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Author: search.filters.author.Watchareeruetai, UkritSubject: search.filters.subject.image processing

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    Leaf identification using apical and basal features
    (2016-03-23)
    Rojanamontien, Mathara
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    Sihanatkathakul, Poomkawin
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    Piemkaroonwong, Nicha
    ;
    Kamales, Supanat
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    Watchareeruetai, Ukrit
    This paper proposes a method that extracts local features, i.e., angle patterns, around the apex and base of a leaf. The proposed method only requires leaf contour and the location of apex and base as inputs. Starting from an origin point, which can be either the apex or base, the contour is tracked in two directions, i.e., leftward and rightward, and then sampled at five different distances from the origin point. The angle formed by the origin and two sampled points, at each distance, is then calculated. Altogether, 10 angle features, five from the apex and five from the base, are obtained. These features are invariant to translation, rotation, and scaling. In addition, this paper also aims to measure the effectiveness of the proposed apical and basal features. In the experiment, two sets of features are compared. The first set includes 12 shape descriptors while the second set includes not only the 12 shape descriptors but also the proposed features. By using support vector machine as a classifier, leaf identification has been done by using the two sets of features. Experimental results indicate that the use of apical and basal features can significantly improve the accuracy of leaf identification.
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    Detection of leaf apex and base by using contour and symmetry analysis
    (2016-02-08)
    Watchareeruetai, Ukrit
    ;
    Ditthawibun, Matchima
    ;
    Phanjan, Kriangkrai
    This paper proposes a contour analysis method for automatic detection of leaf apex and base. The contour is investigated to determine the optimal pair of points that are possibly the apex and base. The results are then affirmed using a leaf symmetry analysis and post-processing. Experimental results show that the proposed method can detect apex and base with an accuracy of 85%, given a 5 mm distance error.
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    Fat detection algorithm for liver biopsy images
    (2014-10-15)
    Sumitpaibul, Pawesuda
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    Damrongphithakkul, Anurak
    ;
    Watchareeruetai, Ukrit
    This paper presents an image-processing-based method for analyzing the fat proportion in liver biopsy images. Firstly, the proposed method extracts the area of candidate fat blobs, as well as the background area, from the input image. Then the features of each candidate blobs will be computed. Finally a classification technique called k-nearest neighbors is used to classify each candidate blob if it is fat. Experimental results show that the proposed method can detect fat in the liver biopsy images with the accuracy of 97.52%.