Chee Seng Chan

Senior Lecturer

cs.chan at um edu my
cs.chan at ieee org

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.

In general, my research interests are computer vision and fuzzy qualitative reasoning; with a focus on image/video content analysis and human-robot interaction.

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Derek Anderson, James Keller and I will be conducting a back-to-back tutorials on "Computer Vision: A Computational Intelligence Perspective", at WCCI'16, Vancouver, Canada.

    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.

    E. Vats & C.S. Chan, Early Detection of Human Actions - A Hybrid Approach, Applied Soft Computing (accepted)

        Nominated as the Best Student Paper award in FUZZ-IEEE 2015, Istanbul, Turkey

        PDF

    Early anticipation of human action is essential in a wide spectrum of applications ranging from video surveillance to health- care. While human action recognition has been extensively studied, little attention is paid to the problem of detecting ongoing human action early, i.e. detecting an action as soon as it begins, but before it finishes. This study aims at training a detector to be capable of recognizing a human action when only partial action sample is seen. To do so, a hybrid technique is proposed in this work which combines the benefits of computer vision as well as fuzzy set theory based on the fuzzy Bandler and Kohout’s sub-triangle product (BK subproduct). The novelty lies in the construction of a frame-by-frame membership function for each kind of possible movement. Detection is triggered when a pre-defined threshold is reached in a suitable way. Experimental results on a publicly available dataset demonstrate the benefits and effectiveness of the proposed method.

    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.

    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 visualisa- tion 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.

    Object recognition systems usually require fully complete manually labeled training data to train classifier. In this paper, we study the problem of object recognition, where the training samples are missing during the classifier learning stage, a task also known as zero-shot learning. We propose a novel zero-shot learning strategy that utilizes the topic model and hierarchical class concept. Our proposed method advanced where cumbersome human anno- tation stage (i.e., attribute-based classification) is eliminated. We achieve comparable performance with state-of-the-art algorithms in four public datasets: PubFig (67.09%), Cifar-100 (54.85%), Caltech-256 (52.14%), and Animals with Attributes (49.65%), when unseen classes exist in the classification task.

    C.H. Lim, E. Vats & C.S. Chan, Fuzzy Human Motion Analysis: A Review, Pattern Recognition, 2015

        First fuzzy review paper in human motion analysis with 252 references

        PDF

    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.

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.

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.

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Crowd Dataset

The dataset is used in our ICPR'14 paper.

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Malaya Abrupt Motion (MAMo) Dataset

The tracking dataset is used in our Info. Sci.'14 paper.

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Pratheepan Human Skin Detection Dataset

The dataset is used in our IEEE T-II'12 paper.

Details ....