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Methods of detection and spatial localization of groups of point objects
Ipatov Yurii Arkad'evich

PhD in Technical Science

Associate Professor of the Department of Informatics at the Volga State University of Technology

424000, Russia, Mari El, Yoshkar-Ola, pl. Lenina, d. 3

philsilver@mail.ru
Krevetskii Aleksandr Vladimirovich

PhD in Technical Science

Professor, Department of Computer Science, Volga State University of Technology

424000, Rossiya, respublika Mariy El, g. Yoshkar-Ola, pl. Lenina, 3

krevetskyav@marstu.net
Abstract. Modern systems of computer vision use intelligent algorithms that solve a wide class of problems from simple text recognition to complex systems of spatial orientation. One of the main problems for developers of such systems is in selection of unique attributes which remain invariant to various kinds of transformations. The article presents a comparative analysis of methods of detection and spatial localization of groups of point objects. The reviewed methods are compared by the performance and efficiency at specified dimensions. As of today there are no universal approaches to determine of such attributes, and its’ selection depends on the context of the problem being solved and on the registered conditions of observation. Various kinds of descriptors such as points, lines, angles and geometric primitives can be selected as dominating attributes. The authors study  algorithms for detection of groups of point objects based on the minimum spanning tree (MST) and using a model of associated continuous image (ACI).
Keywords: computer vision, object recognition, speed of the algorithm, modeling of point objects, associated continuous image, minimum spanning tree, group of point objects, point objects, intelligent algorithms, localization of groups
DOI: 10.7256/2306-4196.2014.6.13642
Article was received: 11-11-2014

Review date: 12-11-2014

Publish date: 16-11-2014

This article written in Russian. You can find full text of article in Russian here.

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