Abstract
In this chapter, the implementation of visual routines for topographic cellular processors is described in detail. In specific, AnaLogic Computers Kft. (called AnaLogic henceforth) - as part of their contribution to the SPARK project - has developed special visual algorithms for cognition, and implemented them on the Eye-RIS v1.2 system of AnaFocus, described in Chapter 8. A significant requirement was to be able to run these routines in real-time, enabling the roving robots to react to their environment instantaneously. This was achieved, and a library of image processing functions was efficiently implemented on the Eye-RIS system, utilizing the capabilities of the Q-Eye chip. This significant speedup enables real-time image processing with the system even in case of complex tasks. The new functionalities of the Eye-RIS v1.2 visual device enabled the implementation of several advanced visual routines. These routines run at a speed of 200 to 1,000 frames per second (fps) on the system. The following is a list of the specific routines that were implemented:
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Global displacement calculation;
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Foreground-background separation based segmentation;
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Active contour;
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Multi-object tracking.
In the following sections, each of the above routines is described, along with example programs. Examples are also provided showing the results of the processing, both in terms of what output they produce and also their performance on the Eye-RIS system.
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References
Caselles, V., Catte, F., Dibos, F.: A Geometric Model of Active Contours in Image Processing. Numer. Math. 66 (1993)
Hillier, D., Binzberger, V., Vilarino, D.L., Rekeczky, C.: Topographic Active Contour Techniques: Theory, Implementations and Comparisons. International Journal on Circuit Theory and Applications (2005)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contours Models. International Journal on Computer Vision 1, 321–331 (1988)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape Modeling with Front Propagation: A Level Set Approach. IEEE Trans. Patt. Anal. Mach. Intell. 17(2), 158–174 (1995)
Rekeczky, C., Chua, L.O.: Computing with Front Propagation: Active Contour and Skeleton Models in Continuous-Time CNN. Journal of VLSI Signal Processing Systems 23(2/3), 373–402 (1999)
Vilarino, D.L., Brea, V.M., Cabello, D., Pardo, J.M.: Discrete-Time CNN for Image Segmentation by Active Contours. Pattern Recognition Letters 19(8), 721–734 (1998)
Vilarino, D.L., Rekeczky, C.: Pixel Level Snakes on the CNN-UM: Algorithm Design, On-chip Implementation and Applications. International Journal on Circuit Theory and Applications 33, 17–51 (2005)
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Zarándy, Á., Rekeczky, C. (2009). Visual Algorithms for Cognition. In: Arena, P., Patanè, L. (eds) Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots. Cognitive Systems Monographs, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88464-4_9
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DOI: https://doi.org/10.1007/978-3-540-88464-4_9
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