by a Threshold Method,
FPR72(61-82). 1972.
BibRef
7200
Earlier:
IBMResearch Report RC 3202, December 8, 1970.
Segmentation, Divide and Conquer.
Segmentation, Multi-Level. Divide the image and apply the method in each sub-area.
BibRef
Chow, C.K., and
Kaneko, T.,
Automatic Boundary Detection of the Left Ventricle from Cineangiograms,
Comp. Biomed. Res.(5), 1972, pp. 388-410.
BibRef
7200
Morrin, T.H.,
A Black-White Representation of a Gray-Scale Picture,
TC(23), 1974, pp. 184-186.
BibRef
7400
Ullmann, J.R.[Julian R.],
Binarization Using Associative Addressing,
PR(6), No. 2, October 1974, pp. 127-135.
Elsevier DOI
BibRef
7410
Hu, Q.M.,
Hou, Z.,
Nowinski, W.L.,
Supervised Range-Constrained Thresholding,
IP(15), No. 1, January 2006, pp. 228-240.
IEEE DOI
0601
BibRef
Hou, Z.,
Hu, Q.M.,
Nowinski, W.L.,
On minimum variance thresholding,
PRL(27), No. 14, 15 October 2006, pp. 1732-1743.
Elsevier DOI
0609
Image thresholding; Centroid; Class variance; Class probability
BibRef
Liu, X.,
Wang, D.,
Image and Texture Segmentation Using Local Spectral Histograms,
IP(15), No. 10, October 2006, pp. 3066-3077.
IEEE DOI
0609
BibRef
Liu, X.,
Wang, D.,
Srivastava, A.,
Image Segmentation Using Local Spectral Histograms,
ICIP01(I: 70-73).
IEEE DOI
0108
BibRef
Yuan, J.Y.[Jiang-Ye],
Wang, D.L.[De-Liang],
Li, R.X.[Rong-Xing],
Image segmentation using local spectral histograms and linear
regression,
PRL(33), No. 5, 1 April 2012, pp. 615-622.
Elsevier DOI
1202
Texture segmentation; Spectral histogram; Linear regression
BibRef
Razmjooy, N.[Navid],
Mousavi, B.S.[B. Somayeh],
Khalilpour, M.[Mohsen],
Hosseini, H.[Hossein],
Automatic selection and fusion of color spaces for image thresholding,
SIViP(8), No. 4, May 2014, pp. 603-614.
Springer DOI
1404
BibRef
Feitosa, R.Q.,
Ferreira, R.S.,
Almeida, C.M.,
Camargo, F.F.,
Costa, G.A.O.P.,
Similarity Metrics for Genetic Adaptation of Segmentation Parameters,
GEOBIA10(xx-yy).
PDF File.
1007
BibRef
Feitosa, R.Q.,
Costa, G.A.O.P.,
Cazes, T.B.,
A genetic approach for the automatic adaptation of segmentation
parameters,
OBIA06(xx-yy).
PDF File.
0607
BibRef
Kasvand, T.,
Scene Segmentation and Segment Clustering Experiments,
ICPR78(426-429).
BibRef
7800
Kasvand, T.,
Segmentation of Single Gray Level Pictures of General 3D Scenes,
ICPR74(372-373).
BibRef
7400
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Complete Segmentation Systems Based on Ohlander Technique .