THI THI ZIN

写真a

Affiliation

Engineering educational research section Information and Communication Technology Program

Title

Professor

External Link

Degree 【 display / non-display

  • Doctor of Engineering ( 2007.3   Osaka City University )

  • Master of Engineering ( 2004.3   Osaka City University )

  • Master of Information Science ( 1999.5   University of Computer Studies, Yangon (UCSY) )

  • B.Sc (Hons) (Mathematics) ( 1995.5   University of Yangon (UY) )

Research Areas 【 display / non-display

  • Informatics / Perceptual information processing  / Image Processing

  • Informatics / Database

  • Life Science / Animal production science

 

Papers 【 display / non-display

  • Real-time action recognition system for elderly people using stereo depth camera Reviewed

    Thi Thi Zin, Ye Htet, Akagi Y., Tamura H., Kondo K., Araki S., Chosa E.

    Sensors   21 ( 17 )   2021.9

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Sensors  

    Smart technologies are necessary for ambient assisted living (AAL) to help family mem-bers, caregivers, and health-care professionals in providing care for elderly people independently. Among these technologies, the current work is proposed as a computer vision-based solution that can monitor the elderly by recognizing actions using a stereo depth camera. In this work, we intro-duce a system that fuses together feature extraction methods from previous works in a novel combination of action recognition. Using depth frame sequences provided by the depth camera, the system localizes people by extracting different regions of interest (ROI) from UV-disparity maps. As for feature vectors, the spatial-temporal features of two action representation maps (depth motion appearance (DMA) and depth motion history (DMH) with a histogram of oriented gradients (HOG) descriptor) are used in combination with the distance-based features, and fused together with the automatic rounding method for action recognition of continuous long frame sequences. The experimental results are tested using random frame sequences from a dataset that was collected at an elder care center, demonstrating that the proposed system can detect various actions in real-time with reasonable recognition rates, regardless of the length of the image sequences.

    DOI: 10.3390/s21175895

    Scopus

  • Activity-integrated hidden markov model to predict calving time Reviewed

    K. Sumi, Swe Zar Maw, Thi Thi Zin, Pyke Tin, I. Kobayashi, Y. Horii

    Animals   11 ( 2 )   1 - 12   2021.2

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Animals  

    Accurately predicting when calving will occur can provide great value in managing a dairy farm since it provides personnel with the ability to determine whether assistance is necessary. Not providing such assistance when necessary could prolong the calving process, negatively affecting the health of both mother cow and calf. Such prolongation could lead to multiple illnesses. Calving is one of the most critical situations for cows during the production cycle. A precise video-monitoring system for cows can provide early detection of difficulties or health problems, and facilitates timely and appropriate human intervention. In this paper, we propose an integrated approach for predicting when calving will occur by combining behavioral activities extracted from recorded video sequences with a Hidden Markov Model. Specifically, two sub-systems comprise our proposed system: (i) Behaviors extraction such as lying, standing, number of changing positions between lying down and standing up, and other significant activities, such as holding up the tail, and turning the head to the side; and, (ii) using an integrated Hidden Markov Model to predict when calving will occur. The experiments using our proposed system were conducted at a large dairy farm in Oita Prefecture in Japan. Experimental results show that the proposed method has promise in practical applications. In particular, we found that the high frequency of posture changes has played a central role in accurately predicting the time of calving.

    DOI: 10.3390/ani11020385

    Scopus

  • Imaging tremor quantification for neurological disease diagnosis Reviewed

    Y. Mitsui, Thi Thi Zin, N. Ishii, H. Mochizuki

    Sensors (Switzerland)   20 ( 22 )   1 - 14   2020.11

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Sensors (Switzerland)  

    In this paper, we introduce a simple method based on image analysis and deep learning that can be used in the objective assessment and measurement of tremors. A tremor is a neurological disorder that causes involuntary and rhythmic movements in a human body part or parts. There are many types of tremors, depending on their amplitude and frequency type. Appropriate treatment is only possible when there is an accurate diagnosis. Thus, a need exists for a technique to analyze tremors. In this paper, we propose a hybrid approach using imaging technology and machine learning techniques for quantification and extraction of the parameters associated with tremors. These extracted parameters are used to classify the tremor for subsequent identification of the disease. In particular, we focus on essential tremor and cerebellar disorders by monitoring the finger–nose–finger test. First of all, test results obtained from both patients and healthy individuals are analyzed using image processing techniques. Next, data were grouped in order to determine classes of typical responses. A machine learning method using a support vector machine is used to perform an unsupervised clustering. Experimental results showed the highest internal evaluation for distribution into three clusters, which could be used to differentiate the responses of healthy subjects, patients with essential tremor and patients with cerebellar disorders.

