Relaxation neural network model for optimal binary representation of images and its implementation on a parallel computer
Noboru Sonehara · Systems and Computers in Japan · 1992
Abstract A relaxation neural network model is proposed to solve the binary image representation problem. This network iteratively minimizes the computational energy defined by the quantization error in neighboring picture elements in local and parallel computations. For effective binary representation depending on local features such as edges, a relaxation neural network is proposed. Interactions between binary processes and line processes represent discontinuities of the image. It is shown that the proposed neural models can generate high‐quality binary images. It is also shown that the proposed models can be efficiently implemented on loosely coupled, hypercube multiple‐instruction, multiple data stream (MIMD) and single instruction‐multiple data stream (SIMD) parallel computers by parallel computational models and programming methods.