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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