Análise de desempenho de detectores e descritores de características utilizando a Plataforma Computacional Raspberry Pi
Resumo
Este trabalho consiste em avaliar a precisão e o desempenho dos algoritmos detectores e descritores de características na plataforma computacional Raspberry Pi, dispondo de dados referentes a precisão, consumo de memória e tempo de execução durante os processos de detecção, descrição e correlação dos pontos de interesse. Os resultados apresentados tornam-se úteis durante o processo de planejamento de sistemas aplicados em plataformas dotadas de baixo desempenho computacional, uma vez que, a partir dos dados produzidos é possível estabelecer quais algoritmos detectores e descritores de características são indicados baseado nas particularidades de um sistema.
Referências
Agrawal, M., Konolige, K., and Blas, M. R. (2008). Censure: Center surround extremas for realtime feature detection and matching. In European Conference on Computer Vision, pages 102–115. Springer.
Ahmadabadian, A. H., Robson, S., Boehm, J., Shortis, M., Wenzel, K., and Fritsch, D. (2013). A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs. ISPRS Journal of Photogrammetry and Remote Sensing, 78:157–167.
Awad, A. I. and Hassaballah, M. (2016). Image feature detectors and descriptors: foundations and applications, volume 630. Springer.
Bay, H., Tuytelaars, T., and Van Gool, L. (2006). Surf: Speeded up robust features. In European conference on computer vision, pages 404–417. Springer.
Bombardelli, F. G. (2014). Localização de robôs móveis por aparência visual.
Fernanda, J. (2018). Sistema de navegação autônoma para plataforma robótica móvel com restrições não-holonômicas. B.S. thesis, Universidade Estudal de Londrina.
Kabbai, L., Abdellaoui, M., and Douik, A. (2013). Hybrid classifier using sift descriptor. In Control, Decision and Information Technologies (CoDIT), 2013 International Conference on, pages 388–392. IEEE.
Kabbai, L., Azaza, A., Abdellaoui, M., and Douik, A. (2015). Image matching based on lbp and sift descriptor. In Systems, Signals & Devices (SSD), 2015 12th International Multi-Conference on, pages 1–6. IEEE.
Karami, E., Prasad, S., and Shehata, M. (2017). Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. arXiv preprint ar- Xiv:1710.02726.
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.
Novais, J. P. (2016). Aplicação dos Algoritmos SIFT e SURF na Classificação de Sub- Imagens por Discriminação de Textura.
OpenCV (2014). Feature matching.
Rosten, E. and Drummond, T. (2006). Machine learning for high-speed corner detection. In European conference on computer vision, pages 430–443. Springer.
Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011a). Orb: An efficient alternative to sift or surf. In 2011 International Conference on Computer Vision, pages 2564–2571.
Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011b). ORB: An efficient alternative to SIFT or SURF. In Computer Vision (ICCV), 2011 IEEE international conference on, pages 2564–2571. IEEE.
Santana, B. A., Maia, R. d. S., Figuerola, W. B., and Souza, A. A. (2015). Análise de desempenho de algoritmos detectores de keypoints para um sistema de navegação visual de robôs baseados em smartphones.
Schmid, C., Mohr, R., and Bauckhage, C. (2000). Evaluation of interest point detectors. International Journal of computer vision, 37(2):151–172.
Schmidt, A., Kraft, M., Fularz, M., and Domagała, Z. (2013). Comparative assessment of point feature detectors in the context of robot navigation. Journal of Automation Mobile Robotics and Intelligent Systems, 7.
Schmidt, A., Kraft, M., and Kasi´nski, A. (2010a). An evaluation of image feature detectors and descriptors for robot navigation. In Bolc, L., Tadeusiewicz, R., Chmielewski, L. J., and Wojciechowski, K., editors, Computer Vision and Graphics, pages 251–259, Berlin, Heidelberg. Springer Berlin Heidelberg.
Schmidt, A., Kraft, M., and Kasi´nski, A. (2010b). An evaluation of image feature detectors and descriptors for robot navigation. In International Conference on Computer Vision and Graphics, pages 251–259. Springer.
Vandergheynst, P., Ortiz, R., and Alahi, A. (2012). Freak: Fast retina keypoint. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 510–517. Ieee.
