8.3 Global - Threshold Based Segmentation Techniques

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The early work was to separate an object from its background. Ohlander advanced the technique to general multi-spectral and multi-object images. This basic method was misinterpreted but for a basic method is still the best. Several different implementations have been done. Segmentation, Thresholds.

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.

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.

Liu, X., Wang, D., Srivastava, A.,
Image Segmentation Using Local Spectral Histograms,
ICIP01(I: 70-73).

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

Richtsfeld, A.[Andreas], Zillich, M.[Michael], Vincze, M.[Markus],
Implementation of Gestalt principles for object segmentation,
WWW Link. 1302

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,
PDF File. 1007

Feitosa, R.Q., Costa, G.A.O.P., Cazes, T.B.,
A genetic approach for the automatic adaptation of segmentation parameters,
PDF File. 0607

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 .

Last update:May 23, 2024 at 14:31:23