A chaos synchronization-based dynamic vision model for image segmentation

There has been intense research in feature binding to understand the parallel processing of features in visual information processing. The synchronization of spiking neurons is important for successful feature binding. In this work, we propose a novel approach to feature binding in spiking neurons using chaotic synchronization. We exploit each image pixel intensity value as individual neuron to generate chaotic time series. We generate the coupled map lattice series for neighborhood interaction and synchronization in spatiotemporal space. The largest cluster in the time series with similar chaotic synchronization parameter is used to generate segmented image. We obtain proof-of-concept application of our model in MR image clustering and compare our results with the existing Otsu adaptive segmentation technique.