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.,
Histogram Modification Techniques,
CGIP(4), No. 3, September 1975, pp. 209-224.
WWW Link. BibRef 7509

Frei, W.,
Image Enhancement by Histogram Hyperbolization,
CGIP(6), No. 3, June 1977, pp. 286-294.
WWW Link. BibRef 7706

Blumenthal, A.F., Davis, L.S., Rosenfeld, A.,
Detecting Natural 'Plateaus' in One-Dimensional Patterns,
TC(26), 1977, pp. 178-179. BibRef 7700

Leboucher, G., Lowitz, G.E.,
What a Histogram Can Really Tell the Classifier,
PR(10), No. 5-6, 1978, pp. 351-357.
WWW Link. See also What the Fourier Transform Can Really Bring to Clustering. BibRef 7800

Nahin, P.J.,
A Simplified Derivation of Frei's Histogram Hyperbolization for Image Enhancement,
PAMI(1), No. 4, October 1979, 414-415. See also Image Enhancement by Histogram Hyperbolization. BibRef 7910

Ridler, T.W., and Calvard, S.,
Picture Thresholding Using an Iterative Selection Method,
SMC(8), No. 8, August 1978, pp. 629-632. Segmentation, Binarization. Guess the object and background level, choose a threshold, update the guess and the threshold. For bi-modal histograms, to find a threshold between the means. BibRef 7808

Suk, M.S., and Jung, S.M.,
A New Image Segmentation Technique Based on Partition Mode Test,
PR(16), No. 5, 1983, pp. 469-480.
WWW Link. Can be used for finding multiple motions. BibRef 8300

Rix, H.,
Separation of Equal Shape Overlapping Peaks,
SP(5), 1983, pp. 97-103. BibRef 8300

Ku, F.N.,
The Principles and Methods of Histogram Modification Adapted for Visual Perception,
CVGIP(26), No. 1, April 1984, pp. 107-117.
WWW Link. BibRef 8404

Kautsky, J., Nichols, N.K., Jupp, D.L.B.,
Smoothed Histogram Modification for Image Processing,
CVGIP(26), No. 3, June 1984, pp. 271-291.
WWW Link. BibRef 8406

Jain, R., and Chlamtac, I.,
The P(2) Algorithm for Dynamic Calculation of Quantiles and Histograms without Storing Observations,
CACM(28), No. 10, October 1985, pp. 1076-1085. BibRef 8510

Zito, R.R.,
The Shape of SAR Histograms,
CVGIP(43), No. 3, September 1988, pp. 281-293.
WWW Link. BibRef 8809

Lee, S.U., Chung, S.Y., and Park, R.H.,
A Comparative Performance Study of Several Global Thresholding Techniques for Segmentation,
CVGIP(52), No. 2, November 1990, pp. 171-190.
WWW Link. 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.,
Iterative Histogram Modification,
SMC(8), No. 4, 1978, pp. 300-302. BibRef 7800

Peleg, S.,
Iterative Histogram Modification,
SMC(8), No. 7, 1978, pp. 555-556. BibRef 7800

Otsu, N.,
A Threshold Selection Method from Grey-Level Histograms,
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,
PR(25), No. 10, October 1992, pp. 1231-1240.
WWW Link. BibRef 9210

Rosenfeld, A., and de la Torre, P.,
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.
WWW Link. 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.
WWW Link. 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.,
A Threshold Selection Technique,
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.,
Segmentation Based on Measures of Contrast, Homogeneity, and Region Size,
SMC(17), No. 5, Sept/October 1987, pp. 857-868. It also includes some region merging. BibRef 8710

Murthy, C.A., Pal, S.K.,
Histogram Thresholding by Minimizing Graylevel Fuzziness,
IS(60), 1992, pp. 107-135. BibRef 9200

Sahasrabudhe, S.C., Das Gupta, K.S.,
A Valley-Seeking Threshold Selection Technique,
CVIP92(55-65). BibRef 9200

