Browsing by Author "Tze, Kin Teo (Kenneth)"

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    Exploring Q-Learning Optimization in Traffic Signal Timing Plan Management
    (2011) Yit, Kwong Chin; Lai, Kuan Lee; Nurmin Bolong; Soo, Siang Yang; Tze, Kin Teo (Kenneth)
    Traffic congestions often occur within the entire traffic network of the urban areas due to the increasing of traffic demands by the outnumbered vehicles on road. The problem may be solved by a good traffic signal timing plan, but unfortunately most of the timing plans available currently are not fully optimized based on the on spot traffic conditions. The incapability of the traffic intersections to learn from their past experiences has cost them the lack of ability to adapt into the dynamic changes of the traffic flow. The proposed Qlearning approach can manage the traffic signal timing plan more effectively via optimization of the traffic flows. Qlearning gains rewards from its past experiences including its future actions to learn from its experience and determine the best possible actions. The proposed learning algorithm shows a good valuable performance that able to improve the traffic signal timing plan for the dynamic traffic flows within a traffic network.
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    Image Segmentation via Normalised Cuts and Clustering Algorithm
    (2012) Mei, Yeen Choong; Wei, Yeang Kow; Yit, Kwong Chin; Lorita Angeline; Tze, Kin Teo (Kenneth)
    Image segmentation has been widely applied in image analysis for various areas such as biomedical imaging, intelligent transportation systems and satellite imaging. The main goal of image segmentation is to simplify an image into segments that have a strong correlation with objects in the real world. Homogeneous regions of an image are regions containing common characteristics and are grouped as single segment. One of the graph partitioning methods in image segmentation, normalised cuts, has been recognised producing reliable segmentation result. To date, normalised cuts in image segmentation of various sized images is still lacking of analysis of its performance. In this paper, segmentation on synthetic images and natural images are covered to study the performance and effect of different image complexity towards segmentation process. This study gives some research findings for effective image segmentation using graph partitioning method with computation cost reduced. Because of its cost expensive and it becomes unfavourable in performing image segmentation on high resolution image especially in online image retrieval systems. Thus, a graph-based image segmentation method done in multistage approach is introduced here.
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    Implementing Manifold Learning in Adaptive MCMC for Tracking Vehicle under Disturbances
    (2012) Wei, Yeang Kow; Yit, Kwong Chin; Wei, Leong Khong; Hui, Keng Lau; Tze, Kin Teo (Kenneth)
    In recent years, tracking vehicle with overlapping and maneuvering disturbances has become a challenging task in visual tracking. Markov Chain Monte Carlo (MCMC) is proved to be effective in tracking vehicle under disturbances by probabilistically estimating the vehicle position. However the sampling based tracking algorithm is highly depending on the sampling efficiencies where adequate chain length is necessary to sustain the tracking accuracy. Therefore variance ratio (VR) based MCMC has been implemented in this study to adapt the chain length according to the disturbances encountered. Isomap manifold learning is further implemented to update the vehicle model and accurately track the vehicle with maneuvering disturbances. Multiple vehicle models with different viewing angles are represented by Isomap under low dimensional manifold. The suitable vehicle model will be selected according to the estimated vehicle position. Experimental results have shown that Isomap-VR-MCMC have better tracking performances compared to VR-MCMC with smaller RMSE value.