## Interpolation Based method for Directional Derivative Calculation in Image Processing 1.0

Directional derivative calculation can be done by using a number of kernels in image processing. Most of them are fixed and can only use in single direction. This article attempts to discuss and use a geometrical model to as a solution for this situation.

Considering the gradient calculation in image processing, since it is discrete domain, calculations are done by forward and backward differences. Therefore co-efficients of pixels can directly use for built a kernel which can convolve with a given image to determine the gradient.

Consider two pixels *f(x,y) *and *f(x _{r},y_{r})* of an image which are in

**direction with**

__r__**distance. Then the directional derivative can be written as;**

*|*__r__|

**CASE 1 : 0****≤θ≤45 ^{o}**

This minimised to,

Therefore we can finalise the kernel ** K** for 0≤θ≤45

^{o},

By using the same approach, we can conclude the kernels for **0****≤θ≤180 ^{o }**as;

## RESULTS FOR ROTATION INVARIENT IMAGE

Figure 1:Derivative Angle and Sum of Directional Derivatives

great work..

Comment by කණිෂ්ක | 2010 November 27 |

nice work machan

Comment by janitha | 2010 December 13 |

Hi Thilina,

can you provide matlab codes for this?

Thank you.

Comment by Michael | 2011 January 23 |

Hi Michael,

I’m working on another kernel, which can use for directional derivative calculations also simpler and effective than this one, I’ll add codes for this kernel and for the never one on my next article,

Thanks for the comment..!

Comment by Thilina S. | 2011 January 23 |