Welcome to the Stroud Lab and OMSCS collaboration through the Human-Augmented Analytics Group (HAAG). In this partnership, we develop innovative tools to enhance the lab’s mission of using lizards to uncover broader patterns of biological diversity.
Spring 2025 Projects
- Lizard Classification:
- The Florida Anole Species Classification project aims to develop a robust classification pipeline for identifying five common Anolis species from photographs, primarily to support a community science initiative with middle school students in Miami. Building upon an extensive dataset of over 80,000 verified iNaturalist photographs, this project seeks to improve the current classification system, which, despite having access to substantial training data, currently achieves only 35% accuracy (compared to a random baseline of 20%). The development of this classification pipeline will serve as the foundation for a broader educational tool, whether implemented as a mobile application or web platform, that enables students to receive immediate probability-based species identification feedback before submitting their observations to iNaturalist, thereby enhancing the quality of citizen science data collection while engaging young students in herpetological research.
- Lizard Movement:
- The Lizard Locomotion Analysis project aims to develop a comprehensive understanding of lizard running behavior through advanced computer vision techniques, specifically utilizing DeepLabCut and SLEAP for precise pose estimation and movement analysis. By creating detailed anatomical landmarks across the lizard’s body, this project will enable quantitative analysis of various locomotor patterns and behavioral characteristics, moving beyond traditional center-of-mass tracking to capture complex biomechanical interactions during movement. Through this open-source approach, we will document multiple behavioral patterns across different body segments, contributing to a broader understanding of lizard locomotion while establishing standardized protocols for future research in animal biomechanics. This work will not only advance our understanding of lizard movement patterns but also contribute to the growing field of automated behavioral analysis in biological research
- Lizard Jaw Segmentation:
- The Lizard Jaw Segmentation project aims to automate the segmentation of teeth and lower jaws of Anolis lizards’ 3D Micro CT scans. Traditional segmentation of the scans requires manual efforts using custom software, which can take up to two hours per scan. Automating the process will significantly reduce process time, which will allow researchers to conduct larger-scale data collection and comparative studies across not just different Anolis lizards, but also different species.
- Lizard Lidar Project:
- The project aims to develop innovative algorithms for processing and analyzing terrestrial LiDAR (Light Detection and Ranging) scans of natural vegetation, with the goal of creating comprehensive, open-source software packages in Python or R. This project will focus on creating novel computational methods to extract, process, and analyze complex vegetation structure data from LiDAR point clouds, enabling more accurate and automated assessment of natural vegetation characteristics. We are especially interested in segmenting and measuring elements of branch and vegetation structure. By developing these algorithms into accessible software packages, the project will provide the scientific community with robust tools for vegetation analysis, supporting applications in ecology, forestry, and environmental monitoring.
- Lizard Morph Project
- The project is in collaboration with the Stroud lab in the Department of Biological Sciences, within the domain of Ecology and Evolution. The research question is to investigate the morphology to fitness connection in adaptive evolution, and specifically, the performance cost to missing a leg in lizards. Given videos of experimental trials involving lizards jumping and sprinting, the team uses DeepLabCut (DLC) to track the positions of key body parts, from which biophysical information of interest to biologists can be extracted.