A repository containing all the projects done during the 7th semester university subject "Computer Vision" The code is written in Python mostly using OpenCV (and for the final project Keras)
In this project we must: Given 2 same images from a microscope (One with added Salt and Pepper Noise)
- Create a Median filter in order to remove the noise from the second image
- Count the number of cells that are inside the image boundaries .
- Count the area (in pixels) of each cell
- Calculate the average grayscale value for the pixels inside the bounding box of each cell, using a method with constant time complexity (Integral Image)
In this project we create a Panoramic photo by stitching 4 photos, by extracting their features using the SURF and SIFT algorithms
In this project we classify part of the Caltech-256 Dataset by training a BoV model using k-means and then using the following classifiers:
- Support Vector Machines (One vs all)
- K nearest neighbors
For all methods many hyperparameter comparisons were made
In the final project we classify the same dataset of the previous one using
- Different Deep Neural Network architectures
- Data Augmentations
- Transfer Learning