Architecture of oscillatory neural network for image segmentation

Oscillatory neural networks are a recent approach for applications in image segmentation. In this context, the LEGION (Locally Excitatory Globally Inhibitory Oscillator Network) is the most consistent proposal. As positive aspects, the network has got a parallel architecture and capacity to separate the segments in time. On the other hand, the structure based on differential equations presents high computational complexity and limited capacity of segmentation, which restricts practical applications. In this paper, a proposal of a parallel architecture for implementation of an oscillatory neural network suitable for image segmentation is presented. The proposed network keeps the positive features of the LEGION network, offering lower complexity for implementation in digital hardware and capacity of segmentation unlimited, as well as a few parameters, with an intuitive setting. Preliminary results confirm the successful operation of the proposed network in applications of image segmentation.