Abstract
A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods.
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References
K. S. Fu and J. K. Mui, “A survey on image segmentation,” Pattern Recognition, 13, No. 1, 3–16 (1981).
N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition, 26, No. 9, 1277–1294 (1993).
Y. J. Zhang, Advances in Image and Video Segmentation, IRM Press, London (2006).
S. Belongie, J. Malik, and J. Puzicha, “A survey on image segmentation,” IEEE Transaction on Pattern Analysis and Machine Intelligence, 24, No. 4, 509–522 (2002).
R. J. Kosarevych, M. I. Kobasyar, and B. P. Rusyn, “Multilevel thresholding by clustering the set of extrema of histograms of image fragments,” Information Extraction and Processing, Issue 34 (110), 113–119 (2011).
A. Jain, M. Murty, and P. Flynn, “Data clustering: A review,” ACM Computing Surveys, 31, No. 3, 264–323 (1999).
S. P. Lloyd, “Least squares quantization in PCM,” IEEE Transaction on Information Theory, 28, No. 2, 129–137 (1982).
P. Yang and S. Huang, “Image segmentation by fuzzy c-means clustering algorithm with a novel penalty term,” Computing and Informatics, 26, No. 1, 17–31 (2007).
S. Thilagamani and N. Shanthi, “A survey on image segmentation through clustering,” International Journal of Research and Reviews in Information Sciences, 1, No. 1, 129–137 (2011).
L. A. Waller, “Detection clustering in spatial data,” in: A. S. Fotheringham and P. A. Rogerson (eds.), The Sage Handbook of Spatial Analysis, Sage Publications Inc., Thousand Oaks, CA (2009), pp. 299–320.
J. M. Albert, M. R. Casanova, and V. Orts, “Spatial location patterns of Spanish manufacturing firms,” Papers in Regional Science, 91, No. 1, 107–136 (2012).
I. Alcobia, A. S. Quina, H. Neves, N. Clode, and L. Parreira, “The spatial organization of centromeric heterochromatin during normal human lymphopoiesis,” Experimental Cell Research, 290, No. 2, 358–369 (2003).
P. Haase, “Spatial pattern analysis in ecology based on Ripley’s K-function: Introduction and methods of edge correction,” Journal of Vegetation Science, 6, No. 4, 575–582 (1995).
N. Raghavan and P. K. Goel, “Modeling and characterizing microstructures using spatial point processes,” Statistical Computing & Statistical Graphics Newsletter, 8, No. 2/3, 10–16 (1997).
D. Stoyan and A. Penttinen, “Recent applications of point process methods in forestry statistics,” Statistical Science, 15, No. 1, 61–78 (2000).
K. Vasudevan, S. Eckel, F. Fleischer, V. Schmidt, and F. Cook, “Statistical analysis of spatial point patterns on deep seismic region data: A preliminary test,” Geophysical Journal International, 171, No. 2, 823–840 (2007).
B. Ripley, “Test of randomness for spatial point patterns,” Journal of the Royal Statistical Society, 41, No. 3, 368–374 (1979).
D. J. Gates and M. Westcott, “Clustering estimates for spatial point distributions with unstable potentials,” Annals of the Institute of Statistical Mathematics, 38, No. 1, 123–135 (1986).
B. Ripley, “The second–order analysis of stationary point processes,” Journal of Applied Probability, 13, No. 2, 255–266 (1976).
D. Stoyan and H. Stoyan, “Improving ratio estimators of second order point process characteristics,” Scandinavian Journal of Statistics, 27, No. 4, 641–656 (2000).
D. Stoyan and H. Stoyan, Fractals, Random Shapes, and Point Fields, Wiley, Chichester (1994).
J. Besag, “Discussion of ‘Modeling spatial patterns’ by B. D. Ripley,” Journal of the Royal Statistical Society, 39, No. 4, 192–225 (1977).
V. P. Boyun, “Intelligent selective perception of visual information: Informational aspects,” Artificial Intelligence, No. 3, 16–24 (2011).
S. Alpert, M. Galun, R. Basri, and A. Brandt, “Image segmentation by probabilistic bottom-up aggregation and cue integration,” in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’ 07) (2007), pp. 1–8.
L. Xu, “Robust peak detection of pulse waveform using height ratio,” in: Proc. 30th Annual Intern. Conf. of the IEEE Engineering in Medical and Biology Society, Vancouver, British Columbia, Canada (2008), pp. 3859–3865.
A. Nakib, H. Oulhadj, and P. Siarry, “Image histogram thresholding based on multiobjective optimization,” Signal Processing, 87, No. 11, 2516–2534 (2007).
A. L. Jacobson, “Auto-threshold peak detection in physiological signals,” in: Proc. 23th Annual Intern. Conf. of the IEEE Engineering in Medical and Biology Society, Istanbul, Turkey (2001), pp. 2194–2195.
M. Sezan, “A peak detection algorithm and its application to histogram–based image data reduction,” Computer Vision Graphics Image Processing, 49, No. 1, 36–51 (1990).
J. Canny, “A computational approach to edge-detection,” IEEE Transaction on Pattern Analysis and Machine Intelligence, 8, No. 6, 679–698 (1986).
N. Otsu, “A threshold selection using grey level histograms,” IEEE Transaction on System, Man, and Cybernetics, 9, No. 1, 62–69 (1979).
R. Adams and L. Bischof, “Seed region growing,” IEEE Transaction on Pattern Analysis and Machine Intelligence, 16, No. 6, 641–647 (1994).
Segmentation Evaluation Database, http://www.wisdom.weizmann.ac.il/~vision/Seg_Evaluation_DB/index.html.
W. Yasnoff, J. K. Mui, and J. W. Bacus, “Error measures for scene segmentation,” Pattern Recognition, 9, No. 4, 217–231 (1977).
M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, 13, No. 1, 146–165 (2004).
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Translated from Kibernetika i Sistemnyi Analiz, No. 5, September–October, 2015, pp. 45–55.
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Kosarevych, R.J., Rusyn, B.P., Korniy, V.V. et al. Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics. Cybern Syst Anal 51, 704–713 (2015). https://doi.org/10.1007/s10559-015-9762-5
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DOI: https://doi.org/10.1007/s10559-015-9762-5