# Image Processing

*In cooperation with Alexandra Bodzás*

01 – Introduction to Vision

Introduction to the vision library of NI – Labview.

http://www.ni.com/labview/vision/

02 – Image thresholding

03 – Convolution

Convolution, and convolution theorem on the image.

04 – Discrete Fourier transform

Application of Fourier transform in discrete form on the image, together with inverse transformation (DFT / IDFT)

05 – Filtration in frequency domain

Filtration based on frequency domains of image.

06 – Histogram equalization

07 – Geometric transformation

Scale, rotation, translation. Application of the affine transfrmation.

08 – Edge detection

Detection of the edges in the image.

09 – Object labeling

10 – Features computation

Computation of features based on the objects in original image

11 – Classification with etalons

Classification of the image based on the goldesn standard (etalon).

12 – K-measn classification

Self learning classification of the K-means