MARKOV RANDOM-FIELD IMAGE SEGMENTATION USING CELLULAR NEURAL-NETWORK

Markovian approaches to early vision processes need a huge amount of c omputing power. These algorithms can usually be implemented on paralle l computing structures. with the Cellular Neural Networks (CNN), a new image processing toot is coming into consideration, Its VLSI implemen tation takes place on a single analog chip containing several thousand s of cells. Herein se use the CNN UM architecture for statistical imag e segmentation. The Modified Metropolis Dynamics (MMD) method can be i mplemented into the raw analog architecture of the CNN, We are able to implement a (pseudo) random held generator using one layer (one memor y/cell) of the CNN. We can introduce the whole pseudostochastic segmen tation process in the CNN architecture using 8 memories/cell, We use s imple arithmetic functions (addition, multiplication), equality-test b etween neighboring pixels and very simple nonlinear output functions ( step, jigsaw, With this architecture, a real VLSI CNN chip can execute a pseudostochastic relaxation algorithm of about 100 iterations in ab out 1 ms, In the proposed solution the segmentation is unsupervised. W e have developed a pixel-level statistical estimation model. The CNN t urns the original image into a smooth one. Then we have two gray-level values for every pixel: the original and the smoothed one. These two values are used for estimating the probability distribution of region label at a given pixel. Using the conventional first-order Markov Rand om Field (MRF) model, some misclassification errors remained at the re gion boundaries, because of the estimation difficulties in case of low SNR. By using a greater neighborhood, this problem has been avoided. In our CNN experiments, we used a simulation system with a fixed-point integer precision of 16 bits, Our results show that even in the case of the very constrained conditions of value-representations (the inter val is (-64, +64), the accuracy is 0.002) can result in an effective a nd acceptable segmentation.