Woraratpanya, Kuntpong

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Weraratpanya, Kuntpong

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School of Information Technology

kuntpong.wo@kmitl.ac.th

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Thai traffic sign detection and recognition for driver assistance
(2018-11-05)
Promlainak, Sakan
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Woraratpanya, Kuntpong
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Kuengwong, Jirapat
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Kuroki, Yoshimitsu
Nowadays, driver assistance systems are embedded with some expensive cars, but more importantly, those systems are not able to recognize Thai traffic signs. This paper proposes a Thai traffic sign detection and recognition system. The proposed system is implemented with two main processes: Thai traffic sign detection and recognition. For the former process, a cascade classifier trained with histogram of oriented gradient (HOG) features is used to generate a trained model for a sign detector, and then Viola-Jones cascade detector is used to classify sign and non-sign objects of the input image. For the latter process, a linear support vector machine (SVM) learner trained with HOG features is used to generate the trained model for sign symbol recognition, and then a SVM class prediction is applied for recognizing the HOG features of the detected sign. Based on a real world data-set, the proposed system can correctly detectand recognize Thai traffic signs in near real time.
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A compact optimal learning machine
(2019-01-01)
Sae-Pae, Kanathip
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Woraratpanya, Kuntpong
Artificial neural networks (ANNs) have been developed and applied to a variety of problems, such as pattern recognition, clustering, function approximation, forecasting, optimization, etc. However, existing ANNs have a high computational cost, since their learning methods are mostly based on a parameter tuning approach. Extreme learning machine (ELM) is a state-of-the-art method that generally dramatically reduces the computational cost. An analysis of the ELM method reveals that there are unsolved key factors, including inefficient hidden node construction, redundant hidden nodes, and unstable results. Therefore, we describe a new learning machine based on analytical incremental learning (AIL) in conjunction with principal component analysis (PCA). This learning machine, PCA-AIL, inherited the advantages from the original one and solved the unsolved key factors of ELM, and also extended AIL capability to serve a multiple-output structure. PCA-AIL was implemented with a single-layer feed-forward neural network architecture, used an adaptive weight determination technique to achieve a compact optimal structure and also used objective relations to support multiple output regression tasks. PCA-AIL has two steps: objective relation estimation and multiple optimal hidden node constructions. In the first step, PCA estimated the objective relations from multiple-output residual errors. In the second step, the multiple optimal nodes were obtained from objective relations and added to the model. PCA-AIL was tested with 16 multiple-objective regression datasets. PCA-AIL mostly outperformed other methods (ELM, EM-ELM, CP-ELM, DP-ELM, PCA-ELM, EI-ELM) in terms of fast testing speed-0.0017 second, a compact model-19.9 nodes, an accurate performance-RMSE 0.11261, and a stable result-S.D. of RMSE 0.00911: reported in averaged.
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Text-background decomposition for thai text localization and recognition in natural scenes
(2014-01-12)
Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
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Suttapakti, Ungsumalee
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Boonchukusol, Pimlak
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Titijaroonroj, Taravichet
Thai text localization and recognition in natural scenes is still a grand challenge in current applications. However, the efficiency of recognition rates depends on text localization, i.e., the higher purity of text-background decomposition leads to the higher accuracy rate of character recognition. In order to achieve this purpose, the text-background decomposition methods, namely adaptive boundary clustering (ABC) and n-point boundary clustering (n-PBC), are proposed to improve a precision of text localization. These methods are evaluated by self-en-tropy for purity measure. Based on 300 test images, the experimental results demonstrate that the ABC method achieves the very low self-entropy, i.e., the low self-entropy implies the good decomposition of text and background. Furthermore, based on 8,077 characters in natural scene test images, the ABC method helps increase the precision of text localization and improves the accuracy rate of character recognition, when compared to the conventional methods.
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Iteration-free Bi-dimensional empirical mode decomposition and its application
(2017-09-01)
Titijaroonroj, Taravichet
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Woraratpanya, Kuntpong
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.
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Analytical incremental learning: Fast constructive learning method for neural network
(2016-01-01)
Alfarozi, Syukron Abu Ishaq
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Setiawan, Noor Akhmad
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Adji, Teguh Bharata
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Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
Extreme learning machine (ELM) is a fast learning algorithm for single hidden layer feed-forward neural network (SLFN) based on random input weights which usually requires large number of hidden nodes. Recently, novel constructive and destructive parsimonious (CP and DP)-ELM which provide the effectiveness generalization and compact hidden nodes have been proposed. However, the performance might be unstable due to the randomization either in ordinary ELM or CP and DP-ELM. In this study, analytical incremental learning (AIL) algorithm is proposed in which all weights of neural network are calculated analytically without any randomization. The hidden nodes of AIL are incrementally generated based on residual error using least square (LS) method. The results show the effectiveness of AIL which has not only smallest number of hidden nodes and more stable but also good generalization than those of ELM, CP and DP-ELM based on seven benchmark data sets evaluation.
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Improved Thai text detection from natural scenes
(2013-01-01)
Woraratpanya, Kuntpong
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Boonchukusol, Pimlak
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Kuroki, Yoshimitsu
