Cellular Neural Networks for Markov Random Field Image Segmentation
نویسنده
چکیده
Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to 40,000) cells. Every CNN cell is connected to its close neighbors through feedback and feedforward convolutions, and it has an incell dynamics and nonlinearity. CNN Universal Machine (CNN-UM) has been developed for complex image processing tasks. It can be programmed through incell memories and instructions. However, CNN is basically a deterministic analog circuit.
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