Pal, S.K., Das Gupta, A.,
Spectral Fuzzy Sets and Soft Thresholding,
IS(65), 1992, pp. 65-97. BibRef 9200

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.
WWW Link. 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.,
Application of entropic measures of stochastic dependence in pattern recognition,
PR(19), No. 6, 1986, pp. 473-476.
WWW Link. 0309

Wong, A.K.C., and Sahoo, P.K.,
A Gray-Level Threshold Selection Method Based on Maximum Entropy Principle,
SMC(19), No. 4, July 1989, pp. 866-871. BibRef 8907

Sahoo, P.K., Wilkins, C., Yeager, J.,
Threshold Selection Using Renyis Entropy,
PR(30), No. 1, January 1997, pp. 71-84.
WWW Link. 9702

Sahoo, P.K.[Prasanna K.], Arora, G.[Gurdial],
A thresholding method based on two-dimensional Renyi's entropy,
PR(37), No. 6, June 2004, pp. 1149-1161.
WWW Link. 0405

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.
WWW Link. Image segmentation; Thresholding; Tsallis-Havrda-Charvát entropy 0604

Lim, Y.W., and Lee, S.U.,
On the Color Image Segmentation Algorithm Based on the Thresholding and the Fuzzy C-Means Techniques,
PR(23), No. 9, 1990, pp. 935-952.
WWW Link. Hierarchical segmentation using a scale space filter. BibRef 9000

Tsai, W.H.,
Moment-Preserving Thresholding: A New Approach,
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.],
Histogram Analysis Using A Scale Space Approach,
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., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B.M., Zimmerman, J.B., Zuiderveld, K.,
Adaptive Histogram Equalization and Its Variations,
CVGIP(39), No. 3, September 1987, pp. 355-368.
WWW Link. BibRef 8709

Touzani, A., and Postaire, J.G.,
Mode Detection by Relaxation,
PAMI(10), No. 6, November 1988, pp. 970-978.
IEEE DOI BibRef 8811

Sezan, M.I.,
A Peak Detection Algorithm and Its Application to Histogram-Based Image Data Reduction,
CVGIP(49), No. 1, January 1990, pp. 36-51.
WWW Link. 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., Shalev, S.,
A Robust Algorithm for Contrast Enhancement by Local Histogram Modification,
IVC(7), No. 3, August 1989, pp. 205-209.
WWW Link. BibRef 8908

O'Gorman, L.,
A Note on Histogram Equalization for Optimal Intensity Range Utilization,
CVGIP(41), No. 2, February 1988, pp. 229-232.
WWW Link. BibRef 8802

McCallum, A.J., Bowman, C.C., Daniels, P.A., Batchelor, B.G.,
A Histogram Modification Unit for Real-Time Image Enhancement,
CVGIP(42), No. 3, June 1988, pp. 387-398.
WWW Link. BibRef 8806

Chochia, P.A.,
Image Enhancement Using Sliding Histograms,
CVGIP(44), No. 2, November 1988, pp. 211-229.
WWW Link. BibRef 8811

Brunelli, R.,
Optimal Histogram Partitioning Using a Simulated Annealing Technique,
PRL(13), 1992, pp. 581-586. Relaxation algorithm to select appropriate thresholds. BibRef 9200

Tsai, D.M., and Chen, Y.H.,
A Fast Histogram-Clustering Approach for Multi-Level Thresholding,
PRL(13), 1992, pp. 245-252. Number of peaks must be known or spurious thresholds will be selected. BibRef 9200

Tsai, D.M.,
A Fast Thresholding Selection Procedure for Multimodal and Unimodal Histograms,
PRL(16), No. 6, June 1995, pp. 653-666. Segmentation, Unimodal. BibRef 9506

Gauch, J.M.[John M.],
Investigations of Image Contrast Space Defined by Variations on Histogram Equalization,
GMIP(54), No. 4, July 1992, pp. 269-280. BibRef 9207

