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Engineering educational research section Information and Communication Technology Program |
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THI THI ZIN
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Degree 【 display / non-display 】
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Doctor of Engineering ( 2007.3 Osaka City University )
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Master of Engineering ( 2004.3 Osaka City University )
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Master of Information Science ( 1999.5 University of Computer Studies, Yangon (UCSY) )
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B.Sc (Hons) (Mathematics) ( 1995.5 University of Yangon (UY) )
Research Interests 【 display / non-display 】
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工場での作業の見える化
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高度な画像処理技術やAI技術を活用した 研究開発
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24-hour monitoring system for the elderly to support independent living
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ICT Farm Monitoring System
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Perceptual information processing
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Image Processing and Its Application
Research Areas 【 display / non-display 】
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Informatics / Perceptual information processing / Image Processing
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Informatics / Database
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Life Science / Animal production science
Papers 【 display / non-display 】
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A STUDY ON MACHINE LEARNING APPROACHES FOR PREDICTING FETAL PH LEVEL USING FETAL HEART RATE VARIABILITY Reviewed International journal
Cho Nilar Phyo, Tunn Cho Lwin, Pyae Phyo Kyaw, E. Kino, T. Ikenoue, Pyke Tin, Thi Thi Zin
ICIC Express Letters Part B Applications 16 ( 8 ) 879 - 886 2025.8
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Icic Express Letters Part B Applications
Fetal well-being monitoring system is essential for ensuring healthy labor outcomes. One of the non-invasive methods for assessing fetal health during labor and delivery is by analyzing fetal heart rate variability (FHRV), which can be used to predict fetal pH levels. This study compares different machine learning approaches for predicting fetal pH levels based on FHRV data collected during labor and delivery. The dataset used in this study includes FHRV signals together with corresponding umbilical cord blood gas measurements such as pH, which are used to train and evaluate the models. This study applies several machine learning algorithms and evaluates their performance using key metrics such as sensitivity, specificity, precision, F1-score, and accuracy. These metrics help to determine which model is the most accurate predicting fetal pH levels based on FHRV characteristics. The results reveal that the support vector machine (SVM) model outperforms with the accuracy of 81.67%, better than the other algorithms in predicting fetal pH levels. The findings of this study aim to contribute to the development of more reliable and accurate prediction models for assessing fetal well-being during labor, enhanced clinical decision-making, allowing for timely interventions and improved healthy labor outcomes for both the mother and the baby.
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Automatic cattle identification system based on color point cloud using hybrid PointNet++ Siamese network Reviewed International journal
Pyae Phyo Kyaw, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
Scientific reports 15 ( 21938 (2025) ) 21938 2025.7
Authorship：Last author, Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Scientific Reports
Cattle health monitoring and management systems are essential for farmers and veterinarians, as traditional manual health checks can be time-consuming and labor-intensive. A critical aspect of such systems is accurate cattle identification, which enables effective health monitoring. Existing 2D vision-based identification methods have demonstrated promising results; however, their performance is often compromised by environmental factors, variations in cattle texture, and noise. Moreover, these approaches require model retraining to recognize newly introduced cattle, limiting their adaptability in dynamic farm environments. To overcome these challenges, this study presents a novel cattle identification system based on color point clouds captured using RGB-D cameras. The proposed approach employs a hybrid detection method that first applies a 2D depth image detection model before converting the detected region into a color point cloud, allowing for robust feature extraction. A customized lightweight tracking approach is implemented, leveraging Intersection over Union (IoU)-based bounding box matching and mask size analysis to consistently track individual cattle across frames. The identification framework is built upon a hybrid PointNet ++ Siamese Network trained with a triplet loss function, ensuring the extraction of discriminative features for accurate cattle identification. By comparing extracted features against a pre-stored database, the system successfully predicts cattle IDs without requiring model retraining. The proposed method was evaluated on a dataset consisting predominantly of Holstein cow along with a few Jersey cows, achieving an average identification accuracy of 99.55% over a 13-day testing period. Notably, the system can successfully detect and identify unknown cattle without requiring model retraining. This cattle identification research aims to integrate the comprehensive cattle health monitoring system, encompassing lameness detection, body condition score evaluation, and weight estimation, all based on point cloud data and deep learning techniques.
