THE CURVE ARC AS A STRUCTURE ELEMENT OF AN OBJECT CONTOUR IN THE IMAGE TO BE RECOGNIZED

Authors

  • V. G. Kalmykov Institute of Mathematical Machines and Systems Problems, Kyiv Ukraine, Ukraine
  • A. V. Sharypanov Institute of Mathematical Machines and Systems Problems, Kyiv Ukraine, Ukraine
  • V. V. Vishnevskey Institute of Mathematical Machines and Systems Problems, Kyiv Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2023-1-9

Keywords:

image, contour, curve arc, straight line segment, cellular complex, neurons, receptive field

Abstract

Context. The proposed article relates to the field of visual information processing in a computer environment, more precisely to the determination the parameters of the interest object in the image, in particular, the contour of the interest object In most cases, the contour of an object is a simply connected sequence of curve arcs.

Objective. The purpose and subject of the study is to find and to propose such a definition of the digital curve arc, as the most important element of the object contour in the recognizable image, which does not contradict modern neurophysiological conceptions about visual perception, and to recognize the object contour as a sequence of the digital curve arcs.

Method. The representation of the image in the form of a structural model is used, one of the structural elements of which is the contour of the object, consisting of digital curve arcs. Also, the image is considered as a cellular complex which corresponds to modern ideas about human visual perception.

Results. The new definition for arc of a digital curve as a sequence of digital straight segments is proposed, which does not contradict to modern concepts of neurophysiology. In contrast to the known definitions of a curve arc, the proposed definition of a digital curve arc makes it possible to determine the start and end points of the arc. According to the description of the contour of an object as a simply connected closed sequence of line segments, it is proposed to construct a description of the contour as a sequence of arcs of digital curves.

Conclusions. The use of the proposed definition of the digital curve arc in image processing makes it possible to recognize the contour of an object in an image and present it in a form close to visual perception. For best results, the use of variable resolution in image processing algorithms is recommended.

Author Biographies

V. G. Kalmykov, Institute of Mathematical Machines and Systems Problems, Kyiv Ukraine

PhD, Senior Reseacher

A. V. Sharypanov, Institute of Mathematical Machines and Systems Problems, Kyiv Ukraine

PhD, Senior Reseacher

V. V. Vishnevskey, Institute of Mathematical Machines and Systems Problems, Kyiv Ukraine

PhD, Leading Reseacher

References

Pavlidis T. Algorithms for Graphics and Image Processing. Berlin, Springer-Verlag, 1982, 400 p.

Gonzalez R. C., Woods R. E., Eddins S. L. Digital Image Processing using MATLAB. New York, Pearson Education, 2004, 616 p.

Pratt W. K. Digital Image Processing. New York, John Wiley & Sons, Inc, 1982, 738 p.

Schlesinger M., Hlavac V. Ten Lectures on Statistical and Structural Pattern Recognition. Dordrecht / Boston / London, Computational Imaging and Vision Kluwer Academic Publishers, 2002. 520 p.

Ivakhnenko A. G., Lapa V. G. Cybernetics and Forecasting Techniques. New York, American Elsevier Publishing Company, 1967, 168 p.

LeCun Y., Boser B., Denker J. S., Henderson D., Howard R. E., Hubbard W., Jackel L. D. Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, 1989, Vo1. 1, No. 4, pp. 541–551. doi:10.1162/neco.1989.1.4.541. S2CID 41312633.

Maitra D. S., Bhattacharya U., Parui S. K. CNN based common approach to handwritten character recognition of multiple scripts, 13th International Conference on Document Analysis and Recognition (ICDAR): 23-26 August 2015: proceedings. Tunis, IEEE 2015, pp. 1021–1025. doi:10.1109/ICDAR.2015.7333916. ISBN 978-1-47991805-8. S2CID 25739012.

Fu K. S. Syntactic Methods in Pattern Recognition. New York and London, Academic Press, 1974, 511 p.

Aleksandrov P. S. Combinatorial Topology. Rochester, Graylock Press, 1956, 656 p.

Kovalevsky V. Finite Topology as Applied to Image Analysis, Computer Vision, Graphics and Image Processing, 1989, Vol. 46, No. 2, pp. 141–161.

Hubel D. H. Eye, brain, and vision. New York, Scientific American Library, Distributed by W.H. Freeman, 1988, 240 p.

Berg G. O., Julian W., Mines R., Richman F. The constructive Jordan curve theorem, Rocky Mountain Journal of Mathematics, 1975, Vol. 5, № 2, pp. 225–236. DOI: 10.1216/RMJ-1975-5-2-225, ISSN 0035-7596, MR 0410701

Dovgoshey O., Martio O., Ryazanov V., Vuorinen M. The Cantor function, Expositiones Mathematicae. Elsevier BV, 2006, Vol. 24, № 1, pp. 1–37. DOI: 10.1016/j.exmath.2005.05.002. ISSN 0723-0869. MR 2195181

Alexandrov A. D., Reshetnyak Yu. G. General theory of irregular curves, Mathematics and its Applications (Soviet Series), 29. Kluwer, Academic Publishers Group, Dordrecht, 1989, 288 p. ISBN: 90-277-2811-9

Schlesinger M. I. Mathematical Tools of Picture Processing. Kyiv, Naukowa Dumka, 1989, 117 p.

Kovalevsky V. A. Applications of Digital Straight Segments to Economical Image Encoding, 7th International Workshop, DGCI’97. Montpellier, France, December 3–5 1997, proceedings, Springer 1997, pp. 51–62.

Downloads

Published

2023-02-26

How to Cite

Kalmykov, V. G., Sharypanov, A. V., & Vishnevskey, V. V. (2023). THE CURVE ARC AS A STRUCTURE ELEMENT OF AN OBJECT CONTOUR IN THE IMAGE TO BE RECOGNIZED. Radio Electronics, Computer Science, Control, (1), 89. https://doi.org/10.15588/1607-3274-2023-1-9

Issue

Section

Progressive information technologies