8.3.7 Histogram Analysis for Threshold Selection and Segmentation

Chapter Contents (Back)
Threshold Selection. Segmentation, Thresholds. Segmentation, Histogram. Histogram Analysis. These tend to assume that the thresholds can be performed all at once, thus are doing more work than is really necessary.

Hummel, R.A.[Robert A.],
Histogram Modification Techniques,
CGIP(4), No. 3, September 1975, pp. 209-224.
Elsevier DOI Analysis of transformations. BibRef 7509

Frei, W.[Werner],
Image Enhancement by Histogram Hyperbolization,
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Elsevier DOI Contrast enhancement. BibRef 7706

Blumenthal, A.F., Davis, L.S., Rosenfeld, A.,
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Lee, S.U.[Sang Uk], Chung, S.Y.[Seok Yoon], Park, R.H.[Rae Hong],
A Comparative Performance Study of Several Global Thresholding Techniques for Segmentation,
CVGIP(52), No. 2, November 1990, pp. 171-190.
Elsevier DOI Evaluation, Segmentation. Segmentation, Evaluation. Thresholds, Evaluation. Compares 5 different techniques (
See also Minimum Error Thresholding.
See also Threshold Selection Method from Grey-Level Histograms, A.
See also New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram, A.
See also Moment-Preserving Thresholding: A New Approach. and
See also Threshold Selection Using Quadtrees. ). The first two were rated best (Simple image statistic and Between class variance). BibRef 9011

Rosenfeld, A., and Davis, L.S.,
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Peleg, S.,
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SMC(8), No. 7, 1978, pp. 555-556. BibRef 7800

Otsu, N.,
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SMC(9), No. 1, January 1979, pp. 62-66. A Variance measure for threshold selection. Compared in:
See also Comparative Performance Study of Several Global Thresholding Techniques for Segmentation, A. Analysis in:
See also Comment on Using the Uniformity Measure for Performance-Measure in Image Segmentation. Code:
See also C++ Implementation of Otsu's Image Segmentation Method, A. BibRef 7901

Kurita, T., Otsu, N., and Abdelmalek, N.,
Maximum Likelihood Thresholding Based on Population Mixture Models,
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Elsevier DOI BibRef 9210

Rosenfeld, A., and de la Torre, F.[Fernando],
Histogram Concavity Analysis as an Aid in Threshold Selection,
SMC(13), No. 3, March 1983, pp. 231-235. BibRef 8303

Boukharouba, S., Rebordao, J.M., and Wendel, P.L.,
An Amplitude Segmentation Method Based on the Distribution Function of an Image,
CVGIP(29), No. 1, January 1985, pp. 47-59.
Elsevier DOI Use the curvature of the cumulative histogram for determining the place to perform the threshold. Results are unclear, since they are using it for image compression or coding rather than standard segmentation. Further derivation:
See also Peak Detection Algorithm and Its Application to Histogram-Based Image Data Reduction, A. BibRef 8501

Wang, S.[Shyuan], and Haralick, R.M.[Robert M.],
Automatic Multithreshold Selection,
CVGIP(25), No. 1, January 1984, pp. 46-67.
Elsevier DOI A recursive segmentation technique that looks at edge pixel values separated by those on the "bright" and "dark" side of the edge. BibRef 8401

Wu, A.Y., Hong, T.H., and Rosenfeld, A.,
Threshold Selection Using Quadtrees,
PAMI(4), No. 1, January 1982, pp. 90-94. Segmentation, Multi-Level. Histogram based thresholds using the quadtree representation to eliminate small features. Compared in :
See also Comparative Performance Study of Several Global Thresholding Techniques for Segmentation, A. BibRef 8201

Weszka, J.S., Nagel, R.N., and Rosenfeld, A.,
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TC(23), 1974, pp. 1322-1326. BibRef 7400
A Technique for Facilitating Threshold Selection for Object Extraction from Digital Pictures,
UMDTR, 1973. BibRef

Weszka, J.S., and Rosenfeld, A.,
Histogram Modification for Threshold Selection,
SMC(9), No. 1, January 1979, pp. 38-52. BibRef 7901

Weszka, J.S., and Rosenfeld, A.,
Threshold Evaluation Techniques,
SMC(8), 1978, pp. 622-629. Thresholds, Evaluation. BibRef 7800

