Analog VLSI and neural systems
Analog VLSI and neural systems
Winner-take-all networks of O(N) complexity
Advances in neural information processing systems 1
Analog VLSI circuits for stimulus localization and centroid computation
International Journal of Computer Vision - Special issue: VLSI for computer vision
Dynamic wires: an analog VLSI model for object-based processing
International Journal of Computer Vision - Special issue: VLSI for computer vision
VLSI analogs of neuronal visual processing: a synthesis of form and function
VLSI analogs of neuronal visual processing: a synthesis of form and function
An analog VLSI model of the fly elementary motion detector
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Analog VLSI Excitatory Feedback Circuits for AttentionalShifts and Tracking
Analog Integrated Circuits and Signal Processing
Analog VLSI Circuits for Covert Attentional Shifts
MICRONEURO '96 Proceedings of the 5th International Conference on Microelectronics for Neural Networks and Fuzzy Systems
Analog vlsi visual attention systems
Analog vlsi visual attention systems
Modeling Selective Attention Using a Neuromorphic Analog VLSI Device
Neural Computation
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A two-dimensional object-based analog VLSI model of selective attentional processing has been implemented using a standard 1.2 u m CMOS process. This chip extends previous work modeling object-based selection and scanning by incorporating the circuity and architectural changes necessary for two-dimensional focal plane processing. To balance the need for closely spaced, large photodetectors with the space requirements of complex in-pixel processing, the chip implements a multiresolution architecture. The system has the ability to group pixels into objects; this grouping is dynamic, driven solely by the segmentation criterion at the input. In the demonstration system, image intensity has been chosen for the input saliency map and the segmentation is based on spatial lowpass filtering followed by an intensity threshold. We present experimental results.