Parameters configuration for interest points in images obtained by Drone

  • Rodrigo A. Rebouças INPE
  • Elcio H. Shiguemori IEAv-DCTA
  • Lamartine N. F. Guimarães IEAv-DCTA


Drone use has grown with the use of image processing and computer vision techniques, such as autonomous image navigation, mosaic generation, elevation modeling, 3D reconstruction, and object recognition. In all techniques, an important step is an extraction of features, such as methods of interest points. This work addresses the modes of application of interest points, such as BRISK, ORB, FREAK, AKAZE and LATCH with the parameters configured automatically using the optimization method for images with different textures. This process is one of the pieces of final software that selects the use of a meta heuristic the best parameters automatically according to an input image.


Abade, A., de Campos, M. D., Porto, L. F., de Farias Coelho, Y., de Moura Sousa, Y., and Nespolo, J. P. (2016). A construção otimizada de um drone para aplicações na agricultura e pecuária de precis˜ao. Anais da Escola Regional de Informática da Sociedade Brasileira de Computação (SBC)–Regional de Mato Grosso, 7.

Alahi, A., Ortiz, R., and Vandergheynst, P. (2012). Freak: Fast retina keypoint. In Computer vision and pattern recognition (CVPR), 2012 IEEE conference on, pages 510–517. Ieee.

Alcantarilla, P. F., Nuevo, J., and Bartoli, A. (2013). Fast explicit diffusion for accelerated features in nonlinear scale spaces. In British Machine Vision Conf. (BMVC).

Bradski, G. and Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. ”O’Reilly Media, Inc.”.

Bures, L. and Müller, L. (2016). Selecting keypoint detector and descriptor combination for augmented reality application. In International Conference on Speech and Computer, pages 604–612. Springer.

Cowan, B., Imanberdiyev, N., Fu, C., Dong, Y., and Kayacan, E. (2016). A performance evaluation of detectors and descriptors for uav visual tracking. In ICARCV, pages 1–6.

Domiciano, M. A. P., Shiguemori, E. H., Dias, L. A. V., and da Cunha, A. M. (2018). Particle collision algorithm applied to automatic estimation of digital elevation model from images captured by uav. IEEE Geoscience and Remote Sensing Letters, (99).

Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381–395.

Kanellakis, C. and Nikolakopoulos, G. (2017). Survey on computer vision for uavs: Current developments and trends. Journal of Intelligent&Robotic Systems, 87(1):141–168.

Leutenegger, S., Chli, M., and Siegwart, R. Y. (2011). Brisk: Binary robust invariant scalable keypoints. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2548–2555. IEEE.

Levi, G. and Hassner, T. (2016). Latch: Learned arrangements of three patch codes. IEEE.

Li, Y., Wang, S., Tian, Q., and Ding, X. (2015). A survey of recent advances in visual feature detection. Neurocomputing, 149:736–751.

Nijim, M. (2016). Multitasking intelligent surveillance and first response system. In Technologies for Homeland Security (HST), 2016 IEEE Symposium on, pages 1–6, Kingsville, USA. IEEE.

Otake, V. S. (2017). Produtos cartográficos gerados a partir de drones e aplicações na agricultura.

Pusztai, Z. and Hajder, L. (2016). Quantitative comparison of feature matchers implemented in opencv.

Radovic, M., Adarkwa, O., and Wang, Q. (2017). Object recognition in aerial images using convolutional neural networks. Journal of Imaging, 3(2):21.

Roberto, L. (2017). Acurácia do posicionamento e da orientação espacial de veículos aéreos a partir de imagens de câmeras de pequeno formato embarcadas.

Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011). Orb: An efficient alternative to sift or surf. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2564–2571. IEEE.

Satnik, A., Hudec, R., Kamencay, P., Hlubik, J., and Benco, M. (2016). A comparison of key-point descriptors for the stereo matching algorithm. In Radioelektronika (RADIOELEKTRONIKA), 2016 26th International Conference, pages 292–295. IEEE.

Shang, Z. and Shen, Z. (2017). Real-time 3d reconstruction on construction site using visual slam and uav. arXiv preprint arXiv:1712.07122.

Wu, S., Oerlemans, A., Bakker, E. M., and Lew, M. S. (2017). A comprehensive evaluation of local detectors and descriptors. Signal Processing: Image Communication, 59:150–167.

Yang, X. and Cheng, K.-T. (2012). Ldb: An ultra-fast feature for scalable augmented reality on mobile devices. In 2012 IEEE international symposium on mixed and augmented reality (ISMAR), pages 49–57. IEEE.

Zhang, Y., Atkinson, P. M., Li, X., Ling, F.,Wang, Q., and Du, Y. (2017). Learning-based spatial–temporal superresolution mapping of forest cover with modis images. IEEE Transactions on Geoscience and Remote Sensing, 55(1):600–614.

Zhen, Y., Sun, Z., Li, J., and Peng, Y. (2016). An airborne remote sensing image mosaic algorithm based on feature points. In Instrumentation & Measurement, Computer, Communication and Control (IMCCC), 2016 Sixth International Conference on, pages 202–205. IEEE.

REBOUÇAS, Rodrigo A.; SHIGUEMORI, Elcio H.; GUIMARÃES, Lamartine N. F.. Parameters configuration for interest points in images obtained by Drone. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 299-307. ISSN 2763-9061. DOI: