ChanMin Park's Blog

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        /url: /
        
        text: 
        
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        text: 
      - navigation [ref=e18]:
        
        list [ref=e19]:
        
        listitem [ref=e20]:
        
        link “Home” [ref=e21] [cursor=pointer]:
        
        /url: /
        
        listitem [ref=e22]:
        
        link “Project” [ref=e23] [cursor=pointer]:
        
        /url: /project.html
        
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        link “Research ” [ref=e25] [cursor=pointer]:
        
        /url: /research.html
        
        text: Research
        
        generic [ref=e26]: 
        
        list [ref=e27]:
        
        listitem [ref=e28]:
        
        link “Image Retrieval” [ref=e29] [cursor=pointer]:
        
        /url: /pages/image-retrieval.html
        
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        link “3D Vision” [ref=e31] [cursor=pointer]:
        
        /url: /pages/3dvision.html
        
        listitem [ref=e32]:
        
        link “Study ” [ref=e33] [cursor=pointer]:
        
        /url: /study.html
        
        text: Study
        
        generic [ref=e34]: 
        
        list [ref=e35]:
        
        listitem [ref=e36]:
        
        link “Image Processing” [ref=e37] [cursor=pointer]:
        
        /url: /pages/image-processing.html
        
        listitem [ref=e38]:
        
        link “Deep Learning” [ref=e39] [cursor=pointer]:
        
        /url: /pages/deep-learning.html
        
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        link “3D Geometry” [ref=e41] [cursor=pointer]:
        
        /url: /pages/3d-geometry.html
        
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        link “ROS2” [ref=e43] [cursor=pointer]:
        
        /url: /pages/ros2.html
        
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        link “Graph” [ref=e45] [cursor=pointer]:
        
        /url: /graph/
        
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        link “Portfolio” [ref=e47] [cursor=pointer]:
        
        /url: https://smoggy-fibula-4b3.notion.site/My-Portfolio-Chan-Min-Park-195c3ffbc67f4f67a80eca8b9a506a3b?pvs=74
        
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        generic [ref=e50]: 
  - article [ref=e54]:
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        generic [ref=e57]:
        
        img “ChanMin Park” [ref=e58]
        
        generic [ref=e59]:
        
        heading “ChanMin Park” [level=1] [ref=e60]
        
        paragraph [ref=e61]: Vision Research Engineer
        
        generic [ref=e62]:
        
        paragraph [ref=e63]: “I build industrial and medical vision systems end to end: from Physical AI and domain adaptation to 3D detection, MLOps, and production deployment.”
        
        generic [ref=e64]:
        
        generic [ref=e65]:
        
        generic [ref=e66]:
        
        generic [ref=e67]: 
        
        generic [ref=e68]: Physical AI · Spatial Intelligence
        
        paragraph [ref=e69]: Build Physical AI, sim-to-real, and autonomous vision systems for manufacturing processes.
        
        generic [ref=e70]:
        
        generic [ref=e71]: Physical AI
        
        generic [ref=e72]: Spatial Intelligence
        
        generic [ref=e73]: Vision Control
        
        generic [ref=e74]:
        
        generic [ref=e75]:
        
        generic [ref=e76]: 
        
        generic [ref=e77]: System Engineering · MLOps
        
        paragraph [ref=e78]: Design end-to-end vision systems from data and model pipelines to packaging, deployment, and reliable operations.
        
        generic [ref=e79]:
        
        generic [ref=e80]: System Engineering
        
        generic [ref=e81]: MLOps
        
        generic [ref=e82]: Deployment
        
        generic [ref=e83]:
        
        generic [ref=e84]:
        
        generic [ref=e85]: 
        
        generic [ref=e86]: Sim2Real · SSL · Domain Adaptation
        
        paragraph [ref=e87]: Develop sim-to-real, self-supervised learning, and domain adaptation workflows that keep vision models stable under domain shift.
        
        generic [ref=e88]:
        
        generic [ref=e89]: Sim2Real
        
        generic [ref=e90]: Self-Supervised
        
        generic [ref=e91]: Domain Adaptation
        
        generic [ref=e92]:
        
        link “010-3999-3403” [ref=e93] [cursor=pointer]:
        
        /url: tel:+821039993403
        
        generic [ref=e94]: 
        
        text: 010-3999-3403
        
        link “Email” [ref=e95] [cursor=pointer]:
        
        /url: mailto:pcmin03@gmail.com
        
        generic [ref=e96]: 
        
        generic [ref=e97]: Email
        
        link “LinkedIn” [ref=e98] [cursor=pointer]:
        
        /url: https://www.linkedin.com/in/chan-min-park-0abab713b/
        
        generic [ref=e99]: 
        
        text: LinkedIn
        
        link “GitHub” [ref=e100] [cursor=pointer]:
        
        /url: https://github.com/pcmin03
        
        generic [ref=e101]: 
        
        text: GitHub
        
        link “CV” [ref=e102] [cursor=pointer]:
        
        /url: /MY_CV_final.pdf
        
        generic [ref=e103]: 
        
        generic [ref=e104]: CV
        
        generic [ref=e105]:
        
        generic [ref=e106]:
        
        generic [ref=e107]: 5+
        
        generic [ref=e108]: Years in CV & Industry
        
        generic [ref=e109]:
        
        generic [ref=e110]: 5+
        
        generic [ref=e111]: Major projects
        
        generic [ref=e112]:
        
        generic [ref=e113]: 5+
        
        generic [ref=e114]: Patents
        
        generic [ref=e115]:
        
        generic [ref=e116]: “6”
        
        generic [ref=e117]: Vision papers
        
        navigation [ref=e118]:
        
        list [ref=e119]:
        
        listitem [ref=e120]:
        
        link “ Experience” [ref=e121] [cursor=pointer]:
        
        /url: “#experience”
        
        generic [ref=e122]: 
        
        generic [ref=e123]: Experience
        
        listitem [ref=e124]:
        
        link “ Education” [ref=e125] [cursor=pointer]:
        
        /url: “#education”
        
        generic [ref=e126]: 
        
        generic [ref=e127]: Education
        
        listitem [ref=e128]:
        
        link “ Projects” [ref=e129] [cursor=pointer]:
        
        /url: “#projects”
        
        generic [ref=e130]: 
        
        generic [ref=e131]: Projects
        
        listitem [ref=e132]:
        
        link “ Seminar” [ref=e133] [cursor=pointer]:
        
        /url: “#seminar”
        
        generic [ref=e134]: 
        
        generic [ref=e135]: Seminar
        
        listitem [ref=e136]:
        
        link “ Publications” [ref=e137] [cursor=pointer]:
        
        /url: “#publications”
        
        generic [ref=e138]: 
        
        generic [ref=e139]: Publications
        
        listitem [ref=e140]:
        
        link “ Expertise & Tools” [ref=e141] [cursor=pointer]:
        
        /url: “#skills”
        
        generic [ref=e142]: 
        
        generic [ref=e143]: Expertise & Tools
        
        listitem [ref=e144]:
        
        link “ Awards” [ref=e145] [cursor=pointer]:
        
        /url: “#awards”
        
        generic [ref=e146]: 
        
        generic [ref=e147]: Awards
        
        listitem [ref=e148]:
        
        link “ Recent Posts” [ref=e149] [cursor=pointer]:
        
        /url: “#recent-posts”
        
        generic [ref=e150]: 
        
        generic [ref=e151]: Recent Posts
        
        generic [ref=e152]:
        
        text:        
        
        generic [ref=e153]:
        
        generic [ref=e154]:
        
        heading “Experience” [level=3] [ref=e155]
        
        generic [ref=e156]:
        
        heading “POSCO DX AX Technology R&D Group · Spatial Intelligence Vision Engineer · POSCO DX” [level=4] [ref=e157]:
        
        img “POSCO DX” [ref=e158]
        
        generic [ref=e159]: AX Technology R&D Group · Spatial Intelligence Vision Engineer · POSCO DX
        
        generic [ref=e160]: Pangyo, Korea · July 2024 - Present
        
        generic “POSCO DX key technologies” [ref=e161]:
        
        generic [ref=e162]: Physical AI
        
        generic [ref=e163]: Isaac Sim
        
        generic [ref=e164]: PLC Integration
        
        generic [ref=e165]: Dataiku
        
        generic [ref=e166]: MLOps
        
        generic [ref=e167]: Image Retrieval
        
        list [ref=e168]:
        
        listitem [ref=e169]:
        
        text: •
        
        link “Isaac Sim·PLC·Sim2Real” [ref=e170] [cursor=pointer]:
        
        /url: https://kidd.co.kr/news/245144
        
        text: “: Built physical-AI training environments that mirrored steel-site conditions and iterated models against real deployment constraints.”
        