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パイ テイン, 相川 勝, 小林 郁雄, ティ ティ ズイン
Scientific Reports 15 21938 2025.7
Language：English Publishing type：Research paper (scientific journal) Publisher：Springer Science and Business Media LLC
Cattle health monitoring and management systems are essential for farmers and veterinarians, as traditional manual health checks can be time-consuming and labor-intensive. A critical aspect of such systems is accurate cattle identification, which enables effective health monitoring. Existing 2D vision-based identification methods have demonstrated promising results; however, their performance is often compromised by environmental factors, variations in cattle texture, and noise. Moreover, these approaches require model retraining to recognize newly introduced cattle, limiting their adaptability in dynamic farm environments. To overcome these challenges, this study presents a novel cattle identification system based on color point clouds captured using RGB-D cameras. The proposed approach employs a hybrid detection method that first applies a 2D depth image detection model before converting the detected region into a color point cloud, allowing for robust feature extraction. A customized lightweight tracking approach is implemented, leveraging Intersection over Union (IoU)-based bounding box matching and mask size analysis to consistently track individual cattle across frames. The identification framework is built upon a hybrid PointNet ++ Siamese Network trained with a triplet loss function, ensuring the extraction of discriminative features for accurate cattle identification. By comparing extracted features against a pre-stored database, the system successfully predicts cattle IDs without requiring model retraining. The proposed method was evaluated on a dataset consisting predominantly of Holstein cow along with a few Jersey cows, achieving an average identification accuracy of 99.55% over a 13-day testing period. Notably, the system can successfully detect and identify unknown cattle without requiring model retraining. This cattle identification research aims to integrate the comprehensive cattle health monitoring system, encompassing lameness detection, body condition score evaluation, and weight estimation, all based on point cloud data and deep learning techniques.
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A Study on the Analysis and Classification of Gait States Using Keypoint Information Reviewed International journal
Ryusei Tanno, Thi Thi Zin and Cho Nilar Phyo
ICIC Express Letters 19 ( 6 ) 677 - 684 2025.6
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：ICIC Express Letters
In Japan, 29.0% of the population is elderly, and as this demographic grows, the need for care increases, straining healthcare facilities. Mobility issues, especially falls, significantly contribute to this demand. This study aims to provide a non-contact, accessible method for quantifying gait states in elderly individuals using image processing technology. The experiment, conducted at a commercial facility in Higashi-Osaka City, involved capturing walking sequences from two viewpoints with RGB cameras. Using OpenPose to extract skeletal keypoints, walking balance and kyphosis were evaluated. The angle between the neck and hips served as an indicator for balance, classified as “Normal”, “Warning”, or “Danger”, while kyphosis was classified as “Normal”, “Mild”, or “Severe”. Results showed a correlation between age and balance decline, with older individuals having more “Danger” classifications. Kyphosis was also accurately identified through visual posture comparison.
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Advanced Predictive Analytics for Fetal Heart Rate Variability Using Digital Twin Integration. Reviewed International journal
Tunn Cho Lwin, Thi Thi Zin, Pyke Tin, E. Kino, T. Ikenoue
Sensors (Basel, Switzerland) 25 ( 5 ) 2025.2
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Sensors
Fetal heart rate variability (FHRV) is a critical indicator of fetal well-being and autonomic nervous system development during labor. Traditional monitoring methods often provide limited insights, potentially leading to delayed interventions and suboptimal outcomes. This study proposes an advanced predictive analytics approach by integrating approximate entropy analysis with a hidden Markov model (HMM) within a digital twin framework to enhance real-time fetal monitoring. We utilized a dataset of 469 fetal electrocardiogram (ECG) recordings, each exceeding one hour in duration, to ensure sufficient temporal information for reliable modeling. The FHRV data were preprocessed and partitioned into parasympathetic and sympathetic components based on downward and non-downward beat detection. Approximate entropy was calculated to quantify the complexity of FHRV patterns, revealing significant correlations with umbilical cord blood gas parameters, particularly pH levels. The HMM was developed with four hidden states representing discrete pH levels and eight observed states derived from FHRV data. By employing the Baum–Welch and Viterbi algorithms for training and decoding, respectively, the model effectively captured temporal dependencies and provided early predictions of the fetal acid–base status. Experimental results demonstrated that the model achieved 85% training and 79% testing accuracy on the balanced dataset distribution, improving from 78% and 71% on the imbalanced dataset. The integration of this predictive model into a digital twin framework offers significant benefits for timely clinical interventions, potentially improving prenatal outcomes.