    DOI: 10.3390/s20226684

    Scopus

  • Feature Detection and Classification of Cow Motion for Predicting Calving time Reviewed

    Thi Thi Zin, Saw Zay Maung Maung, Pyke Tin, Y. Horii

    2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020   305 - 306   2020.10

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020  

    The monitoring and automatic detecting of cow behaviors is a key factor for predicting cow calving times. This paper describes the analysis of cow motion patterns by using 360 camera in order to identify various views of cow states. Firstly, Principle Component Analysis (PCA) is applied to solve the rotation variant problem in different postures of cow body and then the dominant features (shape distances) are extracted for cow motion classification such as Standing, Lying, and Transition States (Standing-to-Lying and Lying-to-Standing). During the movement of cow motions, the increasing and decreasing trends of shape (Beta features) from cow body are used to classify transition activities of cows. We prepared the datasets by grouping similar motion sequences and tested against with the proposed features. According to experimental results, the proposed system can give the high accuracy with low computational cost in case of detecting and classifying cow motions.

    DOI: 10.1109/GCCE50665.2020.9291999

    Scopus

  • Elderly monitoring and action recognition system using stereo depth camera Reviewed

    Thi Thi Zin, Ye Htet, Y. Akagi, H. Tamura, K. Kondo, S. Araki

    2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020   316 - 317   2020.10

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020  

    The proposed system used stereo type depth camera by examining the human action recognition and also sleep monitoring in the elderly care center. Different regions of interest (ROI) are extracted using the U-Disparity and V-Disparity maps. The main information used for recognition is 3D human centroid height relative to the floor and percentage of movement from frame differencing for sleep monitoring. The results from the experiments of the proposed method show that this system can detect the person location, sitting or lying and also sleep behaviors effectively.

    DOI: 10.1109/GCCE50665.2020.9291785

    Scopus

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Books 【 display / non-display

  • 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 

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    Total pages:Springer   Language:English

    その他リンク: https://www.amazon.com/Data-Analysis-Deep-Learning-Applications-ebook/dp/B07DL46RJX

  • 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 

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    Language:English Book type:Scholarly book

MISC 【 display / non-display

  • 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 Invited

    Thi Thi Zin, 小林 郁雄, 椎屋 和久, PYKE TIN, 堀井 洋一郎, 濱 裕光

    ICTイノベーションフォーラム2020   2021.1

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    Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (other)   Publisher:戦略的情報通信研究開発推進事業(SCOPE)  

    高齢化、大規模化する現代の畜産で、24時間365日にわたり家畜の健康管理を適切に行い、異常や変化に留意し続けながら経営を継続することは容易でない。本研究開発では、家畜生産性の改善と地域活性化の実現を目的とする牛のモニタリングシステム構築に必要な要素技術の開発を行う。

  • Introduction to IEEE GCCE2020 overview Invited

    Thi Thi Zin and Ryota Nishimura

    IEEE GCCE2020 Abstract Book   8 - 8   2020.10

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    Language:English   Publishing type:Research paper, summary (national, other academic conference)   Publisher:IEEE CT Soc  

  • Interdisciplinary Approach to Smart Dairy Farming Invited

    Thi Thi Zin

    Proceedings on 3rd University Conference on Science, Engineering and Research   1 - 1   2020.8

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    Language:English   Publishing type:Article, review, commentary, editorial, etc. (bulletin of university, research institution)   Publisher:Technological University (Kyaukse), Myanmar  

    Smart dairy farming emerged from the concept of Precision Agriculture, in which IoT technologies and artificial intelligence analysis are put to efficient use. Using these technologies to provide individual care for cows is fundamental to the future of dairy farming. Most dairy farms around the globe adhere to international ISO standards in identifying individual cows. On the other hand, 5G communications are being
    developed widely and nicely making the dairy farming smarter and faster in wealth and health. Thus, the dairy farming in agriculture should be explored from the perspectives of engineering disciplines. In this talk, we shall focus on how image processing techniques can be utilized to develop a tracking system for individual cows using an ear tag visual analysis.

  • Introduction to Organized Sessions and the OS Chairs/Co-Chairs

    Goto T., Thi Thi Zin, Hama H., Hojo R., Shimizu S., Koremura Y., Kuroda T.

    LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies   9 - 11   2020.3

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    Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (bulletin of university, research institution)   Publisher:LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies  

    DOI: 10.1109/LifeTech48969.2020.9081326

    Scopus

  • 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 Invited

    Thi Thi Zin、小林 郁雄、椎屋 和久、Pyke Tin、堀井 洋一郎、濱 裕光

    2019年度電気・情報関係学会九州支部連合大会(第72回連合大会)講演論文集   ( 11 )   1 - 2   2019.9

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    Language:Japanese   Publishing type:Research paper, summary (national, other academic conference)   Publisher:電気・情報関係学会  

    高齢化、大規模化する現代の畜産で、24時間365日にわたり家畜の健康管理を適切に行い、異常や変化に留意し続けながら経営を継続することは容易でない。本研究では、家畜生産性の改善と地域活性化の実現を目的とした要素技術の開発を行う。人の監視・見守りの分野で開発された非接触・非侵襲センサ情報の解析アルゴリズムを独自の手法で応用し、生産者の負担を大幅に軽減しながら家畜の状態を24時間監視できるシステムを開発する。具体的には、畜産農家からの強い要望がある、ボディコンディションスコア(BCS:Body Condition Score)の評価、発情検知、分娩過程の管理、個体識別、異常検知等を可能とするシステムの開発を目指す。

    DOI: 10.11527/jceeek.2019.0_237

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Presentations 【 display / non-display

  • Automatic Detection of Mounting Behavior in Cattle using Semantic Segmentation and Classification International conference

    Su Myat Noe, Thi Thi Zin, Ikuo Kobayashi, Pyke Tin

    The 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021)  (Nara Royal Hotel (Nara, Japan))  IEEE Life Sciences Technical Community

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    Event date: 2021.3.9 - 2021.3.11

    Language:English   Presentation type:Oral presentation (general)  

    Venue:Nara Royal Hotel (Nara, Japan)  

    In cattle farming sector, the accurate detection of estrus plays a vital role because incorrect timing for artificial insemination affects the cattle business. The noticeable sign of estrus is the standing heat, where the cattle standing to be mounted by other cows for a couple of seconds. In this paper, we proposed cattle region detection using deep learning semantic segmentation model and automatic detection of mounting behavior with machine learning classification methods. Based on the conducted experiment, the results show that a mean Intersection of Union (IoU) of 98% on the validation set. The pixel-wise accuracy for two classes (cattle and background) was found to be both 98%, respectively. For the classification, the proposed method compares the four supervised machine learning methods which can detect with the accuracy rate of Support Vector Machine, Naïve Bayes, Logistic Regression and Linear Regression are 87%, 96%, 90%, and 80% respectively. Among them, Naïve Bayes algorithm perform the best. The novelty of this work noticeably implies that deep learning semantic segmentation could be effectively employed as a pre-processing step in segmenting the cattle and background prior to using various classification models.

  • Markov Chain Monte Carlo Method for the Modeling of Posture Changes Prior to Calving International conference

    Thi Thi Zin, Pyke Tin, Pann Thinzar Seint, Yoichiro Horii

    The 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021)  (Nara Royal Hotel (Nara, Japan))  IEEE Life Sciences Technical Community

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    Event date: 2021.3.9 - 2021.3.11

    Language:English   Presentation type:Oral presentation (general)  

    Venue:Nara Royal Hotel (Nara, Japan)  

    An accurate and careful analysis of posture changes for a dairy cow prior to calving plays an important role in making calving time prediction. The patterns of activities such as frequent changes in postures of a pregnant cows during the time closer to calving are utilized as indicators to predict the time of calving. In this paper, we introduce Markov Chain Monte Carlo (MCMC) method to generate the patterns of four states activities such lying, transitions from lying to standing, standing itself and transitions from standing to lying based on the monitored cow activity changes data three days prior to calving. The validity of the generated cow activities in posture changes data is compared with the actual collected data in terms of Euclidean and Cosine distance measures. The experimental results show that the method in this paper can be used as a generalized method to generate synthetic data series of dairy cow activities prior to calving.

  • Petrochemical Characteristics of the Granitoid Rocks of Northern Myanmar International conference

    Htin Lynn Aung, Thaire Phyu Win, Thi Thi Zin

    The 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021)  (Nara Royal Hotel (Nara, Japan))  IEEE Life Sciences Technical Community

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    Event date: 2021.3.9 - 2021.3.11

    Language:English   Presentation type:Oral presentation (general)  

    Venue:Nara Royal Hotel (Nara, Japan)  

    The research area is located on the Mogaung - Kamaing-Hpakant road in Hpakant Township, Kachin State, northern Myanmar. The dominant lithologic units comprise igneous and metamorphic rocks. The present work is mainly intended to establish the petrogenesis of the igneous rocks based on the petrochemical analysis results. The igneous rocks are mainly microgranite and serpentinite. Major element analysis of some rocks was determined by XRF spectrometer and interpreted the genesis of these rock units. On the basis of the petrochemical characteristics, the microgranite of the study area is I-type peraluminous granitoid formed by partial melting of mantle and / or lower crust in the extensional tectonics.

