Adaptive Harris Corner Detector Evaluated with Cross-Spectral Images

  • Patricia L. Suárez
  • Angel D. Sappa
  • Boris X. Vintimilla
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 721)

Abstract

This paper proposes a novel approach to use cross-spectral images to achieve a better performance with the proposed Adaptive Harris corner detector comparing its obtained results with those achieved with images of the visible spectra. The images of urban, field, old-building and country category were used for the experiments, given the variety of the textures present in these images, with which the complexity of the proposal is much more challenging for its verification. It is a new scope, which means improving the detection of characteristic points using cross-spectral images (NIR, G, B) and applying pruning techniques, the combination of channels for this fusion is the one that generates the largest variance based on the intensity of the merged pixels, therefore, it is that which maximizes the entropy in the resulting Cross-spectral images.

Harris is one of the most widely used corner detection algorithm, so any improvement in its efficiency is an important contribution in the field of computer vision. The experiments conclude that the inclusion of a (NIR) channel in the image as a result of the combination of the spectra, greatly improves the corner detection due to better entropy of the resulting image after the fusion, Therefore the fusion process applied to the images improves the results obtained in subsequent processes such as identification of objects or patterns, classification and/or segmentation.

Keywords

Near Infrared Cross-spectral Visible spectra Pixel Fusion Pruning 

Notes

Acknowledgment

This work has been partially supported by the ESPOL under projects PRAIM and KISHWAR.

References

  1. 1.
    Sappa, A.D., Vintimilla, B.X.: Cost-based closed-contour representations. J. Electron. Imaging 16, 023009–023009 (2007)CrossRefGoogle Scholar
  2. 2.
    Ricaurte, P., Chilán, C., Aguilera-Carrasco, C.A., Vintimilla, B.X., Sappa, A.D.: Feature point descriptors: Infrared and visible spectra. Sensors 14, 3690–3701 (2014)CrossRefGoogle Scholar
  3. 3.
    Shaw, G.A., Burke, H.K.: Spectral imaging for remote sensing. Lincoln Lab. J. 14, 3–28 (2003)Google Scholar
  4. 4.
    Ceamanos, X., Douté, S., Luo, B., Schmidt, F., Jouannic, G., Chanussot, J.: Intercomparison and validation of techniques for spectral unmixing of hyperspectral images: a planetary case study. IEEE Trans. Geosci. Remote Sens. 49, 4341–4358 (2011)CrossRefGoogle Scholar
  5. 5.
    Moroni, M., Dacquino, C., Cenedese, A.: Mosaicing of hyperspectral images: the application of a spectrograph imaging device. Sensors 12, 10228–10247 (2012)CrossRefGoogle Scholar
  6. 6.
    Bruce, D.A., Owens, G.P., Maliki, A., Ajeel, A.A., et al.: Capabilities of remote sensing hyperspectral images for the detection of lead contamination: a review (2012)Google Scholar
  7. 7.
    Pohl, C., Van Genderen, J.L.: Review article multisensor image fusion in remote sensing: concepts, methods and applications. Int. J. Remote Sens. 19, 823–854 (1998)CrossRefGoogle Scholar
  8. 8.
    Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4353–4361 (2015)Google Scholar
  9. 9.
    Suárez, P.L., Sappa, A.D., Vintimilla, B.X.: Cross-spectral image patch similarity using convolutional neural network. In: 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), pp. 1–5. IEEE (2017)Google Scholar
  10. 10.
    Aguilera, C.A., Aguilera, F.J., Sappa, A.D., Aguilera, C., Toledo, R.: Learning cross-spectral similarity measures with deep convolutional neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, p. 9. IEEE (2016)Google Scholar
  11. 11.
    Suárez, P.L., Villavicencio, M.: Canny edge detection in cross-spectral fused images. In: 2017 International Conference on Information Systems and Computer Science, ENFOQUE UTE (INCISCOS), pp. 16–30. LATINDEX (2017)Google Scholar
  12. 12.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, Manchester, UK, vol. 15, pp. 10–5244 (1988)Google Scholar
  13. 13.
    Wu, M., Ramakrishnan, N., Lam, S.K., Srikanthan, T.: Low-complexity pruning for accelerating corner detection. In: 2012 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1684–1687. IEEE (2012)Google Scholar
  14. 14.
    Wilkinson, G.G.: Results and implications of a study of fifteen years of satellite image classification experiments. IEEE Trans. Geosci. Remote Sens. 43, 433–440 (2005)CrossRefGoogle Scholar
  15. 15.
    Iordache, M.D., Bioucas-Dias, J.M., Plaza, A.: Sparse unmixing of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 49, 2014–2039 (2011)CrossRefGoogle Scholar
  16. 16.
    Harris, J.L.: Image evaluation and restoration. JOSA 56, 569–574 (1966)CrossRefGoogle Scholar
  17. 17.
    Pavithra, A.S.: Agile segmentation and classification for hyper spectral image using Harris Corner Detector. Int. J. Sci. Res. 4, 196–199 (2015)Google Scholar
  18. 18.
    Brown, M., Süsstrunk, S.: Multi-spectral SIFT for scene category recognition. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 177–184. IEEE (2011)Google Scholar
  19. 19.
    Dore, A., Beoldo, A., Regazzoni, C.S.: Multitarget tracking with a corner-based particle filter. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1251–1258. IEEE (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Patricia L. Suárez
    • 1
  • Angel D. Sappa
    • 1
    • 2
  • Boris X. Vintimilla
    • 1
  1. 1.Facultad de Ingeniería en Electricidad y Computacíon, CIDISEscuela Superior Politécnica del Litoral, ESPOLGuayaquilEcuador
  2. 2.Computer Vision CenterBarcelonaSpain

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