Senior Lecturercs.chan at um edu my
I received my Ph.D. degree from University of Portsmouth, U.K. in 2008. Currenty, I am a Senior Lecturer at the Faculty of Computer Science & Information Technology (FCSIT), University of Malaya. I previously held research appointments at Universities of Ulster and Portsmouth, U.K., respectively.
In general, my research interests are fuzzy qualitative reasoning and computer vision; with a focus on image/video content analysis and human-robot interaction.
I have been involved in various research projects supported by the University of Malaya Research Grant (UMRG), Fundamental Research Grant Scheme (FRGS), Exploratory Research Grant Scheme (ERGS), High Impact Research Grant (HiRG), national and international industrials and academic partners. I also served as the guest editor in International Journal of Uncertainty, Fuzziness and Knowledge-based Systems (IJUFKS), Information Sciences (INS) and Signal, Video and Image Processing (SVIP). I am the founder chair for the IEEE Computational Intelligence Society (CIS) Malaysia chapter, the organising chair for the Asian Conference on Pattern Recognition (ACPR) in 2015, and general chair for the IEEE Visual Communications and Image Processing (VCIP) in 2013. I am a recipient of the Hitachi Research Fellowship in 2013 and the Institution of Engineering & Technology (Malaysia) Young Engineer award in 2010. I am a Senior Member of IEEE, a Chartered Engineer and a Member of Institution of Engineering & Technology (IET).
This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England. To gain intuition on the chosen features from the CNN model (opposed to a 'black box' solution), a visualisation technique based on the deconvolutional networks (DN) is utilized. It is found that venations of different order have been chosen to uniquely represent each of the plant species. Experimental results using these CNN features with different classifiers show consistency and superiority compared to the state-of-the art solutions which rely on hand-crafted features.
Humans have the natural capabilities to perceive and anticipate actions of objects they interact with, including incidents happen within their neighborhoods. These days, this important aspect of human perception has been widely incorporated into computer vision framework to perform human action detection task. However, little attention is paid to the problem of detecting ongoing human actions as early as possible, which is crucial in a number of important applications ranging from video surveillance to health-care. In this paper, we propose a framework for detecting ongoing human actions as early as possible, i.e. detecting an action as soon as it begins, but before it completes. This is make possible with the used of Fuzzy Bandler and Kohout’s sub-triangle product (BK subproduct) inference mechanism, utilizing fuzzy capabilities in handling the arisen uncertainties during the human action recognition stage for reliable decision making. Experimental results with publicly available dataset illustrate the effectiveness of the proposed method.
Crowd Dataset for our ICPR (2014) paper is now online. Download here.
Curve Text (CUTE80) Dataset for our ESWA (2014) paper is now online. Download here.
Tracking Dataset (Malaya Abrupt Motion (MAMo)) for our Information Science (2014) paper is now online. Download here.
Human Skin Detection Dataset (Pratheepan) for our IEEE T-II (2012) paper is now online. Download here.