Research

Publications

Published and preprint work across computer graphics, medical imaging AI, and formal verification.

03 entries

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Venue
PreprintJournal ArticleConference Paper
Areas
Computer GraphicsMedical Imaging AIFormal Verification
  1. 01 Preprint Computer Graphics arXiv preprint (cs.GR, cs.CV) August 2026

    What Will This Copper Look Like Later? Forecasting Surface Appearance and Rendering It as a PBR Material

    Teejuta Sriwaranon, Borworntat Dendumrongkul, Tanapat Chamted, Pizzanu Kanongchaiyos

    A system that forecasts how copper surfaces evolve as they oxidize and renders the predicted appearance as a PBR material (albedo, normal, roughness, metallic) from a single camera observation. A learned spatio-temporal model underperforms last-frame copying on unseen specimens, while a closed-form global color extrapolation transfers successfully, improving accuracy by 13.4-16.7% depending on the prediction horizon, indicating that learned models encode specimen-specific corrosion patterns whereas global color trajectories generalize.

    Appearance ForecastingPBR MaterialComputer GraphicsSurface OxidationSpatio-Temporal Modeling
    DOI 10.48550/arXiv.2608.28102 PDF Code
    BibTeX 
    @misc{sriwaranon2026copper,
  title = {{What Will This Copper Look Like Later? Forecasting Surface Appearance and Rendering It as a PBR Material}},
  author = {Teejuta Sriwaranon and Borworntat Dendumrongkul and Tanapat Chamted and Pizzanu Kanongchaiyos},
  year = {2026},
  eprint = {2608.28102},
  archivePrefix = {arXiv},
  primaryClass = {cs.GR},
  doi = {10.48550/arXiv.2608.28102},
  url = {https://arxiv.org/abs/2608.28102}
}
  2. 02 Journal Article Medical Imaging AI IEEE Access June 2026

    An End-to-End Deep Learning Pipeline for Automated Mandible Virtual Surgical Planning Using Real-World Clinical Data

    Nattapon Kamboonsri, Teejuta Sriwaranon, Natdanai Tantisereepatana, Chedtha Puncreobutr, Boonrat Lohwongwatana, Gregory B. Olson, Alessandro Tel, Massimo Robiony, Titipat Achakulvisut, Peerapon Vateekul

    An end-to-end deep learning pipeline that automates mandible virtual surgical planning from real-world clinical data, combining volumetric segmentation and reconstruction to streamline the workflow for maxillofacial surgery.

    Deep LearningVolumetric SegmentationVolumetric ReconstructionAutomated PipelineVirtual Surgical Planning
    DOI 10.1109/ACCESS.2026.3702327
    BibTeX 
    @article{kamboonsri2026endtoend,
  title = {{An End-to-End Deep Learning Pipeline for Automated Mandible Virtual Surgical Planning Using Real-World Clinical Data}},
  author = {Nattapon Kamboonsri and Teejuta Sriwaranon and Natdanai Tantisereepatana and Chedtha Puncreobutr and Boonrat Lohwongwatana and Gregory B. Olson and Alessandro Tel and Massimo Robiony and Titipat Achakulvisut and Peerapon Vateekul},
  year = {2026},
  journal = {IEEE Access},
  doi = {10.1109/ACCESS.2026.3702327},
  url = {https://ieeexplore.ieee.org/document/11557298}
}
  3. 03 Conference Paper Formal Verification 2026 23rd International Joint Conference on Computer Science and Software Engineering (JCSSE) June 2026

    Fine-Grained Formal Verification of an Asynchronous Speaker Diarization Pipeline Using Hierarchical Timed Colored Petri Nets

    Teejuta Sriwaranon, Nuengwong Tuaycharoen, Wiwat Vatanawood

    A fine-grained hierarchical Timed Colored Petri Net (HTCPN) model of an asynchronous speaker diarization pipeline that exposes three generalizable structural deadlock patterns in concurrent AI pipelines. Seven correctness properties are verified, including a novel attribution consistency property, showing that sub-module decomposition is necessary for complete pipeline verification.

    Formal VerificationHierarchical Petri NetsSpeaker DiarizationReachability AnalysisDeadlock FreedomLiveness
    DOI 10.1109/JCSSE68839.2026.11597080
    BibTeX 
    @inproceedings{sriwaranon2026finegrained,
  title = {{Fine-Grained Formal Verification of an Asynchronous Speaker Diarization Pipeline Using Hierarchical Timed Colored Petri Nets}},
  author = {Teejuta Sriwaranon and Nuengwong Tuaycharoen and Wiwat Vatanawood},
  year = {2026},
  booktitle = {2026 23rd International Joint Conference on Computer Science and Software Engineering (JCSSE)},
  doi = {10.1109/JCSSE68839.2026.11597080},
  url = {https://ieeexplore.ieee.org/document/11597080}
}