Image segmentation via double pulse coupled neural network

Image segmentation is a partitioning of an image into constituent parts using attributes such as pixel intensity, spectral values, and/or textural properties. As a key role in several approaches to image compression and image analysis, image segmentation produces an image representation in terms of boundaries and regions of various shapes and interrelationships. This paper presents a new approach called Double Pulse Coupled Neural Network (DPCNN) to perform fast image segmentation. Currently, most segmentation methods merge regions one by one to alleviate the over-segmentation problem. However, sequential merging would inevitably incur lengthy computation time. DPCNN simultaneously updates features of regions by referring to adjacent regions. Due to the use of synchronous update strategy, DPCNN achieves fast merging and provides great potentiality for a fully parallel hardware implementation. The iterative operation terminates when the numbers of regions in consecutive iterations are identical. Empirical results show that DPCNN outperforms other methods in terms of computation efficiency and segmentation accuracy.