Gullayanon, Rutchanee

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Gullayanon, Rutchanee
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rutchanee.gu@kmitl.ac.th

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    Item type:Publication,
    Fast tracking algorithm for designed marker
    (2016-01-01)
    Siriteerakul, Teera
    ;
    Gullayanon, Rutchanee
    Robust and real-time object tracking is a vital part of many application including the navigation of mobile robot and unmanned aerial vehicle. In such environment, the computational power and battery are limited. Hence, most of the state of the art algorithms will not be able to display their full potential. Thus, we propose an improved tracking algorithm, with a designed marker, to perform robustly in real-time in complex environments. Our algorithm based on a simple binarization and finding candidate connected components. Then, with physical and geometric property of the designed marker, we can filter out all the other components until we only have the marker. This scheme has been tested on Raspberry PI, a limited-power computing unit, and demonstrate sufficient robustness and speed.
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    Robust tracking algorithm with designed marker for limited-power computer
    (2016-03-23)
    Siriteerakul, Teera
    ;
    Gullayanon, Rutchanee
    Object tracking is an important part of many visual systems. The robot navigation task, for example, does not only require a robust and accurate tracking, but also need the tracking module to perform in real-time. This may not be a problem with hi-end robotic system with powerful computing unit. However, in the case of mobile robot where the computing and the battery power are limited, there are only a few tracking methods available. Thus, we propose an algorithm, coupled with a designed marker, for fast and stable tracking. The proposed algorithm utilizes robustness of Hough Circle Transform to detect candidate locations for the marker. Then, all the detected locations are filtered by known properties of the designed marker until we have the correct location. An experiment done on Raspberry PI shows that the marker can be detected robustly with sufficient speed.
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    Character classification framework based on support vector machine and k-nearest neighbour schemes
    (2016-02-01)
    Siriteerakul, Teera
    ;
    Boonjing, Veera
    ;
    Gullayanon, Rutchanee
    The problem of Thai character classification can be difficult because of the large number of characters and the similarity in the shape of many characters. While previous work combined different fonts to build their classifier, this paper proposes a framework based on support vector machine (SVM) and k-NN schemes to exploit characteristics of each font separately. In this framework, each font is used to train an SVM separately. With the trained SVMs, a vector of predicted values can be produced for any input image. Then a class label of the input image can be found by a k-NN based scheme. The proposed framework performs well with familiar fonts while providing an acceptable performance on unfamiliar fonts.