        listitem [ref=e171]:
        
        text: •
        
        link “Dataiku·SDD·MLOps” [ref=e172] [cursor=pointer]:
        
        /url: https://www.digitaltoday.co.kr/news/articleView.html?idxno=500285&rf=toastPopup&utm_source=digitaltoday
        
        text: “: Advanced Digital Transformation (DX) from smart factory to intelligence factory by building SDD-driven image retrieval and automated MLOps pipelines for steel vision systems.”
        
        listitem [ref=e173]:
        
        text: •
        
        link “Autonomous Steelmaking·L2 Integration·Vision AI” [ref=e174] [cursor=pointer]:
        
        /url: https://newsroom.posco.com/kr/%EC%9D%B8%ED%84%B0%EB%B7%B0-%EB%AF%B8%EB%9E%98%EB%A5%BC-%EC%97%AC%EB%8A%94-%ED%98%81%EC%8B%A0-%EA%B8%B0%EC%88%A0-%EA%B0%9C%EB%B0%9C-2025-%ED%8F%AC%EC%8A%A4%EC%BD%94-%EA%B8%B0%EC%88%A0%EB%8C%80/
        
        text: “: Integrated L2 communications and control signals to apply vision-based anomaly detection in autonomous steelmaking operations.”
        
        generic [ref=e175]:
        
        strong [ref=e176]: “Tools used:”
        
        generic “POSCO DX tool stack” [ref=e177]:
        
        generic [ref=e178]:
        
        img “Isaac Sim” [ref=e179]
        
        generic [ref=e180]: Isaac Sim
        
        generic [ref=e181]:
        
        img “PyTorch” [ref=e182]
        
        generic [ref=e183]: PyTorch
        
        generic [ref=e184]:
        
        img “Dataiku” [ref=e185]
        
        generic [ref=e186]: Dataiku
        
        generic [ref=e187]:
        
        img “Docker” [ref=e188]
        
        generic [ref=e189]: Docker
        
        generic [ref=e190]:
        
        img “MLflow” [ref=e191]
        
        generic [ref=e192]: MLflow
        
        generic [ref=e193]:
        
        heading “VUNO AI Research Engineer · VUNO Inc.” [level=4] [ref=e194]:
        
        img “VUNO” [ref=e195]
        
        generic [ref=e196]: AI Research Engineer · VUNO Inc.
        
        generic [ref=e197]: Seoul, Korea · May 2021 - July 2024
        
        generic “VUNO key technologies” [ref=e198]:
        
        generic [ref=e199]: Medical AI
        
        generic [ref=e200]: PyTorch
        
        generic [ref=e201]: Domain Adaptation
        
        generic [ref=e202]: Self-Supervised Learning
        
        generic [ref=e203]: 3D Detection
        
        generic [ref=e204]: Applied Engineering
        
        generic [ref=e205]: MLflow
        
        list [ref=e206]:
        
        listitem [ref=e207]:
        
        text: •
        
        link “Test-Time Adaptation·Domain Adaptation·ACCV 2024” [ref=e208] [cursor=pointer]:
        
        /url: https://openaccess.thecvf.com/content/ACCV2024/html/Cho_CNG-SFDA_Clean-and-Noisy_Region_Guided_Online-Offline_Source-Free_Domain_Adaptation_ACCV_2024_paper.html
        
        text: “: Developed adaptation strategies for medical imaging domain shifts and contributed to the CNG-SFDA research line published at ACCV 2024.”
        
        listitem [ref=e209]:
        
        text: •
        
        link “Self-Supervised Learning·Universal Segmentation·TIGER Challenge” [ref=e210] [cursor=pointer]:
        
        /url: https://tiger.grand-challenge.org/Home/
        
        text: “: Conducted segmentation-oriented SSL research, built pathology workflows around it, and applied the stack to achieve 1st place in the TIGER Challenge.”
        
        listitem [ref=e211]:
        
        text: •
        
        link “Lung CT·3D Tiny Object Detection·MLOps” [ref=e212] [cursor=pointer]:
        
        /url: https://www.docdocdoc.co.kr/news/articleView.html?idxno=3013245
        
        text: “: Built and deployed lung nodule detection pipelines for 3D tiny-object modeling, with production-grade monitoring and release workflows in real service environments.”
        