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Kato, Yasushi
Thai text detection from natural scenes is still a challenging task for language translation applications, since there are many unsolved issues. Furthermore, the existing related works cannot completely detect Thai text. The main reason is that Thai text layout has vowels and tonal marks that differ from other languages. This paper proposes an approach to detect Thai text from natural scenes. The approach consists of two main procedures. (i) Fast boundary clustering algorithm decomposes scene features into multilayers, so that it is faster and easier to analyze Thai text characters. (ii) Modified connected component analysis method is applied to such scene features in order to detect Thai text boundaries. Based on 150 test images with 4,920 characters, the experimental results demonstrate that the proposed approach achieves the high average precision and recall, 0.80 and 0.90. © 2013 IEEE.
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Offline handwritten signature recognition using adaptive variance reduction
(2015-01-01)
Sa-Ardship, Ruangroj
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Woraratpanya, Kuntpong
Although offline handwritten signature recognition has been continually researched, it still requires an improvement of recognition rate. Most of existing techniques focus on feature extraction to improve their performance. This paper proposes an alternative way to increase the recognition rate by analyzing an important characteristic of input information, namely variability of signatures. The proposed method is based on the hypothesis; reducing the variability of signatures leads to boost up the recognition rate. Therefore, the variance reduction technique is applied to normalize offline handwritten signatures by means of an adaptive dilation operator. Then the variability of signatures is analyzed in terms of coefficient of variation (CV). The optimal CV is obtained and used to be a threshold limit value for the acceptable variance reduction. Based on 5,739 signature samples with 140 classes, the experimental results show that the adaptive variance reduction procedure helps improve the recognition rate when compared to the traditional schemes without adaptive variance reduction, including histogram of gradient (HOG) and pyramid histogram of gradient (PHOG) techniques.
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Hinge loss projection for classification
(2016-01-01)
Alfarozi, Syukron Abu Ishaq
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Woraratpanya, Kuntpong
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Pasupa, Kitsuchart
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Sugimoto, Masanori
Hinge loss is one-sided function which gives optimal solution than that of squared error (SE) loss function in case of classification. It allows data points which have a value greater than 1 and less than −1 for positive and negative classes, respectively. These have zero contribution to hinge function. However, in the most classification tasks, least square (LS) method such as ridge regression uses SE instead of hinge function. In this paper, a simple projection method is used to minimize hinge loss function through LS methods. We modify the ridge regression and its kernel based version i.e. kernel ridge regression so that it can adopt to hinge function instead of using SE in case of classification problem. The results show the effectiveness of hinge loss projection method especially on imbalanced data sets in terms of geometric mean (GM).
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A hybrid of fractal code descriptor and harmonic pattern generator for improving speech recognition of different sampling rates
(2018-01-01)
Hokking, Rattaphon
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Woraratpanya, Kuntpong
Currently, the different sampling rate for speech recognition is a grand challenge due to supporting applications of divergent platform devices, such as mobile device interaction, interactive voice response system, voice search, voice dictation and voice identification. Furthermore, such applications require efficient speech features to represent input signals. However, the different sampling rates of speech signals lead to the different features. This phenomenon comes from speech harmonic signal lost. It becomes a key factor that decreases the speech recognition rate. Therefore, this paper proposes a hybrid of fractal code descriptor and harmonic pattern generator to convert all different sampling rate signals to standardized signals. In this method, an independent resolution property of fractal code descriptor is applied to training and testing speech signals. Then, the pitches of such signals are used to recover harmonic pattern of lost signals. This method can effectively reconstruct speech signals at any sampling rates. When its performance is evaluated with AN4 corpus of CMU Sphinx speech recognition engine, the experimental results show that the proposed method can significantly improve the speech recognition rate, even if the sampling rate of testing speeches differs from that of training speeches.
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Modified differential box-counting method using weighted triangle-box partition
(2015-01-01)
Nunsong, Walairach
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Woraratpanya, Kuntpong
Differential box-counting (DBC) is one of the commonly used methods to estimate fractal dimension (FD) for gray scale images. It has been successfully applied in many applications such as image segmentation, pattern recognition, texture analysis and medical signal analysis. However, the accuracy improvement of FD estimation is still a grand challenge. This paper proposes a modified differential box-counting method using weighted triangle-box partition (MDBC) to reduce the estimation error caused by an undercounting problem. The proposed method is derived from two assumptions: (i) increasing the precision of box-counts by using unequally triangle box partition, and (ii) weighting the box-counts in proportion to the size of triangle-box partition. Based on these assumptions, on each grid a square box is divided into four asymmetric triangle-box patterns. Each pattern is calculated the box-counts by a weighted box-counting technique. The maximum number of box-counts represents the better estimation. In this way, the experimental results show that MDBC outperforms the baseline methods in terms of fitting error. Furthermore, the proposed method applies to finger-knuckle-print recognition in order to test its efficiency. The results illustrate that it significantly enhances the recognition rate when compared with the conventional differential box-counting (DBC) and improved triangle box-counting in combination with DBC (ITBC-DBC) methods.

Woraratpanya, Kuntpong

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