README.md

exVision: BasicToolkit v2

Warning

The "exVision-BasicToolkit" repository was the initial version of our work, containing basic computer vision and image processing tools. While the core logic functions properly, the GUI application may not perform efficiently. For a more organized and optimized version with additional features and better resource management, please refer to the exVision repository.

Table of Contents

1. Overview
2. Getting Started with the GUI
3. Acknowledgments
4. References
   • Threshold-Transformation and Frequency Domain Filters
     • Useful Links for Understanding
     • Image Resizing and Interpolation
     • Low and High Pass Filters
     • Common Intensity Transformation Functions (Optional)
   • Image Histograms and Equalization
   • Histogram Equalization Methods
     • [Global Histogram Equalization (GHE)]
     • [Contrast Limiting Adaptive Histogram Equalization (CLAHE)]
     • [Multipurpose Beta Optimized Bihistogram Equalization (MBOBHE)]
     • [Additional Topics]
   • Normalization
   • Hough Line Transform

Overview

BasicToolkit is a PyQt desktop application that offers basic image processing tools and classical computer vision techniques built from scratch. It includes the following features:   - Additive Noise: Uniform, Gaussian, and Salt & Pepper noise   - Filters: Mean, Median, and Gaussian filter   - Edge Detection: Canny, Laplacian, Prewitt, Roberts, Sobel   - Equalization and Normalization   - Frequency Filters and Hybrid Images   - Local and Global Thresholding

Want to know more? Refer to the latest version of the documentation!

Also, in this folder, you will find the pure implementation of these features along with the from-scratch implementation of the Active Contouring (SNAKES) and Hough Transform that aren't integrated in the GUI in this repo, but they're in the exVision repo.

Getting Started with the GUI

Don't use this version :) Refer to the exVision repository.

Acknowledgments

Refer to this organization's README for more details about contributors and supervisors.

References

Threshold-Transformation and Frequency Domain Filters

Useful Links for Understanding

• Histogram: Watch this video on histograms
• Thresholding: Learn about image thresholding and thresholding from scratch

Image Resizing and Interpolation

• Image Resizing Algorithm: Stack Overflow discussion
• Nearest Neighbor Interpolation: Read about nearest neighbor interpolation on ScienceDirect and K-Nearest Neighbors from Scratch

Low and High Pass Filters

• Watch this video.

Common Intensity Transformation Functions (Optional)

• Learn about log, exp, and negation transformations: GeeksforGeeks article (Skip the math part)

Image Histograms and Equalization

• Introduction to Histograms: Watch this tutorial

• Histogram Equalization Overview: Wikipedia article and OpenCV tutorial

• Introduction to Histogram Equalization: Watch this video

• Overview of Color Spaces: Watch this helpful video and read about conversion rules

• Types of Histograms

  • RGB Histogram
  • HSL
  • HSV
  • Intensity Level Histogram (Grey Image)
  • CIELAB: Learn more in the Python Image Processing Cookbook and explore code in C++

Histogram Equalization Methods

• Global Histogram Equalization (GHE):

  • Explanation with a numeric example
  • Another example with theoretical explanation

• Contrast Limiting Adaptive Histogram Equalization (CLAHE):

  • Watch the OpenCV tutorial

• Multipurpose Beta Optimized Bihistogram Equalization (MBOBHE):

  • Read the original paper

• Additional Topics

  • Pseudo Coloring
  • Negative Coloring (RGB, CIELAB, etc.)

Normalization

• Learn 5 ways to normalize an image

Hough Line Transform

• Hough Line Transform: Explanation and Implementation