Modeling inaccurate perception: Desynchronization issues of a chaotic pattern recognition neural network
The usual goal of modeling natural and artificial perception involves determining how a system can extract the object that it perceives from an image which is noisy. The "inverse" of this problem is one of modeling how even a clear image can be perceived to be blurred in certain contexts. We propose a chaotic model of Pattern Recognition (PR) for the theory of "blurring". The paper, which is an extension to a Companion paper  demonstrates how one can model blurring from the view point of a chaotic PR system. Unlike the Companion paper in which the chaotic PR system extracts the pattern from the input, this paper shows that the perception can be "blurred" if the dynamics of the chaotic system are modified. We thus propose a formal model, the Mb-AdNN, and present a rigorous analysis using the Routh-Hurwitz criterion and Lyapunov exponents. We also demonstrate, experimentally, the validity of our model by using a numeral dataset.
|Conference||14th Scandinavian Conference on Image Analysis, SCIA 2005|
Calitoiu, D. (Dragos), Oommen, J, & Nusbaumm, D. (Dorin). (2005). Modeling inaccurate perception: Desynchronization issues of a chaotic pattern recognition neural network. In Lecture Notes in Computer Science (pp. 821–830).