computer vision

Computer Vision laboratory tasks

Implemented the Semi-Global Matching (SGM) algorithm in C++ for computing dense disparity maps from stereo image pairs. Utilized OpenCV for image processing operations. The project involved computing matching costs using Census Transform and Hamming distance, performing cost aggregation along multiple paths, and estimating the final disparity map while incorporating smoothing constraints and confidence thresholding. Evaluated the disparity estimation accuracy by computing the mean squared error against ground truth data.

Generate ALT text (captions for low vision website users or book readers).

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Marine biologists engaged in the study of coral reefs invest a significant portion of their time in manually processing data obtained from research dives. The objective of this challenge is to create an image segmentation pipeline that accelerates the analysis of such data. This endeavor aims to assist conservationists and researchers in enhancing their efforts to protect and comprehend these vital ocean ecosystems. Leveraging computer vision for the segmentation of coral reefs in benthic imagery holds the potential to quantify the long-term growth or decline of coral cover within marine protected areas.

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This is an additional implementation for a robot framework for the SLAM using camera, pose estimation and image segmentation of human. This is used with path planning techniques that allow human and robot to cohabitate in shared environment.

Códigos desenvolvidos ao longo das disciplinas que cursei

Train YOLOv5 for breed classification on Oxford Pets III dataset from scratch on Google Colab, and serve through Dockerized implementation of a flask-based HTML/JS frontend and an asynchronous API service on FastAPI. Use of MLFlow for logging.