  • Image Technology Based Detection of Infected Shrimp in Adverse Environments International conference

    Thi Thi Zin, Takehiro Morimoto, Naraid Suanyuk, Toshiaki Itami, Chutima Tantikitti

    The 1st International Conference on Sustainable Agriculture and Aquaculture: For Well Being and Food Security  (Prince of Songkla University)  www.psu.ac.th, www.kku.ac.th, www.ku.ac.th, www.cmu.ac.th,

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    Event date: 2021.1.11 - 2021.1.12

    Language:English   Presentation type:Oral presentation (general)  

    Venue:Prince of Songkla University  

    In recent years, the cultivation of white leg shrimp (Litopenaeus vannamei) has become popular in countries around Japan, especially in Southeast Asia, and at the same time, various diseases have occurred in the farms [1]. In the early stages of infection, shrimp show three abnormal behaviors: (1) they appear in the shallow waters of the farm, (2) they do not move and do not eat even when fed, and (3) they suddenly start moving. Early detection is important step to control this disease because there are no preventive measures. In addition, we are currently visually confirming shrimp that show characteristic of the disease. However, these lead to a burden on the farmers and delay in discovery [2]. Therefore, we propose an image technology based monitoring system for detecting shrimp showing the characteristics of diseases.

  • Smart Irrigation: An Intelligent System for Growing Strawberry Plants in Different Seasons of the Year International conference

    Ye Htet, Htin Kyaw Oo and Thi Thi Zin

    4th International Symposium on Information and Knowledge Management (ISIKM2020)  (Online Conference)  ICIC International

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    Event date: 2020.12.12 - 2020.12.13

    Language:English   Presentation type:Oral presentation (general)  

    Venue:Online Conference  

    Agriculture is an important source of livelihood and varying the way of cultivating plants could provide more productivity and sustainability of foods than before and thus smart irrigation would be one of the best solutions. Therefore, the proposed system mainly focused on the strawberry plants to grow within small-scale farm using intelligent systems to bear and produce fruits in all seasons of the country. A challenging problem which arises for this objective is the precise temperature, water and fertilizer management for plants. So, this system emphasized on automatic environmental adjustment system integrated with sensors to control temperature and also drip irrigation system for efficient water and fertilizers usage. Moreover, leaf analysis using computer vision which is controlled by Raspberry Pi is implemented for detection of the nutrient deficiency symptoms of plants. As for the communication unit to inform the users via sensors and image processing, Internet of Things is adopted.

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Awards 【 display / non-display

  • IEEE LifeTech 2021 WIE Paper Award

    2021.3   IEEE 3rd Global Conference on Life Sciences and Technologies(LifeTech2021)   Automatic Detection of Mounting Behavior in Cattle using Semantic Segmentation and Classification

    Su Myat Noe, Thi Thi Zin, Ikuo Kobayashi, Pyke Tin

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    Award type:Award from international society, conference, symposium, etc.  Country:Japan

  • IEEE LifeTech 2020 WIE Paper Award

    2020.3   IEEE 2nd Global Conference on Life Sciences and Technologies(LifeTech2020)   Human Action Analysis Using Virtual Grounding Point and Motion History

    Swe Nwe Nwe Htun, Thi Thi Zin, Hiromitsu Hama

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    Award type:Award from international society, conference, symposium, etc.  Country:Japan

    In this paper, we propose an approach to human action analysis for home care monitoring system in the aspect of image processing and life science technologies. We introduce a new concept of a virtual grounding point representing the position of a target person as an innovated feature for action analysis. Specifically, in developing action analysis, the background subtraction is firstly conducted by applying the Mixture of Gaussian and low rank subspace learning. After that, the graph cut is embedded to enhance the foregrounds in order to detect both of moving and motionless object. Secondly, the virtual grounding point is calculated by using the centroid of silhouette image. Finally, motion of the person is estimated by using timed motion history image in order to improve the accuracy of action analysis. A series of the experiments are conducted to confirm the effectiveness of the proposed method.

