About

I'm a Senior Machine Learning Engineer at Pento and a PhD in Computer Vision with over 5 years of experience developing deep learning solutions for image segmentation and analysis.

I completed my PhD thesis on automatic tree-ring detection and analysis, developing novel deep learning architectures and open-source tools now used by the dendrochronology research community worldwide. This work was recognized with First Prize in the Electrical Engineering category by the National Academy of Engineering of Uruguay (ANIU).

Awards & Recognition

First Prize — Doctoral Thesis in Electrical Engineering 2025
National Academy of Engineering of Uruguay (ANIU), awarded for the doctoral thesis Application of Image Processing and Artificial Intelligence Techniques for the Automatic Dendrometry of Native and Commercial Wood Species.

Research

My research focuses on creating efficient, explainable, and impactful AI systems for challenging domains including dendrochronology, medical imaging, and scientific image analysis. I specialize in deep learning architectures for image segmentation, boundary detection, and real-world image analysis workflows.

Projects

TRAS – Tree Ring Analyzer Suite

Professional dendrochronology software with state-of-the-art CV & DL methods. A complete GUI application built with PyQt5 for automatic tree-ring detection and measurement. It integrates multiple detection methods (APD, CS-TRD, DeepCS-TRD), preprocessing tools, scale calibration, and professional data export capabilities.

Python PyQt5 PyTorch OpenCV

DeepCS-TRD – Deep Learning for Tree Ring Detection

State-of-the-art U-Net-based tree-ring segmentation. A deep learning method that outperforms traditional approaches on challenging samples, and includes annotated datasets, benchmarking scripts, and pre-trained models. It helps reduce analysis time from hours to minutes.

PyTorch U-Net Computer Vision

Selected Publications

For a complete list, visit my Google Scholar profile

Recent Journal & Conference Papers

  1. Marichal, H., Blanco, J., Passarella, D., Randall, G. (2025). UruDendro4: A Benchmark Dataset for Automatic Tree-Ring Detection in Cross-Section Images of Pinus taeda L. IEEE 15th International Conference on Pattern Recognition Systems (ICPRS-25).

    Article Dataset

  2. Marichal, H., Casaravilla, V., Power, C., Mello, K., Profumo, L., Mazarino, J., Lucas, C., Passarella, D., Randall, G. (2025). DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring Detector. International Conference on Image Analysis and Processing (ICIAP).

    Project Page

  3. Marichal, H., Passarella, D., Lucas, C., Profumo, L., Casaravilla, V., Rocha Galli, M. N., Ambite, S., Randall, G. (2025). UruDendro, a public dataset of 64 cross-section images and manual annual ring delineations of Pinus taeda L. Annals of Forest Science.

    Article

  4. Marichal, H., Passarella, D., Randall, G. (2025). CS-TRD: a Cross-Section Tree Ring Detection Method. Image Processing On Line (IPOL).

    Article

  5. Marichal, H., Passarella, D., Randall, G. (2024). Automatic Wood Pith Detector: Local Orientation Estimation and Robust Accumulation. International Conference on Pattern Recognition (ICPR).

    Code

Under Review & Submitted

  1. Marichal, H., Passarella, D., Randall, G. (2024). TRAS: An Interactive Software for Tracing Tree Ring Cross Sections. Under Review.

  2. Marichal, H., Power, C., Treier, U. A., Resente, G., Normand, S., Randall, G. (2024). Assessing automatic ring detection on microscopy images of Salix glauca. Under Review.

Thesis

  1. Marichal, H. (2025). Application of Image Processing and Artificial Intelligence Techniques for the Automatic Dendrometry of Native and Commercial Wood Species. Doctoral Thesis in Electrical Engineering, Universidad de la República. (Advisors: Gregory Randall and Diego Passarella).

    Read Thesis