Glasbey, C.A.,
An Analysis of Histogram-Based Thresholding Algorithms,
GMIP(55), No. 6, November 1993, pp. 532-yy. BibRef 9311

Hayat, L., Fleury, M., Clark, A.F.,
Candidate Functions For A Parallel Multilevel Thresholding Technique,
GMIP(58), No. 4, July 1996, pp. 360-381. 9609
Find modes in a gray-level histogram. Compares most standard techniques that can be parallelized. BibRef

Fleury, M., Hayat, L., Clark, A.F.,
Parallel Entropic Auto-Thresholding,
IVC(14), No. 4, May 1996, pp. 247-263.
WWW Link. 9607
Attempts to extend See also New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram, A. to multiple modes. Peaks are selected by simple clipping approach. BibRef

Caglioti, V., Maniezzo, V.,
Mode Determination in Noisy Bimodal Images by Histogram Comparison,
PRL(16), No. 12, December 1995, pp. 1237-1248. BibRef 9512

Stark, J.A., Fitzgerald, W.J.,
An Alternative Algorithm for Adaptive Histogram Equalization,
GMIP(58), No. 2, March 1996, pp. 180-185. BibRef 9603

Yang, C.W., Chung, P.C., Chang, C.,
Hierarchical Fast 2-Dimensional Entropic Thresholding Algorithm Using a Histogram Pyramid,
OptEng(35), No. 11, November 1996, pp. 3227-3241. 9612

Chen, W.T., Wen, C.H., Yang, C.W.,
A Fast 2-Dimensional Entropic Thresholding Algorithm,
PR(27), No. 7, July 1994, pp. 885-893.
WWW Link. BibRef 9407

Jansen, R.C., Reinink, K., van der Heijden, G.W.A.M.,
Analysis of Gray Level Histograms by Using Statistical Methods for Mixtures of Distributions,
PRL(14), 1993, pp. 585-590. BibRef 9300

Davies, E.R.,
Lateral Histograms for Efficient Object Location: Speed Versus Ambiguity,
PRL(6), 1987, pp. 189-198. BibRef 8700

Davies, E.R.,
A Sampling Approach to Ultra-fast Object Location,
RealTimeImg(7), No. 4, August 2001, pp. 339-355.
DOI Link 0110
See also skimming technique for fast accurate edge detection, A. BibRef

Guo, R., Pandit, S.M.,
Automatic Threshold Selection Based on Histogram Modes and a Discriminant Criterion,
MVA(10), No. 5-6, April 1998, pp. 331-338.
HTML Version. 9805

Li, C.H., Tam, P.K.S.,
Modular Expert Network Approach to Histogram Thresholding,
JEI(6), No. 3, July 1997, pp. 286-293. 9807

Kurugollu, F.[Fatih], Sankur, B.[Bülent], Harmanc, A.E.[A. Emre],
Color image segmentation using histogram multithresholding and fusion,
IVC(19), No. 13, November 2001, pp. 915-928.
WWW Link. 0111
See also Image segmentation by relaxation using constraint satisfaction neural network. BibRef

Bonnet, N., Cutrona, J., Herbin, M.,
A 'no-threshold' histogram-based image segmentation method,
PR(35), No. 10, October 2002, pp. 2319-2322.
WWW Link. 0206

Shah-Hosseini, H.[Hamed], Safabakhsh, R.[Reza],
Automatic Multilevel Thresholding for Image Segmentation by the Growing Time Adaptive Self-Organizing Map,
PAMI(24), No. 10, October 2002, pp. 1388-1393.
IEEE Abstract. 0210
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 See also Image Segmentation by a Parallel, Non-Parametric Histogram Based Clustering Algorithm. BibRef

Shah-Hosseini, H.[Hamed], Safabakhsh, R.[Reza],
A TASOM-based algorithm for active contour modeling,
PRL(24), No. 9-10, June 2003, pp. 1361-1373.
WWW Link. 0304
TASOM: Time Adaptive Self-Organizing Map. BibRef

