Advertisement

Generalization of shifted fovea multiresolution geometries applied to object detection

  • Fabián Arrebola
  • Pelegrín Camacho
  • Francisco Sandoval
Poster Session D: Biomedical Applications, Detection, Control & Surveillance, Inspection, Optical Character Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

This work describes a foveal vision system applied to object detection. The novelty of this system consists of carrying the detections using a generalization of the multiresolution shifted fovea images. The main advantage introduced is the great increase of the number of fovea positions allowed in shifted-fovea systems already implemented: this means that the maximum error of placement is reduced to one pixel, implying that any object could be examined at the highest resolution available regardless of its coordinates. The concept is based on increasing the degrees of freedom and the related number of configuration parameters and the application of a new shifting algorithm which allows a higher number of fixation points on the scene and, therefore, reduces the error of fovea positioning on the region of interest and aproaches closer to the required scene details. Besides, we introduce the multiresolution data structure to manipulate and process this type of foveal geometries, as well as the results obtained after applying hierarchical algorithms for segmentation and detection of objects within this type of multiresolution images.

Keywords

Gray Level Configuration Parameter Resolution Level Active Vision Image Pyramid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. [1]
    F. Ferrari, J. Nielsen, P. Questa and G. Sandini, Space variant imaging, Sensor Review Vol. 15, No. 2, pp. 17–20,1995.CrossRefGoogle Scholar
  2. [2]
    M. J. Swain, M. A. Stricker, Promising Directions in Active Vision, NSF Active Vision Workshop, University of Chicago, August 1991.Google Scholar
  3. [3]
    P. Camacho, F. Arrebola, F. Sandoval, Shifted Fovea Multiresolution Geometries, IEEE International Conference on Image Processing (ICIP'96), Vol. 1, pp. 307–310, Lausanne, Switzerland, 1996CrossRefGoogle Scholar
  4. [4]
    C. Bandera, P. Scott, Foveal Machine Vision Systems, IEEE International Conference on System, Man and Cybernetics, Cambridge, MA, Nov. 1989.Google Scholar
  5. [5]
    P. Scott, C. Bandera, Hierarchical Multiresolution Data Structures and Algorithms for Foveal Vision Systems, IEEE International Conference on System, Man and Cybernetics, Los Angeles CA, Nov. 1990.Google Scholar
  6. [6]
    P. J. Burt, T. H. Hong, A. Rosenfeld, Image Smoothing Based on Neighbor Linking, IEEE Trans. SMC, Vol. 11, No. 12, pp. 769–780, 1981.Google Scholar
  7. [7]
    T. H. Hong, K. A. Narayanan, S. Peleg, A. Rosenfeld, T. Silberberg, Image Smoothing and Segmentation by Multiresolution Pixel Linking: Further Experiments and Extension, IEEE Trans. System, Man and Cyb., Vol. 12, pp. 611–622, No. 5, 1982.Google Scholar
  8. [8]
    C. Bandera, Structures and Algortithms for Foveal Machine Vision, Amherst Systems, Tech. Report, Buffalo, NY (USA), May 1994.Google Scholar
  9. [9]
    F. Arrebola, P. Camacho, F. Sandoval, Segmentación de Imágenes Multirresolción con Fóvea Desplazable, Actas URSI-96, Vol. 2, pp. 205–208, Madrid, España 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Fabián Arrebola
    • 1
  • Pelegrín Camacho
    • 1
  • Francisco Sandoval
    • 1
  1. 1.Dpto. Tecnología ElectrónicaE.T.S.I. Telecomunicación Universidad de Málaga Campus de TeatinosMálagaSpain

Personalised recommendations