        listitem [ref=e213]:
        
        text: •
        
        link “PathQuant·End-to-End Delivery·Applied Engineering” [ref=e214] [cursor=pointer]:
        
        /url: https://www.vuno.co/news/view/810
        
        text: “: Owned the full cycle from data collection and model refinement to packaging and final deployment, demonstrating strong applied-engineer execution in medical imaging products.”
        
        generic [ref=e215]:
        
        strong [ref=e216]: “Tools used:”
        
        generic “VUNO tool stack” [ref=e217]:
        
        generic [ref=e218]:
        
        img “PyTorch” [ref=e219]
        
        generic [ref=e220]: PyTorch
        
        generic [ref=e221]:
        
        img “Docker” [ref=e222]
        
        generic [ref=e223]: Docker
        
        generic [ref=e224]:
        
        img “MLflow” [ref=e225]
        
        generic [ref=e226]: MLflow
        
        generic [ref=e227]:
        
        heading “Education” [level=3] [ref=e228]
        
        generic [ref=e229]:
        
        heading “UNIST M.S. in Computer Science · UNIST” [level=4] [ref=e230]:
        
        img “UNIST” [ref=e231]
        
        text: M.S. in Computer Science · UNIST
        
        generic [ref=e232]: Ulsan, Korea · Mar 2019 - Mar 2021
        
        paragraph [ref=e233]: “Cumulative GPA 3.28/4.3, Scholarships: Academic excellence for 4 semesters (2019-2021)”
        
        paragraph [ref=e234]:
        
        strong [ref=e235]: “Thesis:”
        
        text: Neuron segmentation using incomplete and noisy labels via adaptive learning with structure priors
        
        paragraph [ref=e236]:
        
        strong [ref=e237]: “Domestic Patent:”
        
        text: Brain Neural Network Structure Image Processing System, Brain Neural Network Structure Image Processing Method, and a computer-readable storage medium
        
        generic [ref=e238]:
        
        heading “Inje University B.S. in Biomedical Engineering · Inje University” [level=4] [ref=e239]:
        
        img “Inje University” [ref=e240]
        
        text: B.S. in Biomedical Engineering · Inje University
        
        generic [ref=e241]: Gimhae, Korea · Mar 2014 - Aug 2018
        
        paragraph [ref=e242]: “Cumulative GPA 3.89/4.5, Scholarships: Academic excellence for 8 semesters (2014-2018)”
        
        paragraph [ref=e243]:
        
        strong [ref=e244]: “Awards:”
        
        text: “Gold Prize: Inje Creative Comprehensive Design Contest; Bronze Prize: 2017 Engineering Festival Competition; Good Capstone Design Contest”
        
        generic [ref=e245]:
        
        heading “Projects” [level=3] [ref=e246]
        
        generic [ref=e247]:
        
        link “Steel Defect Detection [Detail] Steel Defect Detection PyTorch GAN Anomaly Detection” [ref=e248] [cursor=pointer]:
        
        /url: https://www.digitaltoday.co.kr/news/articleView.html?idxno=500285&rf=toastPopup&utm_source=digitaltoday
        
        generic [ref=e249]:
        
        heading “Steel Defect Detection” [level=4] [ref=e250]
        
        text: “[Detail]”
        
        generic [ref=e251]:
        
        generic [ref=e252]: Steel Defect Detection
        
        generic [ref=e253]:
        
        generic [ref=e254]: PyTorch
        
        generic [ref=e255]: GAN
        
        generic [ref=e256]: Anomaly Detection
        
        link “2D Pathology Object Detection Pathology Detection PyTorch Object Detection Whole Slide Image” [ref=e257] [cursor=pointer]:
        
        /url: /projects/path-quant.html
        
        img “2D Pathology Object Detection” [ref=e258]
        
        generic [ref=e259]:
        
        generic [ref=e260]: Pathology Detection
        
        generic [ref=e261]:
        
        generic [ref=e262]: PyTorch
        
        generic [ref=e263]: Object Detection
        
        generic [ref=e264]: Whole Slide Image
        
        link “3D Lung CT Nodule Detection Lung CT Detection 3D CNN Nodule Detection Medical AI” [ref=e265] [cursor=pointer]:
        