Sadeghi, F.[Fereshteh], Izadinia, H.[Hamid], Safabakhsh, R.[Reza],
A new active contour model based on the Conscience, Archiving and Mean-Movement mechanisms and the SOM,
PRL(32), No. 12, 1 September 2011, pp. 1622-1634.
Elsevier DOI 1108
Active contour model; Self-organizing map; Conscience; Archiving; Mean-movement; Concavity extraction; CAMSOM BibRef

Wang, Q.[Qing], Chi, Z.[Zheru], Zhao, R.C.[Rong-Chun],
Image Thresholding by Maximizing the Index of Nonfuzziness of the 2-D Grayscale Histogram,
CVIU(85), No. 2, February 2002, pp. 100-116.
DOI Link 0210

Tobias, O.J., Seara, R.[Rui],
Image segmentation by histogram thresholding using fuzzy sets,
IP(11), No. 12, December 2002, pp. 1457-1465.

Cheng, H.D., Jiang, X.H., Wang, J.L.[Jing-Li],
Color image segmentation based on homogram thresholding and region merging,
PR(35), No. 2, February 2002, pp. 373-393.
WWW Link. 0201

Baradez, M.O., McGuckin, C.P., Forraz, N., Pettengell, R., Hoppe, A.,
Robust and automated unimodal histogram thresholding and potential applications,
PR(37), No. 6, June 2004, pp. 1131-1148.
WWW Link. 0405

Hoppe, A., Baradez, M.O.,
Thresholding based on linear diffusion for feature segmentation,
HTML Version. 0409

Park, S.J.[Soo Jun], Won, C.S.[Chee Sun], Park, D.K.[Dong Kwon], Choi, D.S.[Dong See], Yoo, S.J.[Seong Joon], Kim, H.J.[Hyun Jin],
Method for generating a block-based image histogram,
US_Patent6,807,298, Oct 19, 2004
WWW Link. BibRef 0410
And: US_Patent7,106,900, Sep 12, 2006
WWW Link. include color, brightness, and edge components BibRef

Arifin, A.Z.[Agus Zainal], Asano, A.[Akira],
Image segmentation by histogram thresholding using hierarchical cluster analysis,
PRL(27), No. 13, 1 October 2006, pp. 1515-1521.
WWW Link. Image thresholding; Clustering; Inter-class variance; Intra-class variance 0606

Qiao, Y.[Yu], Hu, Q.M.[Qing-Mao], Qian, G.Y.[Guo-Yu], Luo, S.[Suhuai], Nowinski, W.L.[Wieslaw L.],
Thresholding based on variance and intensity contrast,
PR(40), No. 2, February 2007, pp. 596-608.
WWW Link. 0611
Histogram; Intensity contrast; Small object segmentation; Prior knowledge BibRef

Hu, Q.M.[Qing-Mao], Luo, S.[Suhuai], Qiao, Y.[Yu], Qian, G.[Guoyu],
Supervised grayscale thresholding based on transition regions,
IVC(26), No. 12, 1 December 2008, pp. 1677-1684.
WWW Link. 0810
Grayscale thresholding; Transition region; Supervision; Prior knowledge BibRef

Zhang, C.L.[Chao-Lin], Zhang, X.G.[Xue-Gong], Zhang, M.Q.[Michael Q.], Li, Y.[Yanda],
Neighbor number, valley seeking and clustering,
PRL(28), No. 2, 15 January 2007, pp. 173-180.
WWW Link. 0611
Nonparametric density estimation; Neighbor number; Valley seeking; Shape-free clustering; Image segmentation BibRef

Delon, J.[Julie], Desolneux, A.[Agnes], Lisani, J.L.[Jose Luis], Petro, A.B.[Ana Belen],
A Nonparametric Approach for Histogram Segmentation,
IP(16), No. 1, January 2007, pp. 253-261.
Color Image Segmentation Using Acceptable Histogram Segmentation,
Springer DOI 0509
Applied to documents. Find small modes in the histogram. BibRef

Shortt, A.E., Naughton, T.J., Javidi, B.[Bahram],
Histogram Approaches for Lossy Compression of Digital Holograms of Three-Dimensional Objects,
IP(16), No. 6, June 2007, pp. 1548-1556.