DOI： 10.3390/s25051469
Books 【 display / non-display 】
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Lecture Notes in Electrical Engineering 1322 Reviewed International journal
（ Role： Joint author）
2025.2 （ ISBN:978-981-96-1534-6 ）
Total pages：412 Language：English Book type：Scholarly book
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Lecture Notes in Electrical Engineering (LNEE, volume 1321) Reviewed International journal
（ Role： Joint author）
2025.2 （ ISBN:978-981-96-1530-8 ）
Total pages：528 Language：English Book type：Scholarly book
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Big Data Analysis and Deep Learning Applications: Proceedings of the First International Conference on Big Data Analysis and Deep Learning (Advances in Intelligent Systems and Computing Book 744)
Thi Thi Zin (Editor), Jerry Chun-Wei Lin (Editor) （ Role： Joint editor）
Springer 2018.6
Total pages：Springer Language：English
Other Link： https://www.amazon.com/Data-Analysis-Deep-Learning-Applications-ebook/dp/B07DL46RJX
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Systems and Computing: A New Look into Web Page Ranking Systems
Thi Thi Zin, Pyke Tin, H. Hama, T. Toriu（ Role： Joint author）
Springer International Publishing 2014.10
Language：English Book type：Scholarly book
MISC 【 display / non-display 】
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Preface International coauthorship
Pan J.S., Thi Thi Zin, Sung T.W., Lin J.C.W.
Lecture Notes in Electrical Engineering 1322 LNEE v - vii 2025
Authorship：Corresponding author Language：English Publishing type：Rapid communication, short report, research note, etc. (scientific journal) Publisher：Lecture Notes in Electrical Engineering
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A study on Depth Camera-Based Estimation of Elderly Patient Actions
Remon NAKASHIMA, Thi Thi Zin, Kazuhiro KONDO and Shinji Watanabe
37 46 - 52 2024.12
Authorship：Corresponding author Language：Japanese Publishing type：Research paper, summary (national, other academic conference) Publisher：Biomedical Fuzzy Systems Association
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A Study on the Possibility of Distinguishing between Parkinson's disease and Essential Tremor using Motor Symptoms Observed by an RGB camera
Proceedings of the 35th Annual Conference of Biomedical Fuzzy Systems Association (BMFSA2022) 2022.12
Authorship：Corresponding author Language：Japanese Publishing type：Research paper, summary (national, other academic conference) Publisher：Biomedical Fuzzy Systems Association
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Tracking A Group of Black Cows Using SORT based Tracking Algorithm
Cho Cho Aye, Thi Thi Zin, M. Aikawa, I. Kobayashi
第 35 回バイオメディカル・ファジィ・システム学会年次大会 講演論文集 (BMFSA2022) 2022.12
Authorship：Corresponding author Language：English Publishing type：Research paper, summary (national, other academic conference) Publisher：バイオメディカル・ファジィ・システム学会
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Artificial Intelligence Topping on Spectral Analysis for Lameness Detection in Dairy Cattle
Thi Thi Zin, Ye Htet, San Chain Tun and Pyke Tin
第 35 回バイオメディカル・ファジィ・システム学会年次大会 講演論文集 (BMFSA2022) 2022.12
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper, summary (national, other academic conference) Publisher：バイオメディカル・ファジィ・システム学会
Presentations 【 display / non-display 】
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Optimizing Network Message Regulations Using AI-Enhanced Dynamic Programming Methods International conference
Thi Thi Zin, Tunn Cho Lwin, H. Hama, Pyke Tin
IEEE International Conference on Consumer Electronics – Taiwan (ICCE-TW), 2025 (Kaohsiung, Taiwan) 2025.7.17 IEEE Consumer Technology Society
Event date： 2025.7.16 - 2025.7.18
Language：English Presentation type：Oral presentation (general)
Venue：Kaohsiung, Taiwan Country：Taiwan, Province of China
Network message transmission efficiency faces increasing challenges in multi-server systems due to complex traffic patterns and resource allocation demands. This paper presents an AI-enhanced dynamic programming approach for optimizing message flow regulations. By formulating the problem as a Markov Decision Process (MDP) and integrating reinforcement learning techniques, we develop an adaptive framework for network message regulation. Experimental results show our approach achieves 25% reduction in queue length and 30% improvement in resource utilization compared to conventional methods.
