Browsing by Author "Mei Yeen Choong"

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    Enhancement of Particle Filter Resampling in Vehicle Tracking via Genetic Algorithm
    (UKSim-AMSS 6th European Modelling Symposium, 2012) Wei Leong Khong; Wei Yeang Kow; Yit Kwong Chin; Mei Yeen Choong; Kenneth Tze Kin Teo
    Vehicle tracking is an essential approach that can help to improve the traffic surveillance or assist the road traffic control. Recently, the development of video surveillance infrastructure has incited the researchers to focus on the vehicle tracking by using video sensors. However, the amount of the on-road vehicle has been increased dramatically and hence the congestion of the traffic has made the occlusion scene become a challenge task for video sensor based tracking. Conventional particle filter will encounter tracking error during and after occlusion. Besides that, it also required more iteration to continuously track the vehicle after occlusion. Thus, particle filter with genetic operator resampling has been proposed as the tracking algorithm to faster converge and keep track on the target vehicle under various occlusion incidents. The experimental results show that enhancement of the particle filter with genetic algorithm manage to reduce the particle sample size.
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    Variational Level Set Algorithm in Image Segmentation for Foetus Ultrasound Imaging System
    (2012) Mei Yeen Choong; May Chin Seng; Aroland Kiring; Soo Siang Yang; Kenneth Tze Kin Teo
    Segmentation on ultrasound image is difficult when the image is not clear and contains unwanted noise. Since the object to be segmented out can be changing in shape for a period of time, there is a need to apply a computerised segmentation method for future analysis without any assumptions about the object’s topology is made. In general, when performing pregnancy ultrasound scanning, seeking a snapshot with best position or angle of the foetus is often a task done by obstetrician. This snapshot is useful for the obstetrician to locate the crown and the rump of the foetus for specific measurement. In this paper, a computerized segmentation using variational level set algorithm (VLSA) is proposed here. Results showed the variational level set contour evolved well on the low contrast and noise consisting ultrasound image.