        /url: /projects/lungct-ai.html
        
        img “3D Lung CT Nodule Detection” [ref=e266]
        
        generic [ref=e267]:
        
        generic [ref=e268]: Lung CT Detection
        
        generic [ref=e269]:
        
        generic [ref=e270]: 3D CNN
        
        generic [ref=e271]: Nodule Detection
        
        generic [ref=e272]: Medical AI
        
        link “TIGER Challenge TIGER Challenge Multi-Task Segmentation Grand Challenge” [ref=e273] [cursor=pointer]:
        
        /url: https://github.com/vuno/tiger_challenge
        
        img “TIGER Challenge” [ref=e274]
        
        generic [ref=e275]:
        
        generic [ref=e276]: TIGER Challenge
        
        generic [ref=e277]:
        
        generic [ref=e278]: Multi-Task
        
        generic [ref=e279]: Segmentation
        
        generic [ref=e280]: Grand Challenge
        
        link “MRI Image Analysis MRI Analysis MRI Registration Brain Analysis” [ref=e281] [cursor=pointer]:
        
        /url: /projects/mri-image-analysis.html
        
        img “MRI Image Analysis” [ref=e282]
        
        generic [ref=e283]:
        
        generic [ref=e284]: MRI Analysis
        
        generic [ref=e285]:
        
        generic [ref=e286]: MRI
        
        generic [ref=e287]: Registration
        
        generic [ref=e288]: Brain Analysis
        
        link “Universal Segmentation Segmentation U-Net Semantic Seg Multi-Organ” [ref=e289] [cursor=pointer]:
        
        /url: /projects/universal-segmentation.html
        
        img “Universal Segmentation” [ref=e290]
        
        generic [ref=e291]:
        
        generic [ref=e292]: Segmentation
        
        generic [ref=e293]:
        
        generic [ref=e294]: U-Net
        
        generic [ref=e295]: Semantic Seg
        
        generic [ref=e296]: Multi-Organ
        
        link “Medical Image Reconstruction Image Reconstruction WGAN-GP Super Resolution CT/MRI” [ref=e297] [cursor=pointer]:
        
        /url: /projects/medical-image-reconstruction.html
        
        img “Medical Image Reconstruction” [ref=e298]
        
        generic [ref=e299]:
        
        generic [ref=e300]: Image Reconstruction
        
        generic [ref=e301]:
        
        generic [ref=e302]: WGAN-GP
        
        generic [ref=e303]: Super Resolution
        
        generic [ref=e304]: CT/MRI
        
        link “Test Time Adaptation Test-Time Adaptation Domain Adaptation CNG-SFDA Self-Training” [ref=e305] [cursor=pointer]:
        
        /url: /projects/test-time-adaptation.html
        
        img “Test Time Adaptation” [ref=e306]
        
        generic [ref=e307]:
        
        generic [ref=e308]: Test-Time Adaptation
        
        generic [ref=e309]:
        
        generic [ref=e310]: Domain Adaptation
        
        generic [ref=e311]: CNG-SFDA
        
        generic [ref=e312]: Self-Training
        
        link “Video Action Recognition Video Action Video Action Recognition Temporal” [ref=e313] [cursor=pointer]:
        
        /url: /projects/video-action.html
        
        img “Video Action Recognition” [ref=e314]
        
        generic [ref=e315]:
        
        generic [ref=e316]: Video Action
        
        generic [ref=e317]:
        
        generic [ref=e318]: Video
        
        generic [ref=e319]: Action Recognition
        
        generic [ref=e320]: Temporal
        
        generic [ref=e321]:
        
        heading “Seminar & Teaching” [level=3] [ref=e322]
        
        generic [ref=e323]:
        
        link “FastCampus Medical AI Course 딥러닝을 활용한 의료 영상 처리 & 모델 개발 Part-Time Instructor · FastCampus Inc. Seoul, Korea · Sep 2023 - Dec 2023 [Online Course]” [ref=e324] [cursor=pointer]:
        
        /url: https://fastcampus.co.kr/data_online_medicalai
        
        img “FastCampus Medical AI Course” [ref=e326]
        
        generic [ref=e327]:
        
        heading “딥러닝을 활용한 의료 영상 처리 & 모델 개발” [level=4] [ref=e328]
        
        paragraph [ref=e329]: Part-Time Instructor · FastCampus Inc.
        