McElhinney, C.P., McDonald, J.B., Castro, A.[Albertina], Frauel, Y.[Yann], Javidi, B.[Bahram], Naughton, T.J.,
Segmentation of three-dimensional objects from background in digital holograms,

Nakib, A., Oulhadj, H., Siarry, P.[Patrick],
Non-supervised image segmentation based on multiobjective optimization,
PRL(29), No. 1, 15 January 2008, pp. 161-172.
WWW Link. 0711
Image segmentation; Otsu method; Gaussian curve fitting; Multiobjective optimization; Simulated annealing See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Nakib, A., Oulhadj, H., Siarry, P.,
A thresholding method based on two-dimensional fractional differentiation,
IVC(27), No. 9, 3 August 2009, pp. 1343-1357.
Elsevier DOI 0906
Two-dimensional fractional differentiation; Image thresholding; Image segmentation BibRef

Nakib, A., Schulze, Y., Petit, E.,
Image thresholding framework based on two-dimensional digital fractional integration and Legendre moments',
IET-IPR(6), No. 6, 2012, pp. 717-727.
DOI Link 1210

Hammouche, K.[Kamal], Diaf, M.[Moussa], Siarry, P.[Patrick],
A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation,
CVIU(109), No. 2, February 2008, pp. 163-175.
WWW Link. 0711
Thresholding; Image segmentation; Genetic algorithm BibRef

Losson, O.[Olivier], Botte-lecocq, C.[Claudine], Macaire, L.[Ludovic],
Fuzzy Mode Enhancement and Detection for Color Image Segmentation,
JIVP(2008), No. 2008, pp. xx-yy.
DOI Link 0804

Ni, K.Y.[Kang-Yu], Bresson, X.[Xavier], Chan, T.[Tony], Esedoglu, S.[Selim],
Local Histogram Based Segmentation Using the Wasserstein Distance,
IJCV(84), No. 1, August 2009, pp. xx-yy.
Springer DOI 0905
Earlier: A3, A4, A1, Only:
Histogram Based Segmentation Using Wasserstein Distances,
Springer DOI 0705

Vieira Lopes, N., Mogadouro do Couto, P.A., Bustince, H., Melo-Pinto, P.,
Automatic Histogram Threshold Using Fuzzy Measures,
IP(19), No. 1, January 2010, pp. 199-204.

Wang, N.[Na], Li, X.[Xia], Chen, X.H.[Xiao-Hong],
Fast three-dimensional Otsu thresholding with shuffled frog-leaping algorithm,
PRL(31), No. 13, 1 October 2010, pp. 1809-1815.
Elsevier DOI 1003
See also Threshold Selection Method from Grey-Level Histograms, A. Image segmentation; 3-D Otsu thresholding; Shuffled frog-leaping algorithm; Optimization BibRef

Krstinic, D., Skelin, A.K., Slapnicar, I.,
Fast two-step histogram-based image segmentation,
IET-IPR(5), No. 1, February 2011, pp. 63-72.
DOI Link 1103

Yang, W.[Wenjia], Dou, L.[Lihua], Zhan, J.[Juan],
A Multi-histogram Clustering Approach Toward Markov Random Field For Foreground Segmentation,
IJIG(11), No. 1, January 2011, pp. 65-81.
DOI Link 1103

Vazquez, E.[Eduard], Baldrich, R.[Ramon], van de Weijer, J.[Joost], Vanrell, M.[Maria],
Describing Reflectances for Color Segmentation Robust to Shadows, Highlights, and Textures,
PAMI(33), No. 1, January 2011, pp. 917-930.
Segment single material even with variations from shape, etc. Multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. BibRef

