Video object segmentation in the cellular neural networks architecture

Abstract

MPEG-4 provides a basic tool for interactivity and manipulation of video sequences. To take advantage of these content-based functionalities, video sequences must be segmented into semantically meaningful objects. Video object segmentation is a key step in defining the content of any video sequences. The algorithm proposed in this paper is a spatiotemporal segmentation. It starts from an over-segmented image by morphological gradients, and then the segments are merged by spatiotemporal information. To tracking the segmented objects, stochastic optimization methods are used to form homogeneous dense optical vector fields. We simulate the algorithm in the Cellular Neural Networks (CNNs) architecture by MATCNN. It suggests a fully parallel implementation in CNN-UM chip.