        paragraph [ref=e330]: Seoul, Korea · Sep 2023 - Dec 2023
        
        generic [ref=e332]: “[Online Course]”
        
        generic [ref=e333]:
        
        img “Inje University Mentoring” [ref=e335]
        
        generic [ref=e336]:
        
        heading “의용공학과에서 AI연구원되기까지” [level=4] [ref=e337]
        
        paragraph [ref=e338]: Career Mentor · Inje University
        
        paragraph [ref=e339]: Gimhae, Korea · 2023 - 2024
        
        paragraph [ref=e340]: Provided guidance and mentorship to students in computer vision and machine learning projects.
        
        generic [ref=e341]:
        
        img “Gachon University ML Course” [ref=e343]
        
        generic [ref=e344]:
        
        heading “Image Classification · Machine Learning Course” [level=4] [ref=e345]
        
        paragraph [ref=e346]: Instructor · Gachon University (VUNO)
        
        paragraph [ref=e347]: Seongnam, Korea · 2023 - 2024
        
        paragraph [ref=e348]: Taught machine learning fundamentals and applications to university students.
        
        generic [ref=e349]:
        
        heading “Publications & Research” [level=3] [ref=e350]
        
        generic [ref=e351]:
        
        generic [ref=e352]:
        
        img “CNG-SFDA” [ref=e354]
        
        generic [ref=e355]:
        
        ‘heading “CNG-SFDA: Clean-and-Noisy Region Guided Online-Offline Source-Free Domain Adaptation” [level=4] [ref=e356]’
        
        paragraph [ref=e357]: Cho, H., Park, C., Kim, D. H., Kim, J., & Kim, W. H.
        
        paragraph [ref=e358]: ACCV 2024
        
        link “[PDF]” [ref=e360] [cursor=pointer]:
        
        /url: https://openaccess.thecvf.com/content/ACCV2024/html/Cho_CNG-SFDA_Clean-and-Noisy_Region_Guided_Online-Offline_Source-Free_Domain_Adaptation_ACCV_2024_paper.html
        
        generic [ref=e361]:
        
        img “JEPA-SSL” [ref=e363]
        
        generic [ref=e364]:
        
        heading “Joint-Embedding Predictive Architecture for Self-Supervised Learning of Mask Classification Architecture” [level=4] [ref=e365]
        
        paragraph [ref=e366]: Dong, H., Jo, D., Cho, H., Park, C., & Kim, W. H.
        
        paragraph [ref=e367]: arXiv, 2024
        
        link “[PDF]” [ref=e369] [cursor=pointer]:
        
        /url: https://arxiv.org/abs/2407.10733
        
        generic [ref=e370]:
        
        img “Neuron Segmentation” [ref=e372]
        
        generic [ref=e373]:
        
        heading “Neuron segmentation using incomplete and noisy labels via adaptive learning with structure priors” [level=4] [ref=e374]
        
        paragraph [ref=e375]: Park, Chanmin, et al.
        
        paragraph [ref=e376]: ISBI 2021
        
        link “[PDF]” [ref=e378] [cursor=pointer]:
        
        /url: https://ieeexplore.ieee.org/document/9433902
        
        generic [ref=e379]:
        
        img “TIGER Challenge” [ref=e381]
        
        generic [ref=e382]:
        
        ‘heading “Tumor-infiltrating lymphocytes in breast cancer through artificial intelligence: biomarker analysis from the results of the TIGER challenge” [level=4] [ref=e383]’
        
        paragraph [ref=e384]: van Rijthoven, M., … Park, C., … & Ciompi, F.
        
        paragraph [ref=e385]: medRxiv, 2025
        
        link “[PDF]” [ref=e387] [cursor=pointer]:
        
        /url: “#”
        
        generic [ref=e388]:
        
        generic [ref=e390]: 
        
        generic [ref=e391]:
        
        heading “데이터 기반 동적 필터링이 포인트 클라우드 객체 증분 학습에 미치는 영향도 분석” [level=4] [ref=e392]
        
        paragraph [ref=e393]: 진종현, 박찬민, 윤일용
        
        paragraph [ref=e394]: 한국정보과학회 학술발표논문집, 2025
        
        generic [ref=e395]:
        
        generic [ref=e397]: 
        
        generic [ref=e398]:
        
        heading “Development of a KFDA-certified Deep Learning Algorithm for Quantitative Analysis of Ki-67 Immunohistochemical Stains” [level=4] [ref=e399]
        
        paragraph [ref=e400]: Park, C., et al.
        
