Abstract
The faithful detection of the motion and of the distance of the objects in the visual scene is a desirable feature of any artificial vision system designed to operate in unknown environments characterized by conditions variable in time in an often unpredictable way. Here, we propose a distributed neuromorphic architecture, that, by sharing the computational resources to solve the stereo and the motion problems, produces fast and reliable estimates of optic flow and 2D disparity. The specific joint design approach allows us to obtain high performance at an affordable computational cost. The approach is validated with respect to the state-of-the-art algorithms and in real-world situations.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., Szeliski, R.: A database and evaluation methodology for optical flow. In: ICCV (2007)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. of Computer Vision 47, 7–42 (2002)
Tombari, F., Mattoccia, S., Di Stefano, L., Addimanda, E.: Classification and evaluation of cost aggregation methods for stereo correspondence. In: CVPR (2008)
Milner, A.D., Goodale, M.: The visual brain in action. Oxford Univ. Press, Oxford (1995)
Heeger, D.: Model for the extraction of image flow. JOSA 4(8), 1455–1471 (1987)
Grzywacz, N., Yuille, A.: A model for the estimate of local image velocity by cells in the visual cortex. Proc. R. Soc. Lond. B 239, 129–161 (1990)
Chen, Y., Qian, N.: A coarse-to-fine disparity energy model with both phase-shift and position-shift receptive field mechanisms. Neural Computation 16, 1545–1577 (2004)
Fleet, D., Wagner, H., Heeger, D.: Neural encoding of binocular disparity: Energy models, position shifts and phase shifts. Vision Res. 36(12), 1839–1857 (1996)
Bayerl, P., Neumann, H.: A fast biologically inspired algorithm for recurrent motion estimation. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 246–260 (2007)
Shimonomura, K., Kushima, T., Yagi, T.: Binocular robot vision emulating disparity computation in the primary visual cortex. Neural Networks 21(2-3), 331–340 (2008)
Higgins, C., Shams, S.: A neuromorphic vision processor for spatial integration of optical flow. In: ICCNS 2001 (2001)
Dale, J., Johnston, A.: A real-time implementation of a neuromorphic optic-flow algorithm. Perception 31, 136 (2002)
Pouget, A., Dayan, P., Zemel, R.S.: Inference and computation with population codes. Ann. Rev Neurosci 26, 381–410 (2003)
Adelson, E., Bergen, J.: The plenoptic and the elements of early vision. In: Landy, M., Movshon, J. (eds.) Computational Models of Visual Processing, pp. 3–20. MIT Press, Cambridge (1991)
Adelson, E., Bergen, J.: Spatiotemporal energy models for the perception of motion. JOSA 2, 284–321 (1985)
Ohzawa, I., De Angelis, G., Freeman, R.: Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. Science 249, 1037–1041 (1990)
Morgan, M.J., Castet, E.: The aperture problem in stereopsis. Vision Res. 37(19), 2737–2744 (1997)
Serrano-Pedraza, I., Read, J.C.A.: Stereo vision requires an explicit encoding of vertical disparity. J.Vision 9(4), 1–12 (2009)
Daugman, J.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. JOSA A/2, 1160–1169 (1985)
Nestares, O., Navarro, R., Portilla, J., Tabernero, A.: Efficient spatial-domain implementation of a multiscale image representation based on Gabor functions. J. of Electronic Imaging 7(1), 166–173 (1998)
Pauwels, K., Hulle, M.V.: Optic flow from unstable sequences containing unconstrained scenes through local velocity constancy maximization. BMVC 1, 397–406 (2006)
Theimer, W., Mallot, H.: Phase-based binocular vergence control and depth reconstruction using active vision. CVGIP: Image Understanding 60(3), 343–358 (1994)
Chessa, M., Solari, F., Sabatini, S.: A virtual reality simulator for active stereo vision systems. In: VISAPP (2009)
Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. Int. J. of Computer Vision 12, 43–77 (1994)
Jenkin, M., Tsotsos, J.: Applying temporal constraints to the dynamic stereo problem. In: CVGIP, vol. 33, pp. 16–32 (1986)
Sabatini, S., Solari, F., Cavalleri, P., Bisio, G.: Phase-based binocular perception of motion in depth: Cortical-like operators and analog VLSI architectures. EURASIP Journal on Applied Signal Processing 7, 690–702 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chessa, M., Sabatini, S.P., Solari, F. (2009). A Fast Joint Bioinspired Algorithm for Optic Flow and Two-Dimensional Disparity Estimation. In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-04667-4_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04666-7
Online ISBN: 978-3-642-04667-4
eBook Packages: Computer ScienceComputer Science (R0)