// welcome

Hussain Habib

I work on

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A bit about me.

Hussain Habib

I am Hussain, an undergraduate researcher working at the intersection of perception and intelligent systems. I build things that help machines see, understand, and act in the physical world.

My primary focus is computer vision ranging from classical image processing to deep learning-based detection and segmentation. I am fascinated by how visual understanding can be paired with robotics to create autonomous systems that navigate, manipulate, and make decisions in real environments. Lately, I have been diving deep into applied ML, particularly vision-language models (VLMs) that bridge the gap between seeing and reasoning. I have also been putting these skills to work in precision agriculture using drones, LiDAR, and remote sensing to phenotype crops and automate field analysis.

// focus areas

Computer Vision Robotics Applied ML Vision-Language Models Precision Agriculture

Things I've built.

Open-Vocabulary Semantic Room Navigation
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ROS 2 · YOLO-World

Open-Vocabulary Semantic Room Navigation

YOLO-World-powered robot that classifies rooms on sight, skips irrelevant ones, and remembers layouts across missions, distributed across Pi 5 and laptop.

BAGEL: Browser ROS Bag Visualizer
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ROS · WebAssembly

BAGEL: Browser ROS Bag Visualizer

Zero-install web app for ROS1 (.bag) and ROS2 (.mcap, .db3) files with 3D point clouds, time-series plots, and TF-aware rendering, fully client-side via Web Workers and WASM.

3D Scene Reconstruction Virtual Tour
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Structure from Motion

3D Virtual Tour

Incremental SfM pipeline reconstructing 200K+ 3D points from 30 images with bundle adjustment, served as an interactive Three.js virtual tour.

Pick and Drop
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Marker Detection

Pick & Drop

Robomaster robot with computer vision and PID control to detect markers, pick up objects, and place them at target locations.

EV Signs and Charger Detection
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Transfer Learning

EV Signs & Charger Detection

YOLOv4 deep learning to detect EV signs, charging stations, and accessible parking signs achieving 74% mAP via transfer learning.

Obstacle Mapping
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ROS Simulation

Obstacle Mapping

Turtlebot3 in ROS1/Gazebo detecting obstacles, mapping positions, and generating 2D environment visualizations.

Research work.

Computers and Electronics in Agriculture · Elsevier · Vol. 246C

UAV-based crop reconstruction and trait estimation using RGB imagery without external geospatial infrastructure

Muhammad Hussain Habib Chaudhry, Muhammad Ibrahim Rana, Hassan Jaleel

Infrastructure-free UAV phenotyping using only RGB imagery and a consumer drone. Dual-altitude RANSAC-corrected orthomosaics achieve 94.95% plant counting and 91.59% panicle detection accuracy, with strong DEM-ground truth correlation (R = 0.876) — all without GCPs or RTK-GPS.

March 2026 Article 111677

Continuous learning.

Vision Language Models (VLM) Bootcamp

OpenCV · OpenCV University

Advanced Computer Vision with TensorFlow

DeepLearning.AI · Coursera

Advanced Deep Learning

MathWorks · Coursera

Camera and Imaging

Columbia University · Coursera