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An object oriented segmentation on analog CNN chip
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DOI：10.1109/TCSI.2003.813985.png)
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This paper introduces a real-time object oriented segmentation algorithm, designed and implemented on a new type of mixed analog/digital chip based on the cellular neural/nonlinear network (CNN) paradigm. The fully parallel architecture of the CNN processes all the pixels of an image at the same time, so the time spent for the image segmentation is independent on the number of objects in the image. This implementation of the segmentation algorithm is shown to well satisfy the real-time requirements both as a standing alone processing procedure, and as a module inside the MPEG-4 video coding standard. Finally, the general purpose characteristics of the CNN universal chip allow to use the algorithm introduced as an efficient pre-processing procedure for man interesting, image/video standing alone applications.
Keyword:
cellular neural networks (CNNs)
object oriented segmentation (OOS)
MPEG-4 video coding standard
CNN chip prototype
noise filter
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