Titijaroonroj, Taravichet
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Titijaroonroj, Taravichet
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Titijaroonrog, Taravichet
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taravichet.ti@kmitl.ac.th
10 results
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Item type:Publication, Text-background decomposition for thai text localization and recognition in natural scenes(2014-01-12); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Iteration-free Bi-dimensional empirical mode decomposition and its application(2017-09-01); powerful methods for decomposing non-linear and nonstationary signals without a prior function. It can be applied in many applications such as feature extraction, image compression, and image filtering. Although modified BEMDs are proposed in several approaches, computational cost and quality of their bi-dimensional intrinsic mode function (BIMF) still require an improvement. In this paper, an iteration-free computation method for bi-dimensional empirical mode decomposition, called iBEMD, is proposed. The locally partial correlation for principal component analysis (LPC-PCA) is a novel technique to extract BIMFs from an original signal without using extrema detection. This dramatically reduces the computation time. The LPC-PCA technique also enhances the quality of BIMFs by reducing artifacts. The experimental results, when compared with state-of-The-Art methods, show that the proposed iBEMD method can achieve the faster computation of BIMF extraction and the higher quality of BIMF image. Furthermore, the iBEMD method can clearly remove an illumination component of nature scene images under illumination change, thereby improving the performance of text localization and recognition. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Individual Local Mean-based 2DPCA for Face Recognition under Illumination Effects(2019-07-01); Rungrattanaubol, JaratsriPrincipal 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An improved 2DPCA for face recognition under illumination effects(2015-01-01); ; Varakulsiripunth, RuttikornPrincipal component analysis (PCA) is one of the successful techniques for applying to face recognition, but its challenge still remains for solving an illumination effect condition. This paper proposes an improved 2DPCA (I-2DPCA) for overwhelming the illumination effect in face recognition. The proposed method is based on two assumptions. The first assumption is to create the covariance matrix that can effectively decompose the components of illumination effects from the eigenfaces. This avoids the illumination effect problem. The second assumption is to select the suitable eigenvectors that can significantly improve the recognition rate. Based on the Extended Yale Face Database B+ containing 60 illumination conditions, the experimental results show that not only does the proposed method decrease the computing time, but it also improves the recognition rate up to 95.93%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Seven Segment Display Detection and Recognition using Predefined HSV Color Slicing Technique(2019-07-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Printed Thai character recognition using standard descriptor(2013-01-01); The various font-types, font-sizes, and font-styles have a great impact on recognition performance of optical character recognition (OCR) systems. This becomes a grand challenge for recognition improvement. In order to enhance the performance, this paper proposes the printed Thai character recognition using a standard descriptor. The descriptor construction consists of two principal phases-preprocessing and feature extraction. In the former phase, the preprocessing provides a standard form for each character image. In the latter phase, the singular value decomposition (SVD) is applied to all font-type, fontsize, and font-style character images to extract features. Then the standard descriptor is constructed from the suitable order selection of the SVD feature decomposition. Finally, the projection matrix technique is applied to the recognition phase in order to measure the cosine similarity between the standard descriptor and test set. The experimental results show that the proposed method achieves a high recognition rate and is invariant to font-types, font-sizes, and font-styles. © 2013 Springer-Verlag. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Texture analysis assessment for images(2017-02-23); ; Kuroki, YoshimitsuCommonly, the existing metrics such as mean square error (MSE), peak signal-To-noise ratio (PSNR), quality index (QI), structural similarity index metric (SSIM), and quality index based on local variance (QILV) use the image intensity-based statistics approach to assess the quality of distorted images. These metrics are successful in discriminating the quality of distorted images, such as de-noising, JPEG compressed, and blur images. However, they are unsuccessful in discriminating the quality of channel decomposition images. Therefore, this paper proposes the texture analysis assessment (TAA) to measure the quality of both normally distorted images and channel decomposition images. The proposed metric uses image intensity statistics in conjunction with texture analysis for quality discrimination of slightly different distorted and channel decomposition images. The texture analysis based on edge orientation is an important part employed to measure precise image errors. The experimental results illustrate that the TAA metric can evidently discriminate the quality of normally distorted images and channel decomposition images, when compared with state-of-The-Art metrics. Furthermore, the perceived visual quality and the quality value of TAA are corresponding; the lower visual quality human-eye perceives, the lower quality value TAA measures, and vice versa. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Image Denoising Based on Structural BIMF(2018-08-21); Image denoising is an important process for image analysis. It is always a pre-processing step before feeding its result to the next process. Although many papers based on statistical filter are presented for noise reduction, those still need to improve the quality of image. Bidimensional empirical mode decomposition (BEMD) is an adaptive image analysis method without a prior function for non-linear and non-stationary images in many applications. One of them is image denoising. The BEMD method can reduce the information loss by separating the noised and fundamental components into different bidimensional intrinsic mode function (BIMF) components. This makes it to be effective and flexible method. Therefore, this paper proposes image denoising based on structural BIMF. The proposed method not only removes the noise from noised image but also retrieves the main structure from noised image. Based on 44 noised images, the experimental results demonstrate that the performance of the proposed method outperforms that of baseline methods in terms of image quality assessments: PSNR, Qi, and TAA. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modified Scale-Space Analysis in Frequency Domain Based on Adaptive Multiscale Gaussian Filter for Saliency Detection(2019-07-01); Rungrattanaubol, JaratsriThe 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Adaptive Histogram of Oriented Gradient for Printed Thai Character Recognition(2014-01-01); A similarity of printed Thai characters is a grand challenge of optical character recognition (OCR), especially in case of a variety of font types, sizes, and styles. This paper proposes an effective feature extraction, adaptive histogram of oriented gradient (AHOG), for overcoming the character similarity. The proposed method improves the conventional histogram of oriented gradient (HOG) in two principal phases, which are (i) adaptive partition for gradient images and (ii) adaptive binning for oriented histograms. The former is implemented with quadtree partition based on gradient image variance so as to provide for an effective local feature extraction. The later is implemented with non-uniform mapping technique, so that the AHOG descriptor can be constructed with minimal errors. Based on 59,408 single character images equally divided into training and testing samples, the experimental results show that the AHOG method outperforms the conventional HOG and state-of-the-art methods, including scale space histogram of oriented gradient (SSHOG), pyramid histogram of oriented gradient (PHOG), multilevel histogram of oriented gradient (MHOG), and HOG column encoding algorithm (HOG-Column). © Springer International Publishing Switzerland 2014.