N. Papamarkos
and B. Gatos, "A new approach for multilevel threshold selection",
Computer Vision, Graphics, and Image Processing - Graphical Models and Image
Processing, Vol. 56, No. 5, pp. 357-370, Sept. 1994
Cited by:
2010 (2)
1. Quweider, M.K. Color
image segmentation with an entropy-based cost function (2010) Proceedings -
2010 3rd International Congress on Image and Signal Processing, CISP 2010, 3,
art. no. 5648128, pp. 1358-1363.
2. Zhang, Y., Chen, X. Recognition for
multiple robots linear formation (2010) Proceedings of the World Congress on
Intelligent Control and Automation (WCICA), art. no.
5554677, pp. 6142-6146.
2009 (4)
1. Zou, R., Yu, W., Yu, Z., Yu, X. Image
segmentation based on local ant colony optimization (2009) 5th International
Conference on Natural Computation, ICNC 2009, 3, art. no.
5363299, pp. 35-39.
2. Son, C.S., Seo,
S.T., Lee, I.K., Jeong, H.C., Kwon, S.H. Threshold
selection based on interval-valued fuzzy sets (2009) IEICE Transactions on
Information and Systems, E92-D (9), pp. 1807-1810.
3. Liu, D., Yu, J.Otsu
method and K-means (2009) Proceedings - 2009 9th International Conference on
Hybrid Intelligent Systems, HIS 2009, 1, art. no.
5254345, pp. 344-349.
4. Lai, A.-N., Park, K., Kumar, M.,
Lee, G. Korean text extraction by local color quantization
and k-means clustering in natural scene (2009) Proceedings - 2009 1st Asian
Conference on Intelligent Information and Database Systems, ACIIDS 2009, art. no. 5175982, pp. 138-143.
2008 (4)
1. Lai, A.-N., Lee, G.-S. Binarization
by local K-means clustering for Korean text extraction (2008) Proceedings of
the 8th IEEE International Symposium on Signal Processing and Information
Technology, ISSPIT 2008, art. no. 4775658, pp.
117-122.
2. Jiang, X., Huang, X., Jie, M., Yin, H. Rock detection based on 2D maximum entropy
thresholding segmentation and ellipse fitting (2008)
2007 IEEE International Conference on Robotics and Biomimetics,
ROBIO, art. no. 4522325, pp. 1143-1147.
3. Arora, S., Acharya,
J., Verma, A., Panigrahi,
P.K. Multilevel thresholding for image segmentation
through a fast statistical recursive algorithm (2008) Pattern Recognition
Letters, 29 (2), pp. 119-125.
4. D Yu, TD Pham, H Yan, JS Jin, S Luo, DI Crane, Image processing and reconstruction of
cultured neuron skeletons, International Journal of Hybrid Intelligent Systems,
2008 - IOS Press, Volume 5 , Issue 4,
Pages 179-196.
2007 (5)
1. Yu, D., Pham, T.D., Yan, H., Lai,
W., Crane, D.I. Segmentation and reconstruction of cultured neuron skeleton
(2007) AIP Conference Proceedings, 952, pp. 21-30.
2. Yu, D., Pham, T.D., Crane, D.I. A
region analysis approach for segmenting neural-cell images (2007) Annual
International Conference of the IEEE Engineering in Medicine and Biology -
Proceedings, art. no. 4353598, pp. 5529-5532.
3. Yu, D., Pham, T.D., Yan, H., Zhang,
B., Crane, D.I. Segmentation of cultured neurons using logical analysis of grey
and distance difference (2007) Journal of Neuroscience Methods, 166 (1), pp.
125-137
4. Quweider, M.K., Scargle,
J.D., Jackson, B. Grey level reduction for segmentation, threshholding
and binarisation of images based on optimal
partitioning on an interval (2007) IET Image Processing, 1 (2), pp. 103-111.
5. Wang, L., Bai,
J., Wong, T.-T., Heng, P.-A. Isosurfaces
computation for approximating boundary surfaces within three-dimensional images
(2007) Journal of Electronic Imaging, 16 (1), art. no. 013011
2006 (2)
1. Adam Hoover, Li Yu: Segmentation
Methods through the Stability of Region Count in the Scale Space. IPCV 2006:
467-473
2. Boudraa, A. O.; Zaidi,
H. Quantitative Analysis in Nuclear Medicine Imaging (2006) :308-357, January
01, 2006
2005 (2)
1. Tong, C.S., Zhang, Y., Zheng, N. Variational image binarization and its multi-scale realizations (2005)
Journal of Mathematical Imaging and Vision, 23 (2), pp. 185-198.
2. Huang, Q., Gao, W., Cai, W. Thresholding technique with adaptive window selection for
uneven lighting image (2005) Pattern Recognition Letters, 26 (6), pp. 801-808.
2004 (5)
1. Guan, Y.-P., Gu, W.-K. A study of algorithm for automatically
extracting feature points in 2-dimensional image (2004) Chinese Journal of
Sensors and Actuators, 17 (1), pp. 70-73.
