Control Systems and Computers, N2, 2018, Article 2
DOI: https://doi.org/10.15407/usim.2018.02.012
Upr. sist. maš., 2018, Issue 2 (274), pp. 12-18.
UDK 004.02
VLADIMIR KALMYKOV, PhD (Eng.), Senior research fellow, E-mail: vl.kalmykov@gmail.com,
ANTON SHARYPANOV, Research fellow, E-mail: _sha_@ukr.net, Institute of Mathematical Machines and Systems Problems of the Ukraine National Academy of Science (IMMSP NASU), prosp. acad. Glushkova 42, 03680, Kiev 187, Ukraine
Segmentation of the Experimental Curves
as the Implementations of Unknown Piecewise
Smooth Functions
While processing (e.g. spline approximation) of experimental curves that supposed to be implementations of piecewise smooth functions distorted by noise, the task of determining the boundary points of the pieces arises. A suitable resolution for examining each curve is unknown. Construction of partial answers at a number of increasing resolutions is proposed. Each partial answer contains information about specific points found at a given resolution. The general answer is a subset of maximum cardinality of sequential and not conflicting partial answers. The results of experiments on segmentation of curves based on proposed method are discussed.
Download full text! (On English).
Keywords: experimental curves, segmentation, coarse-to-fine.
REFERENCES
-
1. VISHNEVSKEY, V., KALMYKOV, V., ROMANENKO, T., 2008. “Approximation of experimental data by Bezier curves”. Information theories & applications, 15 (3), pp. 235.
2. Romanenko, T., Vishnevskey, V., Kalmykov, V., 2013. “Analytical Representation of Graphs by Means of Parametrically Defined Splines”. Proc. of the Int. Conf. on applications of information and communication technology and statistics in economy and education ICAICTSEE, 2013 Dec. 6–7th, UNVE, Sofia, Bulgaria, pp. 536–542.
3. Canny, J.F., 1989. “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Machine Intell., PAMI-8, 6, pp. 679–698.
https://doi.org/10.1109/TPAMI.1986.4767851
4. SHARYPANOV, A., ANTONIOUK, A., KALMYKOV, V., 2014. “Joint study of visual perception mechanism and computer vision systems that use coarse-to-fine approach for data processing”. Information content & processing”, 1 (3), pp. 287–300.
5. Forster, B., Van De Ville, D., Berent, J., Sage, D., Unser, M., 2004. “Complex Wavelets for Extended Depth-of-Field: A New Method for the Fusion of Multichannel Microscopy Images,” Microsc. Res. Tech., 65(1–2), pp. 33–42. https://doi.org/10.1002/jemt.20092
6. Pedersoli, M., Vedaldi, A., Gonzalez, J., Roca, X., 2011. A Coarse-to-fine approach for fast deformable object detection. Pattern Recognition. 48 (5), pp. 1844-1853. https://doi.org/10.1016/j.patcog.2014.11.006
7. TYSHCHENKO, M.A., 2012. 3D reconstruction of human face in person identification problems. PhD thesis. International Research and Training Center for Information Technologies and Systems, Kyiv.
8. HUBEL, DAVID H., 1988. Eye, brain, and vision. New York : Scientific American Library : Distributed by W.H. Freeman, 240 p.
9. Podvigin, N.F., 1979. Dinamicheskie svoystva neyronnyh struktur zritel’noi sistemy. Leningrad: Nauka, 158 p.
10. Ruksenas, O., Bulatov, A., Heggelund, P.,2007. “Dynamics of Spatial Resolution of Single Units in the Lateral Geniculate Nucleus of Cat During Brief Visual Stimulation”. Neurophysiol 97, pp. 1445–1456.
https://doi.org/10.1152/jn.01338.2005
