JRM Vol.21 No.1 pp. 128-134
doi: 10.20965/jrm.2009.p0128


Eccentricity Estimator for Wide-Angle Fovea Vision Sensor

Sota Shimizu* and Joel W. Burdick**

*California Institute of Technology, Division of Biology, MC 139-74, Pasadena, CA 91125, USAWaseda University, Advanced Research Institute for Science and Engineering, 17 Kikui-cho, Shinjuku-ku, Tokyo 162-0044, Japan

**California Institute of Technology, Department of Bioengineering

October 6, 2008
October 6, 2008
February 20, 2009
fovea sensor, space-variant image, Fourier transform, wavelet transform, image processing

This paper proposes a method for estimating the eccentricity that corresponds to the incident angle to a fovea sensor. The proposed method uses the Fourier-Mellin Invariant descriptor to estimate rotation, scale, and translation, by taking both geometrical distortion and non-uniform resolution of a space-variant image from the fovea sensor into account. The following 2 points are focused on in this paper. One is to use multi-resolution images by Discrete Wavelet Transform to properly reduce noise caused by foveation. Another is to use a variable window function (although the window function is generally used for reducing DFT leakage caused by both ends of a signal) to change the effective field of view (FOV) so as not to sacrifice high accuracy. The simulation compares the root mean square (RMS) of the foveation noise between uniform and non-uniform resolutions when a resolution level and a FOV level are changed, respectively. The result shows the proposed method is suitable for the wide-angle space-variant image from the fovea sensor, and, moreover, it does not sacrifice the high accuracy in the central FOV. Another simulation is done to determine a reliable resolution level.

This paper is the full translation from the transactions of JSME Vol.74, No.744.

Cite this article as:
Sota Shimizu and Joel W. Burdick, “Eccentricity Estimator for Wide-Angle Fovea Vision Sensor,” J. Robot. Mechatron., Vol.21, No.1, pp. 128-134, 2009.
Data files:
  1. [1] E. L. Schwartz, “Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception,” Biological Cybernetics, Vol.29, pp. 181-194, 1977.
  2. [2] G. Sandini and V. Tagliasco, “An anthropomorphic retina-like structure for scene analysis,” Computer Graphics and Image Processing, 14, pp. 365-372, 1980.
  3. [3] F. Berton, G. Sandini, and G. Metta, “Anthropomorphic Visual Sensors,” Encyclopedia of Sensors, C.A. Grimes, E.C.Dickey, and M.V.Pishko (Eds.), Vol.10, pp. 1-16, 2005.
  4. [4] J. Van der Spiegel, G. Kreider, C. Claeys, I. Debusschere, G. Sandini, P. Dario, et al., “A foveated retina-like sensor using CCD technology,” Analog VLSI Implementations of Neural Networks, Kluwer, C. Mead and M. Ismail (Eds.), Boston, 1989.
  5. [5] R. Wodnicki, G. W. Roberts, and M. D. Levine, “A foveated image sensor in standard CMOS technology,” In Custom Integrated Circuits Conf., Santa Clara, California, 1995.
  6. [6] S. Shimizu,, “Vision Sensor with Wide Angle and High Distortion lens,” Video Proc. of IEEE Int. Conf. on Robotics and Automation, Visual Sensing 3, 1995.
  7. [7] Y. Kuniyoshi, N. Kita, K. Sugimoto,, “A Foveated Wide Angle Lens for Active Vision,” Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 2982-2988, 1995.
  8. [8] S. Shimizu, “A Model of Wide-Angle Foveation for All-Purpose Use,” Transactions of JSME Journal, Vol.073, No.731, C, pp. 2095-2100, 2007, (in Japanese).
  9. [9] S. Shimizu and J. Burdick, “Eccentricity Compensator for Wide-Angle Fovea Vision Sensor,” Transactions of JSME Journal, Vol.73, No.733, C, pp. 2591-2596, 2007, (in Japanese).
  10. [10] S. Shimizu, “Multi-Functional Application of Wide Angle Foveated Vision Sensor in Mobile Robot Navigation,” Journal of Robotics and Mechatronics, Vol.14, No.4, pp. 382-389, 2002.
  11. [11] S. Shimizu, “Wide-Angle Vision Sensor with High-Distortion Lens (Detection of Camera Location and Gaze Direction Based on the Two-Parallel-Line Algorithm),” JSME Int. Journal, Series C, Vol.41, No.4, pp. 893-900, 1998.
  12. [12] D. Casasent and D. Psaltis, “Position, Rotation and Scale-invariant Optical Correlation,” Applied Optics, Vol.15, pp. 1795-1799, 1976.
  13. [13] B. S. Reddy and B. N. Chatterji, “An FFT-based Technique for Translation, Rotation and Scale-Invariant Image Registration,” IEEE Transactions on Image Processing, Vol.5, No.8, pp. 1266-1271, 1996.
  14. [14] Q. Chen, M. Defrise, and F. Deconinck, “Symmetric Phase-Only Matched Filtering of Fourier-Mellin Transforms for Image Registration and Recognition,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.16, No.12, pp. 1156-1168, 1994.
  15. [15] J. Horner and D. Gianino, “Phase-only matched filtering,” Applied Optics, Vol.23, No.6, pp. 812-816, 1984.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 01, 2021