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).
At nighttime, visibility will be greatly decreased and causes image foreground and background to appear blended together. However, ambient light is always present in the natural environment, and as a consequent it creates some contrast within the darkness. In this paper, we for- mulated a visual analytic method that automatically unveils the contrast of dark images (i.e nighttime), revealing the ”hidden” contents. We utilize the traits of image represen- tations obtained from computer vision techniques through a learning based inversion algorithm, eliminating the re- liance to night vision camera and at the same time minimiz- ing the need of human intervention (i.e manual fine-tuning the gamma correction using Adobe Photoshop software). Experiments using the new Malaya Pedestrian in the Dark (MyPD) dataset that we collected from the website Flickr, and a comparison to conventional methods such as image integral and gamma correction show the efficacy of the pro- posed method. Additionally, we showed the potential of this framework in some applications that would benefit public safety.
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.
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.