Specific shape building detection from aerial imagery in infrared range
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 6 (2017) no. 3, pp. 84-100 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

This paper describes an approach to detection of specific shape buildings from the aerial imagery in the infrared range. The proposed algorithm uses contour analysis and is based on a modification of the generalizedHough transform that allows to detect curves defined by a small number of parameters. The main idea is to build atwo-dimensional accumulator array for each possible parameter set of the specified curve, and to combine obtainedarrays into the resultant accumulator array whose local maxima correspond to the positions of the sought objects.The gradient magnitudes of the original image are used to fill the arrays. The closeness of the found contoursto the predefined curve is determined by the morphological analysis of the values calculated by the Canny edgedetector. Filtering detected objects relies on the density of boundaries in their internal area with the ratio of theaverage intensity inside and outside the contour that provides high sensitivity to the specified types of objectsand reduces the number of false alarms of the algorithm. The proposed approach was tested on the problem oflocalization of rectangular buildings and showed the appropriate quality for practical use.
Keywords: image processing, object detection, mathematical morphology.
Mots-clés : contour analysis
@article{VYURV_2017_6_3_a5,
     author = {A. V. Dunaeva and F. A. Kornilov},
     title = {Specific shape building detection from aerial imagery in infrared range},
     journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a Vy\v{c}islitelʹna\^a matematika i informatika},
     pages = {84--100},
     year = {2017},
     volume = {6},
     number = {3},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VYURV_2017_6_3_a5/}
}
TY  - JOUR
AU  - A. V. Dunaeva
AU  - F. A. Kornilov
TI  - Specific shape building detection from aerial imagery in infrared range
JO  - Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika
PY  - 2017
SP  - 84
EP  - 100
VL  - 6
IS  - 3
UR  - http://geodesic.mathdoc.fr/item/VYURV_2017_6_3_a5/
LA  - ru
ID  - VYURV_2017_6_3_a5
ER  - 
%0 Journal Article
%A A. V. Dunaeva
%A F. A. Kornilov
%T Specific shape building detection from aerial imagery in infrared range
%J Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika
%D 2017
%P 84-100
%V 6
%N 3
%U http://geodesic.mathdoc.fr/item/VYURV_2017_6_3_a5/
%G ru
%F VYURV_2017_6_3_a5
A. V. Dunaeva; F. A. Kornilov. Specific shape building detection from aerial imagery in infrared range. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 6 (2017) no. 3, pp. 84-100. http://geodesic.mathdoc.fr/item/VYURV_2017_6_3_a5/

[1] R. C. Gonzalez, R. E. Woods, Digital Image Processing, 2-nd Edition, 2001, 1072 pp.

[2] A. A. Sirota, A. I. Solomatin, “Statistical and Neural Network Algorithms of Allocation of Objects Border in Images”, Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies, 2008, no. 1, 58–64

[3] Y. V. Vizilter, S. Y. Zheltov, A. V. Bondarenko, M. V. Ososkov, A. V. Morzhin, Image Processing and Analysis in Computer Vision: A Course of Lectures and Practical Exercises, Publishing Fizmatkniga, Moscow, 2010, 672 pp.

[4] A. N. Leukhin, Multidimensional Hypercomplex Contour Analysis and Its Applications to Image and Signal Processing. Thesis for the Degree of Doctor of of Physico-Mathematical Sciences, Publishing Mari State Technical University, Yoshkar-Ola, 2004, 389 pp.

[5] Y. A. Furman, Introduction to Contour Analysis, 2-nd Edition, Publishing FIZMATLIT, Moscow, 2003, 592 pp.

[6] P.V . C. Hough, Methods, Means for Recognizing Complex Patterns, U.S., Patent 3069654, 1962

[7] M. Heikkila, M. Pietikainen, “A Texture-Based Method for Modeling the Background and Detecting Moving Objects”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28:4 (2006), 657–662 | DOI

[8] C. Tomasi, R. Manduchi, “Bilateral Filtering for Gray and Color Images”, IEEE Proceedings of the 6-th International Conference on Computer Vision (Bombay, India 7-th Jan), 1998, 839–846 | DOI

[9] K. He, J. Sun, X. Tang, “Guided Image Filtering”, IEEE Transactions on Software Engineering, 35(6) (2013), 1397–1409 | DOI

[10] R. Duda, P. Hart, Pattern Classification and Scene Analysis, John Wiley and Sons, 1973 | DOI

[11] J. Canny, “A Computational Approach to Edge Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8:6 (1986), 679–698 | DOI

[12] L. Xu, E. Oja, “Randomized Hough Transform (RHT): Basic Mechanisms, Algorithms, and Computational Complexities”, CVGIP: Image Understanding, 57:2 (1993), 131–154 | DOI

[13] C. Harris, M. Stephens A Combined Corner and Edge Detector, Proceedings of the 4-th Alvey Vision Conference (University of Manchester. 31-st Aug. — 2-nd Sept), 1988, 147–151 | DOI

[14] E. Rosten, R. Porter, T. Drummond, “Faster and Better: A Machine Learning Approach to Corner Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32:1 (2010), 105–119 | DOI

[15] S. Noronha, R. Nevatia, “Detection and Modeling of Buildings from Multiple Aerial Images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:5 (2001), 501–518 | DOI

[16] F. A. Kornilov, “Using the Hough Transform to Detect Rectangular Contours on Images”, Topical problems of mathematics and its applications: Proceedings of International (45-th National) Youth School-Conference (Yekaterinburg, 2–8 fevralya 2014), Publishing IMM UB RAS, Yekaterinburg, 2014, 195–198

[17] Vegetation indices

[18] A. V. Boreskov et. al., Parallel Computing on GPU. Architectural and Software Model CUDA, Supercomputer education, Publishing Lomonosov Moscow State University, Moscow, 2012, 336 pp.

[19] The library of computer vision, image processing and computational mathematics algorithms with opened source code