Khan, F.S.[Fahad Shahbaz], van de Weijer, J.[Joost], Vanrell, M.[Maria],
Modulating Shape Features by Color Attention for Object Recognition,
IJCV(98), No. 1, May 2012, pp. 49-64.
WWW Link. 1204
Top-down color attention for object recognition,

Khan, F.S.[Fahad Shahbaz], Anwer, R.M.[Rao Muhammad], van de Weijer, J.[Joost], Bagdanov, A.D.[Andrew D.], Vanrell, M.[Maria], Lopez, A.M.[Antonio M.],
Color attributes for object detection,
See also Learning Color Names for Real-World Applications. BibRef

Khan, F.S.[Fahad Shahbaz], van de Weijer, J.[Joost],
Evaluating the Impact of Color on Texture Recognition,
Springer DOI 1308

van de Weijer, J.[Joost], Khan, F.S.[Fahad Shahbaz],
Fusing Color and Shape for Bag-of-Words Based Object Recognition,
Springer DOI 1304

Rojas Vigo, D.A.[David Augusto], Khan, F.S.[Fahad Shahbaz], van de Weijer, J.[Joost], Gevers, T.[Theo],
The Impact of Color on Bag-of-Words Based Object Recognition,

Vazquez, E.[Eduard], Baldrich, R.[Ramon], Vazquez, J.[Javier], Vanrell, M.[Maria],
Topological Histogram Reduction Towards Colour Segmentation,
IbPRIA07(I: 55-62).
Springer DOI 0706

Xue, J.H.[Jing-Hao], Titterington, D.M.[D. Michael],
Median-based image thresholding,
IVC(29), No. 9, August 2011, pp. 631-637.
Elsevier DOI 1109
Image segmentation; Image thresholding; Laplace distributions; Mean absolute deviation from the median (MAD); Minimum error thresholding (MET); Otsu's method See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Fan, J.L.[Jiu-Lun], Lei, B.[Bo],
A modified valley-emphasis method for automatic thresholding,
PRL(33), No. 6, 15 April 2012, pp. 703-708.
Elsevier DOI 1203
Image segmentation; Otsu method; Valley point; Valley-emphasis method BibRef

Chen, Q., Zhao, L., Lu, J., Kuang, G., Wang, N., Jiang, Y.,
Modified two-dimensional Otsu image segmentation algorithm and fast realisation,
IET-IPR(6), No. 4, 2012, pp. 426-433.
DOI Link 1205
See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Zhang, H.L.[Hai-Li], Chen, Y.M.[Yun-Mei], Shi, J.L.[Jiang-Li],
Nonparametric Image Segmentation Using Rényi's Statistical Dependence Measure,
JMIV(44), No. 3, November 2012, pp. 330-340.
WWW Link. 1209

Lu, S.J.[Shi-Jian], Tan, C.[Cheston], Lim, J.H.[Joo-Hwee],
Robust and Efficient Saliency Modeling from Image Co-Occurrence Histograms,
PAMI(36), No. 1, 2014, pp. 195-201.
Earlier: A1, A3, Only:
Saliency Modeling from Image Histograms,
ECCV12(VII: 321-332).
Springer DOI 1210
Computational modeling BibRef

Boulmerka, A.[Aďssa], Allili, M.S.[Mohand Saďd], Ait-Aoudia, S.[Samy],
A generalized multiclass histogram thresholding approach based on mixture modelling,
PR(47), No. 3, 2014, pp. 1330-1348.
Elsevier DOI 1312
Image segmentation BibRef

Sarkar, S., Das, S.,
Multilevel Image Thresholding Based on 2D Histogram and Maximum Tsallis Entropy: A Differential Evolution Approach,
IP(22), No. 12, 2013, pp. 4788-4797.
evolutionary computation BibRef

Mukherjee, S., Acton, S.T.[Scott T.],
Region Based Segmentation in Presence of Intensity Inhomogeneity Using Legendre Polynomials,
SPLetters(22), No. 3, March 2015, pp. 298-302.
Computational modeling BibRef

