Woraratpanya, Kuntpong

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Alternative Name

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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ADGAS: An Advanced Data Generation for Anomalous Signals
(2021-01-01)
Chalongvorachai, Thasorn
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Woraratpanya, Kuntpong
Anomaly detection using deep learning approaches is still challenging, especially in the case of data limitations. A small number of samples for training deep learning models typically result in poor performance of detection and classification. Previously, data augmentation was one of the methods used to solve this problem. The data augmentation with rotation, permutation, time warping, and their combination can increase the performance of anomaly classification. However, this method is still limited and does not guarantee that the generated output data have adequate varieties and keep original characteristics of data. Our recent work, data augmentation and generation for anomalous time series signals (DAGAT) was proposed to expand the space of possible augmented data by implementing vanilla augmentation on various domains in conjunction with variational autoencoder (VAE). Nonetheless, the DAGAT still has barriers, which are an uncontrollable number of target results, a missed opportunity of integrating multiple augmentation characteristics in latent space, and a possibility of including any bad data for training in VAE. To overcome these limitations, this paper proposed an advanced data generation for anomalous signals (ADGAS). By focusing on the quality of generated data, one more quality classifier (QC) was added as a prepossessing step of VAE. In this way, the experimental results showed that convolutional neural networks (CNNs), used as a performance tester, trained with the generated datasets of ADGAS achieved better accuracy in classifying anomalous events when compared to models trained with a combination of rotation, permutation, and time warping data augmentation methods.
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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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Vision Transformer with Fractal Dimension Transformation: Effects of Resolution and Patch Size
(2025-01-01)
Ngamkham, Woramat
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Woraratpanya, Kuntpong
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Kuroki, Yoshimitsu
Vision Transformer (ViT) achieves strong performance in computer vision but requires substantial computational resources, particularly with high-resolution data. A key challenge lies in the quadratic complexity of self-attention with respect to the number of image patches, which is jointly determined by input size and patch size. Conventional resizing is a common strategy to reduce resolution and thus the number of patches, but it risks discarding structural details that may be important for prediction. To address this issue, this study investigates how input size, patch size, and dimensionality reduction influence ViT training time and prediction accuracy. Using the NIH Chest X-ray dataset, we compared two preprocessing methods: conventional resizing and a Fractal Dimension (FD)-based transformation. Results show that the FD-based method consistently reduced training time across all settings, demonstrating its effectiveness in lowering computational costs. In terms of accuracy, conventional resizing generally performed slightly better overall; however, the differences were not uniform, as smaller patches improved AUROC mainly at higher resolutions but not consistently at lower ones. These findings highlight a tradeoff between efficiency and accuracy, positioning FD-based representations as a practical complement to conventional resizing when computational resources are limited.
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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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Advancing GAN Evaluation: The Advanced Mahalanobis Distance Learning Metric for Realistic Car Damage Image Assessment
(2026-01-01)
Kyu, Phyu Mar
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Woraratpanya, Kuntpong
Generative Adversarial Networks (GANs) have demonstrated remarkable capability in synthesizing high-quality images from limited data, addressing challenges of data scarcity and diversity in deep learning (DL) training. This is particularly valuable for car damage classification, where real-world datasets are often limited. To mitigate this, we created a custom damaged-undamaged car dataset for training GAN models and generating realistic car damage images. However, evaluating the per-image realism of GAN-generated images remains challenging. Standard GAN metrics, such as Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Score (IS), provide dataset-level scores but do not assess individual image quality. Meanwhile, Image Quality Assessment (IQA) metrics require reference images, rendering them unsuitable for reference-free scenarios, particularly in unpaired GAN-generated data. To address these limitations—including the practical failure of standard Mahalanobis Distance Learning (MDL) on small or high-dimensional datasets due to non-invertible covariance matrices—we propose Advanced Mahalanobis Distance Learning (AMDL), which incorporates adaptive regularization and pseudo-inverse refinement on deep feature embeddings from pre-trained CNNs. AMDL enables stable and reliable per-image realism assessment under covariance matrix instability, without requiring large datasets or ground-truth references. Our comprehensive evaluation framework involves three procedures: (1) dataset-level evaluation of four GAN models using standard GAN metrics, (2) per-image realism assessment with AMDL, and (3) classifier-based validation with CNN and Vision Transformer (ViT) models (with vs. without AMDL). Experimental results show that AMDL provides precise per-image realism assessment, outperforms existing GAN metrics across datasets, and offers a practical solution for evaluating unpaired GAN-generated images in car damage classification.

Woraratpanya, Kuntpong

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