Bi-i: a standalone ultra high speed cellular vision system

The Bi-i standalone cellular vision system is introduced and discussed. In the first part, the underlying sensor and system level architectures are presented and various implementations are overviewed. This computing platform consists of state-of-the-art sensing, cellular sensing-processing, digital signal processing and communication devices that make it possible to use the system as an ideal computing platform for combined topographic and non-topographic calculations in sensing-processing-actuation scenarios. In the second part of the paper, ultra-high frame rate laboratory experiments are shown and discussed to highlight the most peculiar features of the system and its applicability in various industrial quality control areas. The overview underlines the potentials of the Bi-i vision system for unmanned intelligent vehicle applications in visual exploration, identification, tracking and navigation.

[1]  Ákos Zarándy,et al.  Cellular multiadaptive analogic architecture: a computational framework for UAV applications , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  Tamás Roska,et al.  The CNN universal machine: an analogic array computer , 1993 .

[3]  Tamás Roska,et al.  A CNN framework for modeling parallel processing in a mammalian retina , 2002, Int. J. Circuit Theory Appl..

[4]  A. Rodriguez-Vazquez,et al.  A 64/spl times/64 CNN universal chip with analog and digital I/O , 1998, 1998 IEEE International Conference on Electronics, Circuits and Systems. Surfing the Waves of Science and Technology (Cat. No.98EX196).

[5]  Servando Espejo,et al.  Ultra-high frame rate focal plane image sensor and processor , 2002 .

[6]  Frank S. Werblin,et al.  The network behind spatio‐temporal patterns: building low‐complexity retinal models in CNN based on morphology, pharmacology and physiology , 2001, Int. J. Circuit Theory Appl..

[7]  F. Werblin,et al.  Vertical interactions across ten parallel, stacked representations in the mammalian retina , 2001, Nature.

[8]  Tamás Roska,et al.  CNN-based difference-controlled adaptive non-linear image filters , 1998, Int. J. Circuit Theory Appl..

[9]  Leon O. Chua,et al.  Methods for image processing and pattern formation in Cellular Neural Networks: a tutorial , 1995 .

[10]  Tamás Roska,et al.  CNN‐based difference‐controlled adaptive non‐linear image filters , 1998 .

[11]  A.G. Radvanyi.  On the rectangular grid representation of general CNN networks , 2000, Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA 2000) (Cat. No.00TH8509).

[12]  Ángel Rodríguez-Vázquez,et al.  ACE16k: A Programmable Focal Plane Vision Processor with 128 x 128 Resolution , 2001 .

[13]  Leon O. Chua,et al.  Estimating optical flow with cellular neural networks , 1998 .

[14]  Ángel Rodríguez-Vázquez,et al.  A CNN UNIVERSAL CHIP IN CMOS TECHNOLOGY , 1996 .

[15]  Leon O. Chua,et al.  The analogic cellular neural network as a bionic eye , 1995, Int. J. Circuit Theory Appl..

[16]  S. Wolfram,et al.  Two-dimensional cellular automata , 1985 .

[17]  Tamás Roska,et al.  The CNN universal machine , 1993 .

[18]  Leon O. Chua,et al.  CNN: A Vision of Complexity , 1997 .