Pal, S.K., and Pal, N.R.,
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Murthy, C.A., Pal, S.K.,
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Sahasrabudhe, S.C., Das Gupta, K.S.,
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Pal, S.K., Das Gupta, A.,
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Kapur, J.N., Sahoo, P.K., and Wong, A.K.C.,
A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram,
CVGIP(29), No. 3, 1985, pp. 273-285.
Elsevier DOI Histogram based threshold selection of a single threshold to binarize the image based on the entropy measure. Bi-modal histograms. Interesting results, not clear what it means for region segmentation. Has a set of references of threshold selection methods. The extension to mulit-modal has efficiency problems. (
See also Parallel Entropic Auto-Thresholding. ) Compared in :
See also Comparative Performance Study of Several Global Thresholding Techniques for Segmentation, A. BibRef 8500

Kapur, J.N.,
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Wong, A.K.C., and Sahoo, P.K.,
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Sahoo, P.K.[Prasanna K.], Wilkins, C.[Carrye], Yeager, J.[Jerry],
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Elsevier DOI 9702

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Sahoo, P.K.[Prasanna K.], Arora, G.[Gurdial],
Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy,
PRL(27), No. 6, 15 April 2006, pp. 520-528.
Elsevier DOI Image segmentation; Thresholding; Tsallis-Havrda-Charvát entropy 0604

Borjigin, S.[Surina], Sahoo, P.K.[Prasanna K.],
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Color image segmentation, Multi-level thresholding, Two-dimensional histogram, Tsallis-Havrda-Charvát entropy, PSO BibRef

Lim, Y.W.[Young Won], Lee, S.U.[Sang Uk],
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Elsevier DOI Hierarchical segmentation using a scale space filter. BibRef 9000

Tsai, W.H.,
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CVGIP(29), No. 3, March 1985, pp. 377-393. Another threshold selection method based on histogram analysis, the moments are preserved in the thresholded image. Compared in :
See also Comparative Performance Study of Several Global Thresholding Techniques for Segmentation, A.
See also Moment-Preserving Sharpening: A New Approach to Digital Picture Deblurring. BibRef 8503

Carlotto, M.J.[Mark J.],
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PAMI(9), No. 1, January 1987, pp. 121-129. BibRef 8701
Earlier: CVPR85(334-340). Scale Space. The Analytic Sciences Corp. Approximate the histogram by a sum of gaussian distributions. This gives better threshold choices. The modeling is done using different size gaussian smoothing functions. BibRef

Pizer, S.M.[Stephen M.], Amburn, E.P.[E. Philip], Austin, J.D.[John D.], Cromartie, R.[Robert], Geselowitz, A.[Ari], Greer, T.[Trey], ter Haar Romeny, B.M.[Bart M.], Zimmerman, J.B.[John B.], Zuiderveld, K.[Karel],
Adaptive Histogram Equalization and Its Variations,
CVGIP(39), No. 3, September 1987, pp. 355-368.
Elsevier DOI BibRef 8709

Touzani, A., and Postaire, J.G.,
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IEEE DOI BibRef 8811

Sezan, M.I.[M. Ibrahim],
A Peak Detection Algorithm and Its Application to Histogram-Based Image Data Reduction,
CVGIP(49), No. 1, January 1990, pp. 36-51.
Elsevier DOI Find the peaks and use them to quantize the image for data reduction and reconstruction. Derived from
See also Amplitude Segmentation Method Based on the Distribution Function of an Image, An. using a simpler filter on the histogram. BibRef 9001

Jolion, J.M.[Jean-Michel], and Rosenfeld, A.[Azriel],
Coarse-Fine Bimodality Analysis of Circular Histogram,
PRL(10), 1989, pp. 201-207. Pyramid Technique. BibRef 8900

Leszczynski, K.W.[Konrad W.], Shalev, S.[Shlomo],
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IVC(7), No. 3, August 1989, pp. 205-209.
Elsevier DOI Moving Histogram Equalization. BibRef 8908

O'Gorman, L.[Lawrence],
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Tsai, D.M., and Chen, Y.H.,
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Find modes in a gray-level histogram. Compares most standard techniques that can be parallelized. BibRef

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See also skimming technique for fast accurate edge detection, A. BibRef

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Compare to
See also New Approach For Multilevel Threshold Selection, A.
See also New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram, A. and
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Histogram; Intensity contrast; Small object segmentation; Prior knowledge BibRef

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Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Fuzzy Threshold Segmentation .

Last update:Nov 30, 2023 at 15:51:27