Wattanacheep, Bhattarabhorn

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Wattanacheep, Bhattarabhorn
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bhattarabhorn.wa@kmitl.ac.th

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    A CNN-BASED MULTI-MODEL ENSEMBLE METHOD FOR INDOOR AND OUTDOOR MULTI-VIEW STEREO RECONSTRUCTION
    (2022-01-01)
    Wattanacheep, Bhattarabhorn
    ;
    Chitsobhuk, Orachat
    Camera poses estimation is a critical process that ensures the success of Three-Dimensional (3D) modelling. We present a Convolutional Neural Network (CNN)-based multi-model ensemble method for indoor and outdoor multi-view stereo reconstruction capable of learning across multiple domains, including images from both indoor and outdoor environments. Each domain’s images have distinct properties and shooting view-points, which leads to difficulty in efficient learning such a large difference and requires large amount of computational resources. In order to reduce complexity of the end-to-end single model, the proposed model is divided into multiple learning agents consisting of domain-specific agents and domain relationship agent. The domain-specific agent is trained independently on its own set of unique image characteristics, for example, one for indoor datasets and another for outdoor datasets. The domain relationship agent then ensembles and analyzes the multiple domain features and finalizes the estimation. In terms of average root mean square error, we compare the performance of the combined domain single model with the suggested ensemble CNN model. The experimental results indicate that the proposed model outperforms the others, with rotation and translation prediction errors of 0.112012266.
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    Vision-Based Human Movement Matching for Muay Thai Training Support System
    (2024-01-01)
    Wangsiripitak, Somkiat
    ;
    Pipatbowornkul, Napong
    ;
    Wedsuwan, Nawin
    ;
    Wattanacheep, Bhattarabhorn
    An algorithm is proposed to evaluate Muay Thai trainees based on body movement matching with the trainer, aiming to support Muay Thai training. Skeletons of the trainee in image sequences captured by a monocular camera are extracted by a pre-trained deep-learning model. They are then synchronized with those of the trainer by matching keyframes and subsequently other image frames between both sequences using dynamic time warping. In each matched frame, the skeleton lines (bones) are compared by measuring the angle deviation of each bone from the trainee to the corresponding bone of the trainer. The similarity score of each bone pair is computed based on a cosine similarity metric. Simultaneously, a predefined upper-value limit of the maximum expected difference in bone comparison is applied to enhance the difference in similarity scores between excellent and underperformed postures, making the evaluation results more distinctive. Each matched frame is then scored by weighted averaging of bone scores, allowing the evaluation to be adaptively focused on significant parts of the body according to movement types. The overall score of the sequence is an average over all frames. Experimental results demonstrate that the proposed scoring method is flexible for use with an off-the-shelf camera and adaptive to a marking scheme that often changes with training strategies and patterns. Prior matching of keyframes reduces the mismatching of similar but non-corresponding frames. The score is also normalized to penalize misoriented bones, resulting in even lower scores for trainees with less skill.
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    Camera Pose Estimation using CNN
    (2020-08-23)
    Wattanacheep, Bhattarabhorn
    ;
    Chitsobhuk, Orachat
    Estimating camera pose is a significant process, which assures the success of the 3D modeling performance. This research presents a camera pose estimation using convolutional neural network (CNN) to transfer learning from pre-trained deep learning VGG19 model in order to extract features from a single image using several datasets captured in indoor and outdoor environments with diverse perspectives and photographic styles. Due to the large dimensions of the extracted features, Latent Semantic Analysis (LSA) are introduced prior to the CNN input. Then, the CNN is trained to predict the camera views and translations. The prediction performance is measured in terms of average mean square errors and compared to the reference techniques. As a result, the regression estimation of the proposed CNN model outperforms the others with average 0.24 degrees rotation error and 0.26 m. translation errors.
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    Thai Stock Chart Pattern via Deep Learning-Based Multi-Indicator Object Detection
    (2025-01-01)
    Wattanacheep, Bhattarabhorn
    Currently, stock market investment strategies are categorized into fundamental analysis and technical analysis. Technical analysis seeks to understand market behavior by examining historical price and volume data, employing methods such as price pattern analysis, candlestick interpretation, and support-resistance levels. Among these, one of the particularly interesting approaches is chart pattern recognition, which consists of bullish and bearish patterns that may indicate potential price reversals to support investment decisions. However, the complexity and uncertainty of price movements, coupled with the presence of multiple technical indicators on a single chart, pose significant challenges to human pattern recognition. This study proposes a deep learning-based object detection framework to automatically recognize bullish and bearish patterns. Daily stock chart images from the Stock Exchange of Thailand were collected, incorporating price graphs alongside RSI, MACD, and STO indicators. The labeled images were used to train and validate models through K-fold cross-validation, evaluating seven state-of-the-art detection architectures using the mAP@50 metric. Experimental results revealed that YOLOX achieved the best performance, with an average mAP@50 of 0.887 and a variance of 0.057, particularly excelling on charts combining price graphs with MACD indicators. These findings suggest that deep learning models may assist technical analysis by supporting more systematic chart pattern recognition.
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    Automated Verification of English Proficiency Test Scores Using OCR for Graduation Qualification
    (2025-01-01)
    Titijaroonroj, Taravichet
    ;
    Maliwan, Thitiwut
    ;
    Jaimetha, Nattakamon
    ;
    Wattanacheep, Bhattarabhorn
    An algorithm is proposed to verify English proficiency scores using OCR for graduation qualification, aiming to reduce errors and workload in the current manual verification process of officer. Score reports in image or PDF format, including TOEIC and KMITL-TEP, are processed using two deep learning-based document classifiers that identify the test type and score type prior to OCR execution. Preprocessing techniques such as noise reduction, contrast enhancement, and skew correction are applied to improve OCR accuracy. Three OCR models-Tesseract, TrOCR, and EasyOCR-are evaluated for text extraction performance. The extracted textual data are then converted into structured JSON, enabling automated rule-based comparison against graduation criteria. Evaluation performance is measured using Accuracy, F1-Score, Character Error Rate (CER), and Word Error Rate (WER). Experimental results show that the proposed system achieves 99% accuracy, demonstrating both high reliability and adaptability to institutional scoring standards. The integration of OCR significantly reduces processing time while maintaining flexibility to accommodate future changes in assessment policies.
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    An OCR-Based Framework for Automated Verification of Thai and English Academic Transcripts
    (2025-01-01)
    Wattanacheep, Bhattarabhorn
    ;
    Komkris, Pannathorn
    ;
    Heymun, Issariyapon
    ;
    Srimontrisanga, Wongsawan
    Academic transcripts are essential documents for employment and higher education but remain susceptible to forgery and manipulation, creating a need for efficient and reliable verification methods. Traditional verification is often time-consuming due to the structural complexity of transcripts and the challenges of extracting information from PDF and image formats. This study presents an OCR-based framework for automated transcript verification that integrates preprocessing and postprocessing techniques to enhance data extraction quality. English transcripts were evaluated using OCR models PaddleOCR, Tesseract, and EasyOCR, while Thai transcripts were assessed with Tesseract and EasyOCR. Experimental results demonstrate that preprocessing substantially improves extraction accuracy and that postprocessing further refines the outputs. Among the evaluated models, PaddleOCR achieved the highest performance on English transcripts with an overall accuracy of 81.87%, whereas Tesseract yielded the best accuracy for Thai transcripts at 81.11%. These findings underscore the effectiveness of combining OCR with tailored preprocessing and postprocessing strategies to support reliable and efficient transcript verification in academic settings.