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Behavior Estimation of Calf Groups Using RGB Cameras and Deep Learning International conference
D. Nishimoto, Thi Thi Zin, M. Aikawa
IEEE International Conference on Consumer Electronics – Taiwan (ICCE-TW), 2025 (Kaohsiung, Taiwan) 2025.7.17 IEEE Consumer Technology Society
Event date： 2025.7.16 - 2025.7.18
Language：English Presentation type：Oral presentation (general)
Venue：Kaohsiung, Taiwan Country：Taiwan, Province of China
This paper presents a non-contact, real-time behavior estimation system for calf groups on large-scale farms. Leveraging an RGB camera and deep learning techniques, the proposed method detects calves via YOLO and tracks them using a Hungarian + Weighted IoU + Re-identification framework to maintain consistent IDs. The Segment Anything Model2 is employed to extract calf regions from each frame, and EfficientNetv2-L is used to identify individuals from these regions. By classifying postures (sitting, standing) and detecting specific intake behaviors (drinking milk, drinking water, eating), the system enables comprehensive health monitoring of each calf. Experiments conducted on 16 calves (Holstein and Jersey) achieved 91.33% MOTA in multi-object tracking, approximately 80% accuracy for posture classification, and 50–70% for intake behaviors. Furthermore, the integrated system processes five frames in about 0.70 seconds, meeting real-time requirements. These results suggest that the proposed approach can effectively reduce labor burdens, support early disease detection, and facilitate scalable livestock management.
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A study on action recognition for the elderly using depth camera International conference
Remon Nakashima, Thi Thi Zin, Hiroki Tamura, Shinji Watanabe, Etsuo Chosa
IEEE International Conference on Consumer Electronics – Taiwan (ICCE-TW), 2025 (Kaohsiung, Taiwan) 2025.7.17 IEEE Consumer Technology Society
Event date： 2025.7.16 - 2025.7.18
Language：English Presentation type：Oral presentation (general)
Venue：Kaohsiung, Taiwan Country：Taiwan, Province of China
In Japan, the rapid aging of the population has exacerbated the shortage of caregiving staff, making the optimization of care environments imperative. Conventional video surveillance methods extract human regions to perform action recognition; however, these approaches often fail to capture detailed motion analysis. In this study, a depth camera-based system is proposed to achieve non-contact, privacy-preserving action recognition using human skeleton recognition. Specifically, human regions are first extracted using bounding box detection (BB), followed by action recognition based on Keypoint-based pose estimation. The estimated Keypoints capture detailed joint positions, and their structural relationships are modeled using a Graph Convolutional Network (GCN). Furthermore, a Transformer is employed to capture the temporal features of the skeletal data. This Keypoint-centric method distinguishes this approach from conventional methods and significantly enhances the granularity of action recognition.