2. Bak, E., Najarian,
K. Robust segmentation using parametric and nonparametric local spatial
posteriors (2004) International Conference on Information Technology: Coding
Computing, ITCC, 1, pp. 626-630.
3. Sahoo, P.K., Arora,
G. A thresholding method based on two-dimensional Renyi's entropy (2004) Pattern Recognition, 37 (6), pp.
1149-1161.
4. Sezgin, M., Sankur,
B. Survey over image thresholding techniques and
quantitative performance evaluation (2004) Journal of Electronic Imaging, 13
(1), pp. 146-168.
5. Yu, L. and Hoover, A., Threshold
Selection as a Function of Region Count Stability Computer Vision and Pattern
Recognition Workshop, 2004, pp. 59-59
2003 (2)
1. Warne, K., Prasad, G., Siddique, N.H., Maguire, L.P. A novel intelligent approach
to anchorage measurement using electron microscopy (2003) Proceedings of the
IEEE International Conference on Systems, Man and Cybernetics, 4, pp.
3810-3815.
2. Wang, L., Bai,
J. Threshold selection by clustering gray levels of boundary (2003) Pattern
Recognition Letters, 24 (12), pp. 1983-1999.
2002 (5)
1. Shah-Hosseini,
H., Safabakhsh, R. Automatic multilevel thresholding for image segmentation by the growing time
adaptive self-organizing map (2002) IEEE Transactions on Pattern Analysis and
Machine Intelligence, 24 (10), pp. 1388-1393.
2. Chang, J.-H., Fan, K.-C., Chang,
Y.-L. Multi-modal gray-level histogram modeling and decomposition (2002) Image and Vision
Computing, 20 (3), pp. 203-216.
3. Kim, I.-K., Jung, D.-W., Park, R.-H.
Document image binarization based on topographic analysis using a water flow
model (2002) Pattern Recognition, 35 (1), pp. 265-277.
4. José Manuel Iñesta Quereda, Jorge
Calera-Rubio: Robust Gray-Level Histogram Gaussian Characterisation. SSPR/SPR
2002: 833-841
5. Seemann, T. Digital Image Processing using
Local Segmentation, School of Computer Science and Software Engineering Faculty
of Information Technology Monash University
Australia, PhD thesis, 2002
2001 (5)
1. Tang, M., Ma, S. General scheme of
region competition based on scale space (2001) IEEE Transactions on Pattern
Analysis and Machine Intelligence, 23 (12), pp. 1366-1378.
2. Zhao, M., Fu, A.M.N., Yan, H. A
technique of three-level thresholding based on
probability partition and fuzzy 3-partition (2001) IEEE Transactions on Fuzzy
Systems, 9 (3), pp. 469-479.
3. Zhang, Y. and Zheng,
N. and Zhao, R. Variation-based approach to image segmentation Science in China
Series F: Information Sciences Vol. 44 Num. 4, 2001, pp. 259-269
4. Tong, C.S. and Zhang, Y. and Zheng, N. Variation-Based Image Segmentation and its Multiscale Realizations International Conference on Inverse
Problems, 2001, pp. 9-12
5. J.H. Chang, “Fingerprint
Classification by Ridge Distribution Sequences and Ridge Distribution Model,”
Ph.D. Dissertation, Department of Computer Science and Information Engineering,
National Central University, 2001.
2000 (1)
1.
Yang,
Y., Yan, H. An adaptive logical method for binarization of degraded document images(2000) Pattern Recognition, 33 (5), pp. 787-807.
1998 (4)
1. Luo, D., Barker, J., McGrath, J.C.,
Daly, C.J. Iterative multilevel thresholding and
splitting for three-dimensional segmentation of live cell nuclei using laser
scanning confocal microscopy (1998) Journal of
Computer-Assisted Microscopy, 10 (4), pp. 151-162.
2. Cheng, H.D., Chen, C.H., Chiu, H.H.,
Xu, H. Fuzzy homogeneity approach to multilevel thresholding (1998) IEEE Transactions on Image Processing,
7 (7), pp. 1084-1088.
3. Cheng, H.D., Chen, J.-R., Li, J.
Threshold selection based on fuzzy c-partition entropy approach (1998) Pattern
Recognition, 31 (7), pp. 857-870.
4. Rout, S., Seethalakshmy,
P.S., Majumdar, J. Multi-modal image segmentation
using a modified hopfield neural network (1998)
Pattern Recognition, 31 (6), pp. 743-750.
1997 (1)
1.
Sahoo, P., Wilkins, C., Yeager, J. Threshold
selection using Renyi's entropy (1997) Pattern
Recognition, 30 (1), pp. 71-84.
1996 (1)
1.
Lui, Y.M., Cheng, H.-D. A new peak selection
criterion based on minimizing the classification error (1996) Information Sciences,
94 (1-4), pp. 213-233.