Senior Lecturercs.chan at um edu my
I received my Ph.D. degree from University of Portsmouth, U.K. in 2008. Currently, I am a Senior Lecturer at the Faculty of Computer Science & Information Technology (FCSIT), University of Malaya.
In general, my research interests are computer vision and fuzzy qualitative reasoning; with a focus on image/video content analysis.
I have been involved in various research projects supported by the University of Malaya Research Grant (UMRG), Fundamental Research Grant Scheme (FRGS), Prototype Research Grant Scheme (PRGS), High Impact Research Grant (HiRG), national and international industrials and academic partners. 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 Young Scientist Network-Academy of Sciences Malaysia (YSN-ASM) in 2015, 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).
A picture is worth a thousand words. Not until recently, however, we noticed some success stories in understanding of visual scenes: a model that is capable of to detect/name objects, describe their attributes, and recognize their relationships/interactions. In this paper, we propose a phrase-based hierarchical Long Short-Term Memory (phi-LSTM) model to generate image description. The proposed model encodes sentence as a sequence of combination of phrases and words, instead of a sequence of words alone as in those conventional solutions. The two levels of this model are dedicated to i) learn to generate image relevant noun phrases, and ii) produce appropriate image description from the phrases and other words in the corpus. Adopting the convolutional neural network to learn image features and the LSTM to learn word sequence in a sentence, the proposed model has shown better or competitive results in comparison to the state-of-the-art models on Flickr8k and Flickr30k datasets.
Crowd segmentation is important in serving as the basis for a wide range of crowd analysis tasks such as density estimation and behavior understanding. However, due to inter-occlusions, perspective distortion, clutter background and random crowd distribution, localizing crowd segments is technically a very challenging task. This paper proposes a novel crowd segmentation framework based on granular computing (GrCS) to enable the problem of crowd segmentation be conceptualized at different levels of granularity, and to map problems into computationally tractable subproblems. It shows that by exploiting the correlation among pixel granules, we are able to aggregate structurally similar pixels into meaningful atomic structure granules. This is useful in outlining natural boundaries between crowd and background (i.e. non-crowd) regions. From the structure granules, we infer the crowd and background regions by granular information classification. GrCS is scene-independent, and can be applied effectively to crowd scenes with a variety of physical layout and crowdedness. Extensive experiments have been conducted on hundreds of real and synthetic crowd scenes. The results demonstrate that by exploiting the correlation among granules, we can outline the natural boundaries of structurally similar crowd and background regions necessary for crowd segmentation.
A crowd behavior analysis survey with 180 references
Although the traits emerged in a mass gathering are often non-deliberative, the act of mass impulse may lead to irrevocable crowd disasters. The two-fold increase of carnage in crowd since the past two decades has spurred significant advances in the field of computer vision, towards effective and proactive crowd surveillance. Computer vision studies related to crowd are observed to resonate with the understanding of the emergent behavior in physics (complex systems) and biology (animal swarm). These studies, which are inspired by biology and physics, share surprisingly common insights, and interesting contradictions. However, this aspect of discussion has not been fully explored. Therefore, this survey provides the readers with a review of the state-of-the-art methods in crowd behavior analysis from the physics and biologically inspired perspectives. We provide insights and comprehensive discussions for a broader understanding of the underlying prospect of blending physics and biology studies in computer vision.
First fuzzy review paper in human motion analysis with 252 references
Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on. However, most of these real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, the fuzzy set theory has been applied and showed great success in the recent past. In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives. To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA. For ease of understanding, we conceptually classify the human motion into three broad levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.
August 2016:One(1) paper accepted in ACCV'16, Taipei, Taiwan.
July 2016:One(1) paper accepted in ICPR'16, Cancun, Mexico.
May 2016:One(1) paper accepted in ICIP'16, Phoenix, USA.
March 2016:Two(2) papers accepted in FUZZ-IEEE'16, Vancouver, Canada.
March 2016:Paper accepted in IEEE Transactions on Fuzzy Systems.A. Ahmadian, S. Salahshour & C.S. Chan, Fractional Differential Systems: A Fuzzy Solution based on Operational Matrix of Shifted Chebyshev Polynomials and its Applications
In this paper, a new formula of fuzzy Caputo fractional-order derivatives (0 < v ≤ 1) in terms of shifted Chebyshev polynomials is derived. The proposed approach introduces shifted Chebyshev operational matrix in combination with shifted Chebyshev tau technique for the numerical solution of linear fuzzy fractional order differential equations. The main advantage of the propose approach is that it simplifies the problem alike in solving a system of fuzzy algebraic linear equation. An approximated error bound between the exact solution and the proposed fuzzy solution with respect to the number of fuzzy rules and solution errors is derived. Furthermore, we also discuss the convergence of the proposed method from the fuzzy perspective. Experimentally, we show the strength of the proposed method in solving a variety of FDE models under uncertainty encountered in engineering and physical phenomena (i.e viscoelasticity, oscillations and Resistor-Capacitor (RC) circuits). Comparisons are also made with solutions obtained by the Laguerre polynomials and fractional Euler method.PDF (Coming Soon)
The dataset is used in our ICPR'14 paper.Details ....
Malaya Abrupt Motion (MAMo) Dataset
The tracking dataset is used in our Info. Sci.'14 paper.Details ....
Pratheepan Human Skin Detection Dataset
The dataset is used in our IEEE T-II'12 paper.Details ....