        paragraph [ref=e401]: 대한병리학회 (Domestic Conference)
        
        generic [ref=e402]:
        
        heading “Patents” [level=4] [ref=e403]
        
        list [ref=e404]:
        
        listitem [ref=e405]: “Method for analyzing medical image (Application number: 1020210139898)”
        
        listitem [ref=e406]: “Method For Analyzing Medical Image based on deep learning (Application number: 1020210139897)”
        
        listitem [ref=e407]: “Brain Neural Network Structure Image Processing System, Brain Neural Network Structure Image Processing Method, and a computer-readable storage medium (Registration number: 1020210139898)”
        
        generic [ref=e408]:
        
        heading “Expertise & Tools” [level=3] [ref=e409]
        
        generic [ref=e410]:
        
        button “ Expertise” [ref=e411] [cursor=pointer]:
        
        generic [ref=e412]: 
        
        text: Expertise
        
        button “ Tools & Technologies” [ref=e413] [cursor=pointer]:
        
        generic [ref=e414]: 
        
        text: Tools & Technologies
        
        generic [ref=e416]:
        
        generic [ref=e417]:
        
        generic [ref=e419]: 
        
        generic [ref=e421]: Object Detection & Segmentation
        
        generic [ref=e422]:
        
        generic [ref=e424]: 
        
        generic [ref=e426]: 3D Vision · Depth · Pose
        
        generic [ref=e427]:
        
        generic [ref=e429]: 
        
        generic [ref=e431]: Sensor Fusion & Robotics Perception
        
        generic [ref=e432]:
        
        generic [ref=e434]: 
        
        generic [ref=e436]: Video Recognition & Tracking
        
        generic [ref=e437]:
        
        generic [ref=e439]: 
        
        generic [ref=e441]: Self-Supervised Vision (DINO/MoCo/MAE)
        
        generic [ref=e442]:
        
        generic [ref=e444]: 
        
        generic [ref=e446]: Embedding & Metric Learning
        
        generic [ref=e447]:
        
        generic [ref=e449]: 
        
        generic [ref=e451]: Domain Adaptation & Continual Eval
        
        generic [ref=e452]:
        
        generic [ref=e454]: 
        
        generic [ref=e456]: Large-Scale Data Pipelines
        
        generic [ref=e457]:
        
        generic [ref=e459]: 
        
        generic [ref=e461]: Edge Deployment & MLOps
        
        generic [ref=e462]:
        
        heading “Awards & Competitions” [level=3] [ref=e463]
        
        list [ref=e464]:
        
        listitem [ref=e465]:
        
        generic [ref=e466]:
        
        generic [ref=e467]: 
        
        generic [ref=e468]: 🥇
        
        text: TIGER Challenge - 1st Place
        
        generic [ref=e469]: VUNO
        
        listitem [ref=e470]:
        
        generic [ref=e471]:
        
        generic [ref=e472]: 
        
        generic [ref=e473]: 🥇
        
        text: HealthHub Datathon - 1st Place
        
        generic [ref=e474]: “2020”
        
        listitem [ref=e475]:
        
        generic [ref=e476]:
        
        generic [ref=e477]: 
        
        generic [ref=e478]: 🏆
        
        text: “Video Recognition: Google - Isolated Sign Language Recognition”
        
        listitem [ref=e479]:
        
        generic [ref=e480]:
        
        generic [ref=e481]: 
        
        generic [ref=e482]: 🥇
        
        text: “Instance Segmentation: HealthHub Datathon Track B - 1st Place”
        
        generic [ref=e483]: “2020”
        
        listitem [ref=e484]:
        
        generic [ref=e485]:
        
        generic [ref=e486]: 
        
        generic [ref=e487]: 🎯
        
        text: “Object Detection: Lesion Detection AI Competition”
        
        link “Code Link” [ref=e488] [cursor=pointer]:
        
        /url: “#”
        
        listitem [ref=e489]:
        
        generic [ref=e490]:
        
        generic [ref=e491]: 
        
        generic [ref=e492]: 🏅
        
        text: “Tabular Classification: Autonomous Driving Sensor - Top 8%”
        
        listitem [ref=e493]:
        
        generic [ref=e494]: Inje Creative Comprehensive Design Contest
        
        generic [ref=e495]:
        
        generic [ref=e496]: 
        
        generic [ref=e497]: 🥇
        
        generic [ref=e498]: Gold
        
        listitem [ref=e499]:
        
        generic [ref=e500]: 2017 Engineering Festival Competition
        
        generic [ref=e501]:
        
        generic [ref=e502]: 
        
        generic [ref=e503]: 🥉
        
        generic [ref=e504]: Bronze
        
        listitem [ref=e505]:
        
        generic [ref=e506]: Good Capstone Design Contest
        
        generic [ref=e507]:
        
        generic [ref=e508]: 
        
        generic [ref=e509]: 🏅
        
        generic [ref=e510]: Excellence
        
        generic [ref=e511]:
        
        heading “Recent Posts” [level=3] [ref=e512]
        
        generic [ref=e513]:
        