Balarini, J.P.[Juan Pablo], Nesmachnow, S.[Sergio],
A C++ Implementation of Otsu's Image Segmentation Method,
IPOL(6), 2016, pp. 155-164.
DOI Link 1608
Code, Segmentation. Code, Otsu Segmentation. Code, Segmentation, C++. See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Shaus, A.[Arie], Turkel, E.[Eli],
Chan-Vese Revisited: Relation to Otsu's Method and a Parameter-Free Non-PDE Solution via Morphological Framework,
ISVC16(I: 203-212).
Springer DOI 1701

Kiadtikornthaweeyot, W.[Warinthorn], Tatnall, A.R.L.[Adrian R. L.],
Region Of Interest Detection Based On Histogram Segmentation For Satellite Image,
ISPRS16(B7: 249-255).
DOI Link 1610

Cavallaro, G.[Gabriele], Falco, N.[Nicola], Mura, M.D.[Mauro Dalla], Bruzzone, L.[Lorenzo], Benediktsson, J.A.[Jón Atli],
Automatic Threshold Selection for Profiles of Attribute Filters Based on Granulometric Characteristic Functions,
Springer DOI 1506

Chevallier, E.[Emmanuel], Chevallier, A.[Augustin], Angulo, J.[Jesus],
Computing Histogram of Tensor Images Using Orthogonal Series Density Estimation and Riemannian Metrics,
Density measurement BibRef

Al Saeed, D.H.[Duaa H.], Bouridane, A.[Ahmed], El Zaart, A.[Ali],
A new image segmentation method based On 3-dimensional entropic thresholding using a 3-dimensional (GLLALE) histogram,
WSSIP14(235-238) 1406
Face BibRef

Martín-Rodríguez, F.[Fernando],
New Tools for Gray Level Histogram Analysis, Applications in Segmentation,
Springer DOI 1307

Guo, X.[Xin], Zhao, Z.C.[Zhi-Cheng], Cai, A.[Anni],
Find dominant bins of a histogram by sparse representation,
WWW Link. 1302

Berger, R.[Raoul], Dubuisson, S.[Severine], Gonzales, C.[Christophe],
Fast multiple histogram computation using Kruskal's algorithm,
multiple overlapping regions. BibRef

Dubuisson, S.[Séverine], Gonzales, C.[Christophe],
Min-Space Integral Histogram,
ECCV12(II: 188-201).
Springer DOI 1210
compute histograms BibRef

Catańo, M.A.[Miguel Angel], Climent, J.[Joan],
A New Morphological Measure of Histogram Bimodality,
Springer DOI 1209

Nazareth, V.M., Amulya, K., Manikantan, K.,
Optimal Multilevel Thresholding for Image Segmentation Using Contrast-Limited Adaptive Histogram Equalization and Enhanced Convergence Particle Swarm Optimization,

Ma, L.Y.[Li-Yan], Yu, J.[Jian],
Texture segmentation based on local feature histograms,

Sthitpattanapongsa, P.[Puthipong], Srinark, T.[Thitiwan],
An Equivalent 3D Otsu's Thresholding Method,
PSIVT11(I: 358-369).
Springer DOI 1111
See also Threshold Selection Method from Grey-Level Histograms, A. BibRef

Kulkarni, M.[Mandar],
Histogram-based foreground object extraction for indoor and outdoor scenes,
DOI Link 1111

Ferone, A.[Alessio], Pal, S.K.[Sankar Kumar], Petrosino, A.[Alfredo],
A Rough-Fuzzy HSV Color Histogram for Image Segmentation,
CIAP11(I: 29-37).
Springer DOI 1109

Xue, F.[Fei], Zhang, Y.J.[Yu-Jin],
Image Class Segmentation via Conditional Random Field over Weighted Histogram Classifier,