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Research on Feature Extraction for Prediction of Dystocia in Cows Using Image Processing Technology International conference
T. Murayama, Thi Thi Zin, I. Kobayashi, M. Aikawa
IEEE International Conference on Consumer Electronics – Taiwan (ICCE-TW), 2025 (Kaohsiung, Taiwan) 2025.7.17 IEEE Consumer Technology Society
Event date： 2025.7.16 - 2025.7.18
Language：English Presentation type：Oral presentation (general)
Venue：Kaohsiung, Taiwan Country：Taiwan, Province of China
In dairy farming, an aging operator population and a shortage of successors have led to a decline in the number of farms rearing milking cows, while the number of milking cows per farm is increasing. Under these circumstances, effective calving management has become critical. Calving fatalities cause significant economic losses to dairy operations, and dystocia accounts for approximately 20% of these cases. Without proper intervention during dystocia, the risk of fatal incidents rises and the labor burden on farmers increases. Therefore, there is a strong demand for technology that can detect early signs of calving and reduce accidents. In this study, a ceiling-mounted 360° camera was used to record cow behavior before calving. Quantitative features—including posture changes, tail-up behavior, and walking distance—were extracted and computed to develop indicators effective for predicting the onset of calving and detecting dystocia.
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Machine Learning-Based Classification of Umbilical Cord Blood Gas Using Fetal Heart Rate Variability International conference
Tunn Cho Lwin, Thi Thi Zin, Pyke Tin, E. Kino, T. Ikenoue
IEEE International Conference on Consumer Electronics – Taiwan (ICCE-TW), 2025 (Kaohsiung, Taiwan) 2025.7.17 IEEE Consumer Technology Society
Event date： 2025.7.16 - 2025.7.18
Language：English Presentation type：Oral presentation (general)
Venue：Kaohsiung, Taiwan Country：Taiwan, Province of China
Fetal heart rate variability (FHRV) is a key indicator of fetal well-being and has potential in predicting umbilical cord blood gas, an essential biomarker for fetal health assessment. Machine learning techniques can enhance fetal pH classification using FHRV features. This study aims to develop a machine learning-based classification model for fetal pH levels, leveraging FHRV data to support early risk detection during childbirth. To achieve this, we classify fetal pH into two categories using Mahalanobis Distance, Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) based on statistical FHRV features. Model performance was evaluated using confusion matrices for both training and testing datasets. Among the classifiers, SVM demonstrated the best generalizability, suggesting its potential for FHRV-based fetal pH prediction. Future work will focus on refining feature selection and improving classification accuracy to enhance fetal monitoring.
Awards 【 display / non-display 】
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Best Presentation Award
2025.4 2025 10th International Conference on Multimedia and Image Processing (ICMIP 2025) Machine learning-based prediction of cattle body condition score using 3D point cloud surface features
Pyae Phyo Kyaw, Thi Thi Zin, Pyke Tin, M. Aikawa, I. Kobayashi
Award type：Award from international society, conference, symposium, etc. Country：Japan
Body Condition Score (BCS) of dairy cattle is a crucial indicator of their health, productivity, and reproductive performance throughout the production cycle. Recent advancements in computer vision techniques has led to the development of automated BCS prediction systems. This paper proposes a BCS prediction system that leverages 3D point cloud surface features to enhance accuracy and reliability. Depth images are captured from a top-view perspective and processed using a hybrid depth image detection model to extract the cattle’s back surface region. The extracted depth data is converted into point cloud data, from which various surface features are analyzed, including normal vectors, curvature, point density, and surface shape characteristics (planarity, linearity, and sphericity). Additionally, Fast Point Feature Histograms (FPFH), triangle mesh area, and convex hull area are extracted and evaluated using three optimized machine learning models: Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GB). Model performance is assessed using different tolerance levels and error metrics, including Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Among the models, Random Forest demonstrates the highest performance, achieving accuracy rates of 51.36%, 86.21%, and 97.83% at 0, 0.25, and 0.5 tolerance levels, respectively, with an MAE of 0.161 and MAPE of 5.08%. This approach enhances the precision of BCS estimation, offering a more reliable and automated solution for dairy cattle monitoring and health management.