        ‘link “2026-02-01 [ROS2] 01: Introduction (ROS2 소개) ROS2 (Robot Operating System 2) 학습 시리즈 ROS2에 대한 기본적인 내용에 대해서 정리하고 설명하고자 한다…. ROS2” [ref=e514] [cursor=pointer]’:
        
        /url: /ros2/2026/02/01/ros2-01-introduction.html
        
        generic [ref=e515]: 2026-02-01
        
        ‘heading “[ROS2] 01: Introduction (ROS2 소개)” [level=4] [ref=e516]’
        
        paragraph [ref=e517]: ROS2 (Robot Operating System 2) 학습 시리즈 ROS2에 대한 기본적인 내용에 대해서 정리하고 설명하고자 한다….
        
        generic [ref=e519]: ROS2
        
        ‘link “2025-12-04 [CS231A] Lecture 10: Optimal Estimation (최적 추정) Stanford CS231A: Computer Vision, From 3D Reconstruction to Recognition 이 포스트는 Stanford CS231A 강의의 열… 3D Geometry” [ref=e520] [cursor=pointer]’:
        
        /url: /3d%20geometry/2025/12/04/cs231a-10-optimal-estimation.html
        
        generic [ref=e521]: 2025-12-04
        
        ‘heading “[CS231A] Lecture 10: Optimal Estimation (최적 추정)” [level=4] [ref=e522]’
        
        paragraph [ref=e523]: “Stanford CS231A: Computer Vision, From 3D Reconstruction to Recognition 이 포스트는 Stanford CS231A 강의의 열…”
        
        generic [ref=e525]: 3D Geometry
        
        ‘link “2025-12-04 [CS231A] Lecture 09: Optical and Scene Flow (광학 및 장면 흐름) Stanford CS231A: Computer Vision, From 3D Reconstruction to Recognition 이 포스트는 Stanford CS231A 강의의 아홉… 3D Geometry” [ref=e526] [cursor=pointer]’:
        
        /url: /3d%20geometry/2025/12/04/cs231a-09-optical-and-scene-flow.html
        
        generic [ref=e527]: 2025-12-04
        
        ‘heading “[CS231A] Lecture 09: Optical and Scene Flow (광학 및 장면 흐름)” [level=4] [ref=e528]’
        
        paragraph [ref=e529]: “Stanford CS231A: Computer Vision, From 3D Reconstruction to Recognition 이 포스트는 Stanford CS231A 강의의 아홉…”
        
        generic [ref=e531]: 3D Geometry
        
        link “More ” [ref=e533] [cursor=pointer]:
        
        /url: /archive.html
        
        generic [ref=e534]: More
        
        generic [ref=e535]: 
      - text: 
  - contentinfo [ref=e537]:
    - generic [ref=e538]:
      - list [ref=e542]:
        
        listitem “Send me an Email.” [ref=e543]:
        
        link “” [ref=e544] [cursor=pointer]:
        
        /url: mailto:pcmin03@gmail.com
        
        generic [ref=e545]: 
        
        listitem “Follow me on Linkedin.” [ref=e546]:
        
        link [ref=e547] [cursor=pointer]:
        
        /url: https://www.linkedin.com/in/chan-min-park-0abab713b
        
        img [ref=e549]
        
        listitem “Follow me on Github.” [ref=e551]:
        
        link [ref=e552] [cursor=pointer]:
        
        /url: https://github.com/https://github.com/pcmin03
        
        img [ref=e554]
      - generic [ref=e557]:
        
        text: © ChanMin Park’s Blog 2021, Powered by
        
        link “Jekyll” [ref=e558] [cursor=pointer]:
        
        /url: http://jekyllrb.com/
        
        text: “&”
        
        link “TeXt Theme” [ref=e559] [cursor=pointer]:
        
        /url: https://github.com/kitian616/jekyll-TeXt-theme
        
        text: .
- generic:
  - generic:
    - generic: Link Preview