Computer Vision Lab

Exploring computer vision techniques and algorithms. Including spatial and frequency domain filtering, transformation, and color spaces.

Contents:

• Intensity Transformation and Spatial Filtering: All the filtering and transformation is done manually without using image processing libraries.

  • Converting RGB to Grayscale
  • Finding min and max of intensity
  • Finding the mean of intensity
  • Finding the variance of intensity
  • Detecting vertical edges
  • Detecting horizontal edges
  • Detecting vertical and horizontal edges
  • Detecting diagonal edges
  • Applying the Laplacian edge detector
  • Bit slicing using bits 8 and 7
  • Bit slicing using bits 8 and 7 and 6
  • Bit slicing using bits 4 to 1
  • Calculating the histogram
  • Histogram and contrast
  • Reducing brightness
  • Calculating the histogram of a bit sliced image
  • Adding salt and pepper noise to an image
  • Applying mean filter
  • Applying gaussian filter
  • Applying median filter
  • Applying max filter
  • Applying min filter
  • Comparing statistical filters
  • Scaling up an image by a factor of 2
  • Rotating an image by 30 degrees
  • Streching vertically
  • Streching horizontally
  • Streching horizontally and vertically

• Filtering in the Frequency Domain

  • Calculating the fourier transform of a spatially trasformed image
  • Analyzing the fourier transform of an image
  • Remove salt and pepper noise in the frequency domain
  • Fourier transform of a rotated image
  • Fourier transform of a translated image
  • Edge detectiong using band reject filters
  • Smoothing using band reject filters

• Color Spaces

  • Converting RGB to HSI manually
  • Color slicing in HSI
  • Color slicing in RGB
  • Gamma correction
  • Saturation adjustment
  • Hue shifting