ICANN 1996: Artificial Neural Networks — ICANN 96 pp 377-382 | Cite as
An architectural study of a massively parallel processor for convolution-type operations in complex vision tasks
Oral Presentations: Implementations Implementations: Dynamic and Massively Parallel Networks
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Abstract
Complex vision tasks, e.g., face recognition or wavelet based image compression, impose severe demands on computational resources to meet the real-time requirements of the applications. Clearly, the bottleneck in computation can be identified in the first processing steps, where basic features are computed from full size images, as motion cues and Gabor or wavelet transform coefficients. This paper presents an architectural study of a vision processor, which was particulary designed to overcome this bottleneck.
Keywords
Clock Cycle Cellular Neural Network Processor Element Gaussian Pyramid Architectural Study
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© Springer-Verlag Berlin Heidelberg 1996