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Volume 32 #1

Contents

  1. Variability of different functional songs types from Aelia acuminata L. (Heteroptera, Pentatomidae)
  2. Receptive field sizes of sustained ganglion cells in the retina of Carassius gibelio
  3. Fusion of radar, visible and thermal imagery with account for differences in brightness and chromaticity perception
  4. Modelling of the unmanned aerial vehicle navigation on the basis of two height-shifted onboard cameras
  5. Visual localisation of aerial images on vector map using colour-texture segmentation
  6. Comparison of the digitized pages of business documents by means of recognition
  7. The algorithm of epipole position estimation under pure camera translation
  8. Segmentation of vehicle registration plates based on dynamic time warping
  9. Finding the correspondence between closed curves under projective distortions
  10. Blur kernel estimation with algebraic tomography technique and intensity profiles of object boundaries
  11. Weighted search for a projective optical flow resistant to specular reflections
  12. Algebraic methods for tomography problem
  13. Quantitative analysis of the point light source virtual image doubling in underwater stereo vision

Visual localisation of aerial images on vector map using colour-texture segmentation

© 2018 L. M. Teplyakov, I. A. Kunina, A. P. Gladkov

Institute for Information Transmission Problems RAS 127051 Moscow, Bolshoy Karetny per., 19

Received 21 Aug 2017

In this work we investigate the problem of matching images, captured by UAV, with the map of riparian territories with no use of sattelite navigation. The basis of our method is representation of the image by colourtexture segmentation as a bunch of segments. Then we are trying to recover a geometric transformation which matches segments and map. This transformation consists of arbitrary translation, but restricted rotation and scale. Calculation of transform parameters is based on RANSAC algorithm, which matches image segments with contours on map. For conducting this matching we use local shape descriptors, invariant to rotation and scaling.

Key words: aerial images, geolocalisation, colour segmentation, texture segmentation, contours matching, RANSAC

DOI: 10.7868/S0235009218010055

Cite: Teplyakov L. M., Kunina I. A., Gladkov A. P. Vizualnaya lokalizatsiya aerofotosnimkov na vektornoi karte s ispolzovaniem tsvetoteksturnoi segmentatsii [Visual localisation of aerial images on vector map using colour-texture segmentation]. Sensornye sistemy [Sensory systems]. 2018. V. 32(1). P. 26-34 (in Russian). doi: 10.7868/S0235009218010055

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