Spatio-temporal CNN algorithm for object segmentation and object recognition

In this paper a spatio-temporal analogic cellular neural network (CNN) algorithm is designed for front-end filtering, segmentation and object recognition. First, a generalized segmentation strategy is presented based on various diffusion models. Both PDE and non-PDE related schemes are discussed and their VLSI complexity is analyzed. In classification (object recognition) a CNN implementation of the autowave metric, a "nonlinear" variant of the Hausdorff metric, is used. This approach turned out to be superior compared to some other classification methods. A number of tests have been completed within the so-called "bubble/debris" segmentation experiments using original and artificial gray-scale images.