Useful Links
Computer Science
Computer Vision
Computer Vision and Image Analysis
1. Foundations of Computer Vision
2. Image Formation and Representation
3. Fundamental Image Processing
4. Feature Detection and Description
5. Image Segmentation
6. Classical Object Recognition
7. Deep Learning for Computer Vision
8. Motion and Video Analysis
9. 3D Computer Vision
10. Computer Vision Applications
Fundamental Image Processing
Point Operations
Intensity Transformations
Linear Transformations
Nonlinear Transformations
Piecewise Linear Functions
Histogram Processing
Histogram Computation
Histogram Equalization
Histogram Specification
Local Histogram Processing
Thresholding Operations
Global Thresholding
Adaptive Thresholding
Multi-level Thresholding
Optimal Threshold Selection
Spatial Domain Filtering
Convolution Operations
Kernel Design
Convolution vs. Correlation
Separable Filters
Boundary Conditions
Smoothing Filters
Box Filter
Gaussian Filter
Median Filter
Bilateral Filter
Non-local Means
Sharpening Filters
Laplacian Operator
Unsharp Masking
High-boost Filtering
Gradient-based Sharpening
Edge Enhancement
First-order Derivatives
Second-order Derivatives
Directional Derivatives
Frequency Domain Processing
Fourier Transform Theory
Continuous Fourier Transform
Discrete Fourier Transform
Fast Fourier Transform
Properties and Theorems
Frequency Domain Filtering
Ideal Filters
Butterworth Filters
Gaussian Filters
Filter Design Principles
Applications
Noise Removal
Image Enhancement
Pattern Analysis
Compression
Morphological Operations
Binary Morphology
Structuring Elements
Dilation Operation
Erosion Operation
Hit-or-Miss Transform
Compound Operations
Opening Operation
Closing Operation
Morphological Gradient
Top-hat Transform
Grayscale Morphology
Grayscale Dilation
Grayscale Erosion
Reconstruction Operations
Applications
Noise Removal
Shape Analysis
Skeletonization
Boundary Extraction
Previous
2. Image Formation and Representation
Go to top
Next
4. Feature Detection and Description