  • IEEE GCCE 2019 Excellent Student Paper Award (Silver Prize)

    2019.10  

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    Award type:Award from international society, conference, symposium, etc.  Country:Japan

  • Certificate of Merit (Student) for The 2018 IAENG International Conference on Imaging Engineering

    2018.9   IMECS 2018 (Intlernational MultiConference of Engineers and Computer Scientists 2018)   Image Technology based Students' Feedbacks Analyzing System using Deep Learning

    Cho Nilar Phyo, Thi Thi Zin, Hiroshi Kamada, Takashi Toriu

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    Award type:Award from international society, conference, symposium, etc.  Country:Hong Kong

    In these days, the integration of technology in teaching-learning process has become a central role in order to redesign a quality education system especially for the development of interactive education. In this concern, technology based analysis on the interaction between students and teacher and the feedback of the students play key roles. Thus, in this paper, we proposed the automatic students’ feedbacks analyzing system for the purpose of speeding up the communication between students and teacher in the classroom by using the image processing and deep learning technology. In the proposed system, the students can use the five kind of color cards for answering the questions or for describing their feedbacks. Then the automatic students’ feedback analyzing system will analyze the color cards objects by using the camera and describe the analyzed result to the teacher. In this way, the interaction between students and teacher can be faster and can give a lot of benefit for the education system. In order to implement this system, firstly, the color objects segmentation is performed over the input image using the predefined color thresholds. Then, the noise objects are removed by using the predefined maximum size and minimum size thresholds. Finally, the Deep Convolutional Neural Network (DCNN) is applied in order to classify the five color cards objects and non-card color objects. The experiments are performed on the image that have been taken in the large classroom under the different illumination condition. According to the experimental results, the proposed system can robustly analyze the color cards objects with the accuracy of 97.02% on the training data and 94.38% for the testing data. The proposed system can give the ubiquitous (anytime, anywhere) analyzing of the students’ feedback in the classroom.

  • Certificate of Merit for The 2018 IAENG International Conference on Imaging Engineering

    2018.9   IMECS 2018 (Intlernational MultiConference of Engineers and Computer Scientists 2018)   Image Technology based Cow Identification System Using Deep Learning

    Thi Thi Zin, Cho Nilar Phyo, Pyke Tin, Hiromitsu Hama, Ikuo Kobayashi

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    Award type:Award from international society, conference, symposium, etc.  Country:Hong Kong

    Today worldwide trending in precision dairy farming is becoming more focus on an individual cow welfare and health rather than group management by using modern technologies including image processing techniques. In such cases, individual cow identification is one of the fundamental ingredients for the success of modern dairy farming. Thus, in this paper we shall explore and examine how image processing technologies can be utilized in analyzing and identifying individual cows along with deep learning techniques. This system is mainly focus on the identification of individual cow based on the black and white pattern of the cow’s body. In our system, firstly we detect the cow’s body which have been placed on the Rotary Milking Parlour by using the inter-frame differencing and horizontal histogram based approach. Then, we crop the cow’s body region by using the predefined distance value. Finally, the cropped images are used as input data for training the deep convolutional neural network for the identification of individual cow’s pattern. The experiments are performed on the self-collected cow video dataset which have been taken at the large-scale ranch in Oita Prefecture, Japan. According the experimental result, our system got the accuracy of 86.8% for automatic cropping of cow’s body region and 97.01% for cow’s pattern identification. The result shows that our system can automatically recognize each individual cow’s pattern very well.

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Grant-in-Aid for Scientific Research 【 display / non-display

  • 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

    2017.04 - 2021.03

    科学研究費補助金  基盤研究(C)

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    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

    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)

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    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。

  • Development of forensic imaging modality for person identification using integration method of feature correspondences between heterogeneous images

    2015.04 - 2018.03

    Grant-in-Aid for Scientific Research  Grant-in-Aid for challenging Exploratory Research

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    Authorship:Coinvestigator(s) 

  • Study of interactive teaching systems using an image processing techniques

    2015.04 - 2018.03

    Grant-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research(C)

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    Authorship:Coinvestigator(s) 

  • Development of automatic estrus detection systems of cattle using complementary the plane distance image and video footage

    2015.04 - 2017.03

    Grant-in-Aid for Scientific Research  Grant-in-Aid for challenging Exploratory Research

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    Authorship:Principal investigator 

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Other research activities 【 display / non-display

  • Big Data Analysis and Deep Learning Applications - Proceedings of First International Conference on Big Data Analysis and Deep Learning

    2018.01 - 2018.08

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    Conference Program Committee Chair and Publication Chair of the First International Conference on Big Data Analysis and Deep Learning (ICBDL2018)
    (Editor) Advances in Intelligent Systems and Computing 744, Big Data Analysis and Deep Learning Applications: Proceedings of the First International Conference on Big Data Analysis and Deep Learning