Hong Kong Polytechnic University

About: Hong Kong Polytechnic University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Tourism & Population. The organization has 29633 authors who have published 72136 publications receiving 1956312 citations. The organization is also known as: HKPU & PolyU.

Fast Visual Tracking via Dense Spatio-temporal Context Learning

Abstract: In this paper, we present a simple yet fast and robust algorithm which exploits the dense spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its locally dense contexts in a Bayesian framework, which models the statistical correlation between the simple low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is then posed by computing a confidence map which takes into account the prior information of the target location and thereby alleviates target location ambiguity effectively. We further propose a novel explicit scale adaptation scheme, which is able to deal with target scale variations efficiently and effectively. The Fast Fourier Transform (FFT) is adopted for fast learning and detection in this work, which only needs 4 FFT operations. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.

Well-Dispersed Chitosan/Graphene Oxide Nanocomposites

Abstract: Nanocomposites of chitosan and graphene oxide are prepared by simple self-assembly of both components in aqueous media. It is observed that graphene oxide is dispersed on a molecular scale in the chitosan matrix and some interactions occur between chitosan matrix and graphene oxide sheets. These are responsible for efficient load transfer between the nanofiller graphene and chitosan matrix. Compared with the pure chitosan, the tensile strength, and Young's modulus of the graphene-based materials are significantly improved by about 122 and 64%, respectively, with incorporation of 1 wt % graphene oxide. At the same time, the elongation at the break point increases remarkably. The experimental results indicate that graphene oxide sheets prefer to disperse well within the nanocomposites.

Complex network from pseudoperiodic time series: Topology versus Dynamics

Abstract: We construct complex networks from pseudoperiodic time series, with each cycle represented by a single node in the network. We investigate the statistical properties of these networks for various time series and find that time series with different dynamics exhibit distinct topological structures. Specifically, noisy periodic signals correspond to random networks, and chaotic time series generate networks that exhibit small world and scale free features. We show that this distinction in topological structure results from the hierarchy of unstable periodic orbits embedded in the chaotic attractor. Standard measures of structure in complex networks can therefore be applied to distinguish different dynamic regimes in time series. Application to human electrocardiograms shows that such statistical properties are able to differentiate between the sinus rhythm cardiograms of healthy volunteers and those of coronary care patients.