Titijaroonroj, Taravichet

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Titijaroonroj, Taravichet
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Titijaroonrog, Taravichet
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taravichet.ti@kmitl.ac.th

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    Item type:Publication,
    An Individual Local Mean-based 2DPCA for Face Recognition under Illumination Effects
    (2019-07-01)
    Hancherngchai, Kangsadan
    ;
    Titijaroonroj, Taravichet
    ;
    Rungrattanaubol, Jaratsri
    Principal component analysis (PCA) is a classical technique in pattern recognition and computer vision. It is one of the most successful techniques for face recognition. The PCA consists of two main steps including (I) covariance matrix calculation and (II) eigenvector and eigenvalue extraction. In case of face recognition, the input image is converted to the vector form before forwarding to covariance matrix computation. Then, the matrix is used to extract the eigenvector and eigenvalue. Two-dimensional PCA (2DPCA) is introduced to reduce high-dimensional problems. The illumination effect problems in the face recognition is still needed to be resolved. In order to improve and solve the problems, this paper proposes an individual local mean-based 2DPCA (ILM-2DPCA), which replaces a single local mean in 2DPCA method. The individual local mean can provide more appropriate mean to each image, which can reduce the illumination effect effectively. The experimental study is set up on dataset Yale face database B+. The results indicate that the proposed method outperforms, based on the accuracy rate, all the baseline methods which are 2DPCA, I-2DPCA, Bi2DPCA and 2D<sup>2</sup>PCA.
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    Regional covariance matrix-based two-dimensional PCA for face recognition
    (2020-01-01)
    Titijaroonroj, Taravichet
    ;
    Hancherngchai, Kangsadan
    ;
    Rungrattanaubol, Jaratsri
    Two-dimensional principal component analysis (2DPCA) is widely used in many applications, especially, face recognition. A key factor to improve the performance of the 2DPCA method comes from the efficiency of the covariance matrix. This paper believes that the effective eigenvector can be extracted when the effective covariance matrix is given. Therefore, the computing covariance matrix is a focus point in this paper. The set of the covariance matrix in the 2DPCA and its extensions is usually represented with a single directional correlation, which is then used to obtain a mean covariance matrix by using the average technique. This causes in obtaining the ineffective eigenvector since the covariance matrix is ineffective. In order to obtain the effective eigenvector, a regional covariance matrix-based on 2DPCA method (RCM-2DPCA) is proposed here. The contribution of this paper consists of two main parts including (i) regional matrix calculation for computing the two directional correlations and (ii) ELSSP conversion for extracting the effective representation of the covariance matrix. The experimental results show that the performance of the proposed method is higher than the baseline methods including 2DPCA, I-2DPCA, Bi2DPCA, 2D2PCA and ILM-2DPCA methods on a basis of three well-known datasets-ORL Face, Yale Face, and Yale Face extended B+ datasets.
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    Modified Scale-Space Analysis in Frequency Domain Based on Adaptive Multiscale Gaussian Filter for Saliency Detection
    (2019-07-01)
    Jaemsiri, Jenjira
    ;
    Titijaroonroj, Taravichet
    ;
    Rungrattanaubol, Jaratsri
    The salient region is an area in the image that is paid attention from a human. It is a distinctive feature from neighbors such as color, shape or pattern. Saliency detection is a model that imitates the human visual system to perceive the scene. It has been widely used in many vision systems. Many papers use the smoothing or suppressing technique to extract a desirable output as so-called saliency map. Even though most of these researches achieve saliency detection based on the filter, the size of the filter is fixed. This leads to a filter ineffective when applying to the whole area of each image. In order to solve this issue, an adaptive multiscale Gaussian filter (MSS) for scale-space analysis in the frequency domain is proposed. The proposed filter is extended from an adaptive median filter which is a powerful method to remove the noise from the input image. This paper proposes the method that offers the appropriate filter to suppress the repeated pattern spectrum of each region in each image before extracting the saliency map. The experimental result shows that the proposed method outperforms the baseline methods, which includes HFT, Itti, SAL, SR and SUN.
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    Improved adaptive spectrum scale-space in frequency domain for saliency detection
    (2020-01-01)
    Titijaroonroj, Taravichet
    ;
    Jaemsiri, Jenjira
    ;
    Rungrattanaubol, Jaratsri
    Saliency detection is a process to find the significant region on the images, which is popular in the image processing area. It has been extended and used in many applications in computer vision systems. Many researches have attempted to propose effective model to detect the saliency area which is corresponding to human perception. Therefore, this research focuses on improving the saliency detection process by proposing the improved adaptive spectrum scale-space (IASSS). The main contributions of the proposed method include (i) scale-and-space Gaussian filter (AS<sup>2</sup>G filter) and (ii) the new method for saliency map selection based on local entropy. Firstly, the AS2G filter is used to suppress the non-saliency amplitude spectrum to extract the saliency map. Then, the best saliency map is selected from the results of the RGB and Lab color images by using the local entropy criteria. Then, the experimental results based on 235 images show that the overall performance of the proposed IASSS method outperforms the baseline methods including Itti, SR, SUN, SAL, HFT, and MSS methods.