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Best Presentation Award
2025.4 2025 10th International Conference on Multimedia and Image Processing (ICMIP 2025) Minimizing Resource Usage for Real-Time Network Camera Tracking of Black Cows
Aung Si Thu Moe, Thi Thi Zin, Pyke Tin, M. Aikawa, I. Kobayashi
Award type：Award from international society, conference, symposium, etc. Country：Japan
Livestock plays a crucial role in the farming industry to meet consumer demand. A livestock monitoring system helps track animal health while reducing labor requirements. Most livestock farms are small, family-owned operations. This study proposes a real-time black cow detection and tracking system using network cameras in memory and disk constrained environments. We employ the Detectron2 Mask R-CNN ResNeXt-101 model for black cow region detection and the ByteTrack algorithm for tracking. ByteTrack tracks multiple objects by associating each detection box. Unlike other deep learning tracking algorithms that use multiple features such as texture, color, shape, and size. ByteTrack effectively reduces tracking ID errors and ID switches. Detecting and tracking black cows in real-time is challenging due to their uniform color and similar sizes. To optimize performance on low-specification machines, we apply ONNX (Open Neural Network Exchange) to the Detectron2 detection model for optimization and quantization. The system processes input images from network cameras, enhances color during preprocessing, and detects and tracks black cows efficiently. Our system achieves 95.97% mAP@0.75 detection accuracy and 97.16 % in daytime video and 94.83 % in nighttime accuracy of tracking are effectively tracks individual black cows, minimizing duplicate IDs and improving tracking after missed detections or occlusions. The system is designed to operate on machines with minimal hardware requirements.
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2024.12 SOFT九州支部【学会名】第26回日本知能情報ファジィ学会九州支部学術講演会 マハラノビス距離を用いた胎児心拍変動の定量的評価とpH分類
Tunn Cho Lwin, Thi Thi Zin, Pyke Tin, 紀 愛美, 池ノ上 克
Award type：Award from Japanese society, conference, symposium, etc. Country：Japan
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2024.12 SOFT九州支部【学会名】第26回日本知能情報ファジィ学会九州支部学術講演会 軽量なPointNet++モデルを用いたカラー点群に基づく牛識別システム
Pyae Phyo Kyaw, Thi Thi Zin, Pyke Tin, 相川 勝, 小林 郁雄
Award type：Award from Japanese society, conference, symposium, etc. Country：Japan
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Best Presentation Award
2024.9 18th International Conference on Innovative Computing, Information and Control (ICICIC2024) Integrating Entropy Measures of Fetal Heart Rate Variability with Digital Twin Technology to Enhance Fetal Monitoring
Tunn Cho Lwin, Thi Thi Zin, Pyae Phyo Kyaw, Pyke Tin, E. Kino and T. Ikenoue
Award type：Award from international society, conference, symposium, etc. Country：China