Viswanathan, D. G. (2009). Features from accelerated segment test (fast).
Xu, J., Chang, H.-w., Yang, S., and Wang, M. (2012). Fast feature-based video stabilization without accumulative global motion estimation. IEEE Transactions on Consumer Electronics, 58(3).
Ahmadabadian, A. H., Robson, S., Boehm, J., Shortis, M., Wenzel, K., and Fritsch, D. (2013). A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs. ISPRS Journal of Photogrammetry and Remote Sensing, 78:157–167.
Awad, A. I. and Hassaballah, M. (2016). Image feature detectors and descriptors: foundations and applications, volume 630. Springer.
Bay, H., Tuytelaars, T., and Van Gool, L. (2006). Surf: Speeded up robust features. In European conference on computer vision, pages 404–417. Springer.
Bombardelli, F. G. (2014). Localização de robôs móveis por aparência visual.
Fernanda, J. (2018). Sistema de navegação autônoma para plataforma robótica móvel com restrições não-holonômicas. B.S. thesis, Universidade Estudal de Londrina.
Kabbai, L., Abdellaoui, M., and Douik, A. (2013). Hybrid classifier using sift descriptor. In Control, Decision and Information Technologies (CoDIT), 2013 International Conference on, pages 388–392. IEEE.
Kabbai, L., Azaza, A., Abdellaoui, M., and Douik, A. (2015). Image matching based on lbp and sift descriptor. In Systems, Signals & Devices (SSD), 2015 12th International Multi-Conference on, pages 1–6. IEEE.
Karami, E., Prasad, S., and Shehata, M. (2017). Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. arXiv preprint ar- Xiv:1710.02726.
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.
Novais, J. P. (2016). Aplicação dos Algoritmos SIFT e SURF na Classificação de Sub- Imagens por Discriminação de Textura.
OpenCV (2014). Feature matching.
Rosten, E. and Drummond, T. (2006). Machine learning for high-speed corner detection. In European conference on computer vision, pages 430–443. Springer.
Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011a). Orb: An efficient alternative to sift or surf. In 2011 International Conference on Computer Vision, pages 2564–2571.
Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011b). ORB: An efficient alternative to SIFT or SURF. In Computer Vision (ICCV), 2011 IEEE international conference on, pages 2564–2571. IEEE.
Santana, B. A., Maia, R. d. S., Figuerola, W. B., and Souza, A. A. (2015). Análise de desempenho de algoritmos detectores de keypoints para um sistema de navegação visual de robôs baseados em smartphones.
Schmid, C., Mohr, R., and Bauckhage, C. (2000). Evaluation of interest point detectors. International Journal of computer vision, 37(2):151–172.
Schmidt, A., Kraft, M., Fularz, M., and Domagała, Z. (2013). Comparative assessment of point feature detectors in the context of robot navigation. Journal of Automation Mobile Robotics and Intelligent Systems, 7.
Schmidt, A., Kraft, M., and Kasi´nski, A. (2010a). An evaluation of image feature detectors and descriptors for robot navigation. In Bolc, L., Tadeusiewicz, R., Chmielewski, L. J., and Wojciechowski, K., editors, Computer Vision and Graphics, pages 251–259, Berlin, Heidelberg. Springer Berlin Heidelberg.
Schmidt, A., Kraft, M., and Kasi´nski, A. (2010b). An evaluation of image feature detectors and descriptors for robot navigation. In International Conference on Computer Vision and Graphics, pages 251–259. Springer.
Vandergheynst, P., Ortiz, R., and Alahi, A. (2012). Freak: Fast retina keypoint. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 510–517. Ieee.
Viswanathan, D. G. (2009). Features from accelerated segment test (fast).
Xu, J., Chang, H.-w., Yang, S., and Wang, M. (2012). Fast feature-based video stabilization without accumulative global motion estimation. IEEE Transactions on Consumer Electronics, 58(3).
Publicado
14/12/2019
Como Citar
JESUS, Samuel; ROCHA, Walber; BITTENCOURT, João.
Análise de desempenho de detectores e descritores de características utilizando a Plataforma Computacional Raspberry Pi. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE) , 2019, Ilhéus.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 372-381.
