## Basics

**Description:**
Elementary access to pixel data of S&W- and color images using OpenCV.

### Screenshots:

### Downloads:

- Basics V. 1.0
- Source code
- ZIP (download zip)

## HoughTr

**Description:**
Simple examples for using the OpenCV library. This program detects circles/lines on images
of a video device using the Hough transformation and marks them with a green frame/red lines.
The program finishes by keypress and saves the actual image to a JPEG picture.

### Screenshots:

### Downloads:

- HoughTr V. 1.0
- HoughCircle
- ZIPed source (download zip)

- HoughTr V. 1.0
- HoughLines
- ZIPed source (download zip)

## oFlow

**Description:**

Detection of optical flow in images. The programs detects good points to track
using the maximal-eigenvalues method (*goodFeaturesToTrack()*) and marks
these with green circles.

Then it calculates the optical flow for these points using the Lucas-Kanade method and displays the flow using red arrows.

The program finishes by keypress and saves the actual image to a JPEG picture.

### Screenshots:

### Downloads:

- oFlow V. 1.0
- OpenCVOpticalFlow
- ZIPed source (download zip)

## ovlDen

**Description:**

Reduction of background noise for star images by shift-compensated additional overlay of multiple images of the same region. For motion compensation between the images you can choose between two methods

### Normalized crosscorrelation spectrum method

For this method the spectrum of the crosscorrelation of actual image A and its sucessor B will be calculated as Hc=fft(A).*conj(fft(B)). Then this spectrum will be magnitude-normalized so it contains only the phase information.

Another viewpoint is to see this spectrum as the spectrum of a shifted delta peak (when only shifting occurs between the images!). Convoluting B with the shifted delta peak would perform the shifting operation between B and A.

This convolution will be done in frequency domain to correct the shifting of B: fft(B')=fft(B).*Hc. This can be done with the overlay operation as Ha = 0.5*(Ha+Hb.*Hc) where Ha=fft(A) and Hb=fft(B) (all fft's are seperable two-dimensional).

The crosscorrelation spectrum can be calculated from thresholded copies of the images by choice to improve the Delta-peak character of the stars.

This process will be repeated with further images with respect to the result of the last averaging.

### Feature Tracking

This method tracks the motion of a triangle from A to B to compute the affine transformation which maps A to B.
The triangle is build up from the three best-to-track points computed by the maximal-eigenvalue-method (*goodFeaturesToTrack()*-Funktion
of *OpenCV*).

The motion of the triangle is then computed by the Lucas-Kanad-method for calculating the optical flow of points. From the movement an affine Transformation will be calculated which is used to map the actual image A to its successor image B before overlaying them. For the next iteration the result is the actual image.

### Screenshots:

### Downloads:

- ovlDen V. 1.0
- OpenCVoverlayDenoise
- ZIPed source (download zip)