Computer Vision Projects

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

Dependencies:

• OpenCV
• NumPy
• Matplotlib

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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.