8.3.6 Two-Dimensional Histogram Analysis for Segmentation

Chapter Contents (Back)
Segmentation, Histogram. Segmentation, Thresholds. Two Dimensional Histograms. Segmentation, 2-D Histogram.

Schachter, B.J.[Bruce J.], Davis, L.S.[Larry S.], Rosenfeld, A.[Azriel],
Some Experiments in Image Segmentation by Clustering of Local Feature Values,
PR(11), No. 1, 1979, pp. 19-28.
Elsevier DOI BibRef 7900
Scene Segmentation by Cluster Detection in Color Space,
UMD-CS TR-424, 1975. Segmentation, Histogram. Color. Histograms, Multi-Dimensional. Segmentation, Color. Multi-dimensional histograms to find color clusters. BibRef

Ahuja, N., Rosenfeld, A.,
A Note on the Use of Second-Order Gray Level Statistics for Threshold Selection,
SMC(8), 1978, pp. 895-898. BibRef 7800

Ahuja, N.[Narendra], Rosenfeld, A.[Azriel], Haralick, R.M.[Robert M.],
Neighbor Gray Levels as Features in Pixel Classification,
PR(12), No. 4, 1980, pp. 251-260.
Elsevier DOI BibRef 8000

Kirby, R., Rosenfeld, A.,
A Note on the Use of (Gray Level, Average Gray Level) Space as an Aid in Threshold Selection,
SMC(9), 1979, pp. 860-864. BibRef 7900

Kitchen, L.[Leslie], Pietikainen, M.[Matti], Rosenfeld, A.[Azriel], and Wang, C.Y.[Cheng-Ye],
Multispectral Image Smoothing Guided by Global Distribution of Pixel Values,
SMC(13), 1983, pp. 626-631. Basically the superspike algorithm extended to 2-D histograms (red-green). See also Image Segmentation by Texture Using Pyramid Node Linking. BibRef 8300

Narayanan, K.A., and Rosenfeld, A.,
Image Smoothing by Local Use of Global Information,
SMC(11), No. 12, December 1981, pp. 826-831. Smoothing. Each pixel is averaged with only those neighbors which are (1) more probable (higher value in histogram) and (2) in the same peak - no concavity between the two. Applied iteratively, this produces spikes where peaks formerly appeared. Superspike algorithm. BibRef 8112

Pietikainen, M.[Matti], and Rosenfeld, A.[Azriel],
Multispectral Image Smoothing by Local Use of Global Information,
UMDTR 1113, 1981. BibRef 8100

Westman, T., Harwood, D.A., Laitinen, T., and Pietikainen, M.,
Color Segmentation by Hierarchical Connected Components Analysis with Image Enhancements by Symmetric Neighborhood Filters,
ICPR90(I: 796-802).
IEEE DOI Use the images, but do not cite Ohlander. BibRef 9000

Moring, I.[Ilkka], and Pietikainen, M.[Matti],
Experiments with Histogram Guided Image Smoothing,
EE Dept., Univ. of OuluOulu Finland, 1982. Further work on the super spike smoothing algorithm. BibRef 8200

Wang, C.Y.[Cheng-Ye], and Kitchen, L.[Leslie],
Improvements in Multispectral Image Smoothing,
UMD-CS TR-1152, DAAG-53-76C-1038, March 1982. Extensions of the earlier 2-D histogram superspike method. BibRef 8203

Werman, M.[Michael], Peleg, S.[Shmuel], Rosenfeld, A.[Azriel], Werman, M., Peleg, S., and Rosenfeld, A.,
A Distance Metric for Multidimensional Histograms,
CVGIP(32), No. 3, December 1985, pp. 328-336.
Elsevier DOI BibRef 8512

Abutaleb, A.S.[Ahmed S],
Automatic Thresholding of Grey-Level Pictures Using Two-Dimensional Entropy,
CVGIP(47), No. 1, July 1989, pp. 22-32.
Elsevier DOI Threshold based on maximum entropy using the 2-D histogram of the image. BibRef 8907

Jain, A.K., Newman, T.S., Goulish, M.,
Range-Intensity Histogram for Segmenting LADAR Images,
PRL(13), 1992, pp. 41-56. See also Model-Based Classification of Quadric Surfaces. BibRef 9200

Li, L.Y.[Li-Yuan], Gong, R.[Ran], Chen, W.N.[Wei-Nan],
Gray-Level Image Thresholding Based on Fisher Linear Projection of 2-Dimensional Histogram,
PR(30), No. 5, May 1997, pp. 743-749.
Elsevier DOI 9705

Cheng, H.D., Chen, Y.H.[Yen-Hung],
Fuzzy partition of two-dimensional histogram and its application to thresholding,
PR(32), No. 5, May 1999, pp. 825-843.
Elsevier DOI BibRef 9905

Cheng, H.D.[Heng D.], Chen, Y.H.[Yen-Hung],
Thresholding Based on Fuzzy Partition of Two-Dimensional Histogram,
ICPR98(Vol II: 1616-1618).

Cheng, H.D., Chen, Y.H., Jiang, X.H.,
Thresholding Using Two-Dimensional Histogram and Fuzzy Entropy Principle,
IP(9), No. 4, April 2000, pp. 732-735.

Chang, J.H.[Jeng-Horng], Fan, K.C.[Kuo-Chin], Chang, Y.L.[Yang-Lang],
Multi-modal gray-level histogram modeling and decomposition,
IVC(20), No. 3, March 2002, pp. 203-216.
Elsevier DOI 0202

Clément, A., Vigouroux, B.,
Unsupervised segmentation of scenes containing vegetation (Forsythia) and soil by hierarchical analysis of bi-dimensional histograms,
PRL(24), No. 12, August 2003, pp. 1951-1957.
Elsevier DOI 0304

Wang, L.S.[Li-Sheng], Bai, J.[Jing],
Threshold selection by clustering gray levels of boundary,
PRL(24), No. 12, August 2003, pp. 1983-1999.
Elsevier DOI 0304
Consider threshold in continuous image space. BibRef

Doignon, C., Graebling, P., de Mathelin, M.,
Real-time segmentation of surgical instruments inside the abdominal cavity using a joint hue saturation color feature,
RealTimeImg(11), No. 5-6, October-December 2005, pp. 429-442.
Elsevier DOI 0510

Wang, F.C.[Feng-Chao],
An Improved 2-D Maximum Entropy Threshold Segmentation Method Based on PSO,

Xiao, Y.[Yang], Cao, Z.G.[Zhi-Guo], Zhang, T.X.[Tian-Xu],
Entropic thresholding based on gray-level spatial correlation histogram,

Müller, W.[Wolfgang], Henrich, A.[Andreas],
Faster Exact Histogram Intersection on Large Data Collections Using Inverted VA-Files,
Springer DOI 0505

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Histogram Analysis for Threshold Selection and Segmentation .

Last update:Feb 19, 2018 at 12:15:17