Abstract
This paper presents three neuromimetic indicators for the visual perception of motion. They estimate the motion, the speed and the direction. All of them emerge from the first two stages in the Castellanos model [1], where a causal spatio-temporal filtering of Gabor-like type captures the oriented contrast and an antagonist inhibition mechanism estimates the motion. These neuromimetic indicators have been evaluated on sequences of natural and synthetic images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Castellanos-Sánchez, C.: Neuromimetic connectionist model for embedded visual perception of motion. PhD thesis, Université Henri Poincaré (Nancy I), Nancy, France, Bibliothèque des Sciences et Techniques (October 2005)
McCane, B., Novins, K., Grannitch, D., Galvin, B.: On benchmarking optical flow. Computer Vision and Image Undestanding, 126–143 (2001)
Fellez, W.A., Taylor, J.G.: Establishing retinotopy by lateral-inhibition type homogeneous neural fields. Neurocomputing 48, 313–322 (2002)
Latham, P.E., Nirenberg, S.: Computing and stability in cortical networks. Neural Computation, 1385–1412 (2004)
Moga, S.: Apprendre par imitation: une nouvelle voie d’apprentissage pour les robots autonomes. PhD thesis, Université de Cergy-Pontoise, Cergy-Pontoise, France (September 2000)
Simoncelli, E.P., Heeger, D.J.: A model of neural responses in visual area mt. Visual Research 38(5), 743–761 (1998)
Mingolla, E.: Neural models of motion integration and segmentation. Neural Networks 16, 939–945 (2003)
Pack, C., Grossberg, S., Mingolla, E.: A neural model of smooth pursuit control and motion perception by cortical area mst. Technical Report CAS/CNR-TR-99-023, Department of Cognitive and Neural Systems and Center for Adaptive Systems, Boston University, 677 Beacon St, Boston, MA 02215 (September 2000)
Zemel, R.S., Sejnowski, T.J.: A model for encoding multiple object motions and self-motion in area mst of primate visual cortex. The Journal of Neurosciences 18(1), 531–547 (1998)
Castellanos-Sánchez, C., Girau, B., Alexandre, F.: A connectionist approach for visual perception of motion. In: Smith, L., Hussain, A., Aleksander, I. (eds.) BICS 2004. Brain Inspired Cognitive Systems, September 2004, vol. BIS3–1, pp. 1–7 (2004)
Pollen, D., Ronner, S.: Phase relationships between adjacent simple cells in the visual cortex. Science 212, 1409–1411 (1981)
Newsome, W.T., Gizzi, M.S., Movshon, J.A.: Spatial and temporal properties of neurons in macaque mt. Investigative Ophtalmology and Visual Science Supplement 24, 106 (1983)
Hammond, P., Reck, J.: Influence of velocity on directional tuning of complex cells in cat striate cortex for texture motion. Neuroscience Letters 19, 309–314 (1981)
Torres-Huitzil, C., Girau, B., Castellanos-Sánchez, C.: On-chip visual perception of motion: A bio-inspired connectionist model on fpga. Neural Networks 18(5-6), 557–565 (2005)
Torres-Huitzil, C., Girau, B.: Fpga implementation of an excitatory and inhibitory connectionist model for motion perception. In: Brebner, G.J., Chakraborty, S., Wong, W.F. (eds.) FPT 2005. IEEE International Conference on Field-Programmable Technology, December 2005, pp. 259–266. IEEE Computer Society Press, Los Alamitos (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Castellanos-Sánchez, C. (2007). Neuromimetic Indicators for Visual Perception of Motion. In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds) Advances in Brain, Vision, and Artificial Intelligence. BVAI 2007. Lecture Notes in Computer Science, vol 4729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75555-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-75555-5_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75554-8
Online ISBN: 978-3-540-75555-5
eBook Packages: Computer ScienceComputer Science (R0)