Table of Contents
Vision
Color Quantization
References
RGB cone
YUV polygon
HSV rectangle
Lab
Image segmentation
Floodfill
References
Watershed
References
Filters
References
Anti-noise
(smoothing)
References
Median Filter
Vector Median Filter
Kuwahara filter
Peer Group Filtering
References
Anisotropic Filtering
Gradient
Prewitt
Objective
Basic kernels for 1st order gradient.
Quick Def
Horizontal kernel (transpose for vertical) :
| -1 | 0 | 1 |
|---|---|---|
| -1 | 0 | 1 |
| -1 | 0 | 1 |
Roberts
Objective
Quick Def
First kernel :
| 1 | 0 |
|---|---|
| 0 | -1 |
Second kernel :
| 0 | 1 |
|---|---|
| -1 | 0 |
Intensity :
I = sqrt(I1^2 + I2^2)
Direction :
theta = arctan(I2 / I1) + pi/4
Sobel
Objective
Most popular 1st order kernels
Quick Def
Horizontal kernel (transpose for vertical) :
| -1 | 0 | 1 |
|---|---|---|
| -2 | 0 | 2 |
| -1 | 0 | 1 |
Intensity :
I = sqrt(Iv^2 + Ih^2)
Direction :
theta = arctan(Iv / Ih)
Full Def
The filter can be seen as the convolution of two filters.
Derivative :
| -1 |
|---|
| 0 |
| 1 |
Smoothing :
| 1 | 2 | 1 |
|---|
Scharr
Objective
May give more accurate results than Sobel
Quick Def
Horizontal kernel (transpose for vertical) :
| -3 | 0 | 3 |
|---|---|---|
| -10 | 0 | 10 |
| -3 | 0 | 3 |
Laplace
Objective
2nd order kernel
Quick Def
Kernel :
| 0 | 1 | 0 |
|---|---|---|
| 1 | -4 | 1 |
| 0 | 1 | 0 |
Morphological
References
Dilation
Dilatation
Erosion
Erosion
Opening
Ouverture
Closing
Fermeture
Edge detection
Canny detector
Objective
Best theoritical edge detector.
Quick Def
Noise filtering, gradient with horizontal vertical and diagonal kernels, thresholding with hysteresis taking into account the direction of the gradient.
