This repository contains a selection of coursework projects from the Computer Vision course at Rensselaer Polytechnic Institute (Spring 2025). The assignments cover core topics in image analysis, feature extraction, classification, and neural networks, implemented using Python and OpenCV.
📷 Image Difference Analysis
Detects and highlights differences between pairs of images using pixel-based and histogram comparison. Implemented basic thresholding, differencing, and noise filtering.
🔍 Feature Detection
Used OpenCV to extract edges, contours, and key visual features using techniques such as Canny edge detection and intensity-based filtering.
🧠 CNN Image Classification
Trained a convolutional neural network from scratch to classify images from a simplified dataset. Implemented training loop, loss function, and accuracy evaluation.
📊 Histogram Matching & Intensity Transforms
Manipulated image histograms to normalize contrast, perform equalization, and match target image distributions. Visualized the results and intermediate steps.
- Python 3.x
- OpenCV
- NumPy
- Matplotlib
- PyTorch / TensorFlow for CNN demos
All code is written by Qinwen Tang as part of coursework submissions. Starter code provided by RPI faculty has been excluded or rewritten to ensure this repository only contains my original work.
Visual examples and documentation will be added progressively.