Grant-in-Aid for Scientific Research 【 display / non-display 】
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AIと画像データ解析を活用した牛の摂食行動モニタリングによる持続可能な酪農の実現
Grant number：25K15158 2025.04 - 2028.03
独立行政法人日本学術振興会 科学研究費補助金 基盤研究(C)（一般）
Authorship：Principal investigator
畜産は全国農業総生産額の3 割以上を占める重要な産業であるが、不適切な家畜管理による生産性の低下が大きな問題となっている。その主たる原因は飼養形態の変化による1 頭あたり観察時間の短縮であり、飼養頭数の多頭化・農家の高齢化が進む畜産現場において、365 日24 時間にわたり家畜の異常や変化を観察し続けることは困難である。
申請者らは、主に非接触・非侵襲センサ情報のアルゴリズム解析技術に着目し、距離画像とビデオ画像を用いて牛の発情を検知できる独自アルゴリズムの開発に取り組んできた。本研究では、これらの技術を応用することで、牛の発情や分娩監視時の異常を自動検知できる省力的な24 時間
家畜管理システムを開発する。 -
牛の分娩監視システムに関する研究
Grant number：18J14542 2018.04 - 2020.03
科学研究費補助金 特別研究員奨励費
須見 公祐、Thi Thi Zin（受入研究者）
Authorship：Coinvestigator(s)
精度や耐久性が不十分な割に高価なウェラブル型センサの装着や、肉体的・精神的に大きな負担を強いられる目視によるカメラ映像のモニタリング等は、大規模化する畜産現場において現実的なコストで利用できるものが極めて少ない。そこで本研究では、監視カメラから得られる映像を用いて非接触型の分娩管理システムを開発することで、農家そして牛、両方の負担を減らすことを目的とする。
本来、牛は牛群と呼ばれるグループで行動を行う。そして、分娩が間近になると分娩室という分娩専用の牛舎に移される。分娩室には2 頭以上を同時に入れるケースも多く、どの牛で分娩が始まったかを識別する必要があることから、個体識別と追跡処理が必要となる。次に、分娩行動の段階を追って検知を行う。抽出する特徴としては、尻尾が上がっているかどうか、牛が立っているか座っているか、落ち着きがなくなり移動量が増加するか、子牛を出産したかどうか、親牛が子牛を舐めているかどうかなど、それぞれの過程で自動的に異常を見つけ通報を行うアルゴリズムの開発を進める。分娩行動が起きたかどうかの判断は、これらのデータから各特徴の重要度（重み）を学習させることによって行う。そして、最終目標として難産など異常行動の検知を行うために事例を蓄積しながら知識ベースを充実させ、異常事態の検知を行い、分娩の各段階を監視して異常事態の検知ならびに通報が可能なシステムの開発を目指す。 -
画像処理技術と非接触センサを用いた牛の発情検知及び分娩監視システムの開発
Grant number：17K08066 2017.04 - 2021.03
科学研究費補助金 基盤研究(C)
Authorship：Principal investigator
畜産は全国農業総生産額の3 割以上を占める重要な産業であるが、不適切な家畜管理による生産性の低下が大きな問題となっている。その主たる原因は飼養形態の変化による1 頭あたり観察時間の短縮であり、飼養頭数の多頭化・農家の高齢化が進む畜産現場において、365 日24 時間にわたり家畜の異常や変化を観察し続けることは困難である。
申請者らは、主に非接触・非侵襲センサ情報のアルゴリズム解析技術に着目し、距離画像とビデオ画像を用いて牛の発情を検知できる独自アルゴリズムの開発に取り組んできた。本研究では、これらの技術を応用することで、牛の発情や分娩監視時の異常を自動検知できる省力的な24 時間
家畜管理システムを開発する。 -
Development of forensic imaging modality for person identification using integration method of feature correspondences between heterogeneous images
Grant number：15K15457 2015.04 - 2018.03
Grant-in-Aid for Scientific Research Grant-in-Aid for challenging Exploratory Research
Authorship：Coinvestigator(s)
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Study of interactive teaching systems using an image processing techniques
Grant number：15K01041 2015.04 - 2018.03
Grant-in-Aid for Scientific Research Grant-in-Aid for Scientific Research(C)
Authorship：Coinvestigator(s)
Available Technology 【 display / non-display 】
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ICTを活用した牛のモニタリングシステムの開発に関する研究
安全・安心のための24時間自動見守り・監視システムの開発に関する研究
工場の作業効率化のための作業グループ検出に関する研究Home Page： 研究者データベース
Related fields where technical consultation is available：ビッグデータからの新しい知見の獲得・発見を体系的に行える数理的道具の開発
牛のモニタリング情報分析システム
高度な画像処理技術とAI活用による身体・精神機能低下患者の行動認識に関する研究
ビデオ画像を利用した新生児運動モニタリングシステムの開発に関する研究
画像処理技術を用いた疾病の特徴を示すエビを検出するシステムMessage：『画像処理を用いて様々な問題を解決すること』を目的として、農学や医学の分野に係わる学際領域の研究を幅広く行っています。多種多様な課題に対して各分野の専門家と協力して、画像処理分野からの貢献を目指して、研究・開発を行っています。