Ramella, G.[Giuliana], di Baja, G.S.[Gabriella Sanniti],
Color Histogram-Based Image Segmentation,
CAIP11(I: 76-83).
Springer DOI 1109
Multiresolution Histogram Analysis for Color Reduction,
Springer DOI 1011
Color Quantization by Multiresolution Analysis,
Springer DOI 0909

Bellens, P.[Pieter], Palaniappan, K.[Kannappan], Badia, R.M.[Rosa M.], Seetharaman, G.[Guna], Labarta, J.[Jesus],
Parallel Implementation of the Integral Histogram,
Springer DOI 1108

Frias-Velazquez, A.[Andres], Morros, R.[Ramon],
Histogram computation based on image bitwise decomposition,
The actual computation of histograms. BibRef

Lin, A.Y.[Ai-Ying], Wu, L.L.[Li-Li], Zheng, B.Z.[Bao-Zhou], Zan, H.Y.[Hong-Ying],
The Combination of Local Fuzzy-Entropy-Based Transition Region Extraction with Otsu Thresholding Method for Image Segmentation,

Deng, H.G.[Hong-Gui], Wu, R.L.[Rang-Liang], Lai, Z.R.[Zheng-Rong],
Image Segmentation of Drosophila's Compound Eyes via Two-Dimensional Otsu Thresholding on the Basis of AGA,

Thomas, G.[Gabriel],
Image segmentation using histogram specification,

Brancati, N.[Nadia], Frucci, M.[Maria], Sanniti di Baja, G.[Gabriella],
Image Segmentation Via Iterative Histogram Thresholding and Morphological Features Analysis,
Springer DOI 0806

Pardo, A.[Alvaro],
Pixel-Wise Histograms for Visual Segment Description and Applications,
Springer DOI 0611

Schroff, F., Criminisi, A., Zisserman, A.,
Object Class Segmentation using Random Forests,
PDF File. 0809
Single-Histogram Class Models for Image Segmentation,
Springer DOI 0612

Martinez-de Dios, J.R., Ollero, A.,
A Multiresolution Threshold Selection Method Based on Training,
ICIAR04(I: 90-97).
WWW Link. 0409

Sankowski, D.[Dominik], Mosorov, V.[Volodymyr],
Thresholding Image Segmentation Based on the Volume Analysis of Spatial Regions,
Springer DOI 0210

Gibson, S.E., Harvey, R.W.,
Analysing and simplifying histograms using scale-trees,
IEEE Top Reference. 0210

Ińesta, J.M.[José M.], Sanz, P.J.[Pedro J.], del Pobil, Á.P.[Ángel P.],
An automatic transformation from bimodal to pseudo-binary images,
CIAP97(I: 231-238).
Springer DOI 9709

Mlsna, P.A., Zhang, Q., Rodriguez, J.J.,
3-D Histogram Modification of Color Images,
ICIP96(III: 1015-1018).
IEEE DOI BibRef 9600

Mlsna, P.A., Rodriguez, J.J.,
Explosion of multidimensional image histograms,
ICIP94(III: 958-962).

Dingle, A.A.[Alison A.], Morrison, M.W.[Mark W.],
Unsupervised Image Segmentation Based on the Comparison of Local and Regional Histograms,
ICIP96(III: 959-962).
IEEE DOI BibRef 9600

Luchowski, L.[Leszek],
A new method to threshold images of flat binary scenes under uneven lighting,
Springer DOI 9509

Vossepoel, A.M., Stoel, B.C., Meershoek, A.P.,
Adaptive Histogram Equalization Using Variable Regions,
ICPR88(I: 351-353).
IEEE DOI BibRef 8800

Bracho, R.[Rafael], and Sanderson, A.C.,
Segmentation of Images Based on Intensity Gradient Information,
CVPR85(341-347). Schlumberger Palo Alto Research and CMU. Shaded regions are extracted along with an indication of the surface which could generate them. BibRef 8500

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

Last update:Mar 13, 2017 at 16:25:24