Computer Vision Projects

This repository is made for the Computer Vision course projects - Fall 2018.

Dependencies:

This repository contains implementation of the following projects in Python using OpenCV:

Connected Components
- Detecting a specific type of connected components.
- Counting the number of connected components in an input image.
Corner Detection (Harris - Sobel)
- Implementing Harris corner detection algorithm using Sobel.
Face Detection (Haar cascade)
- Detecting faces of an input image by using Haar cascade.
Histogram Stretching and Histogram Clipping
- Processing an image with Histogram Stretching and Histogram Clipping.
Line segmentation (RANSAC - LSD - Hough)
- Circle detection using OpenCV's HoughCircles.
- Implementing RANSAC line detection in Python.
- Detecting rectangles using Line Segment Detector and Hough Algorithm.
Live Face Detection (Kalman)
- Live face detection using built-in OpenCV's methods and Kalman filter.
Noise Reduction (Smoothing - Median)
- Removing Gaussian and Salt-and-pepper noise from an input image by applying Smoothing and Median filters.
Noise Reduction (discrete Fourier transform)
- Removing periodic noise from an input image by applying Discrete Fourier Transform.
Noise Reduction (Gaussian - Mask)
- Removing noise from an input image by applying Gaussian and Mask filters.
Automatic Image Thresholding (Otsu - Adaptive)
- Comparing global and adaptive thresholding on an input image.
- Implementing Otsu method for Automatic Image Thresholding.
Scanner
- Implementing a simple document scanner in Python with OpenCV.

Provide feedback