
IMPROV - Help File


1. IMPROV - Image Processing for Robot Vision

IMPROV is a simple tool to test image processing functions.
Images are taken from the QUICKCAM. Operations may be concatenated, so
even a more complex task can also be tested. The captured camera image is
shown in the image window #1.

2. Selecting and adding image processing operations

The dropchoice on top of each image allows for the selection of
an image processing operation to be performed on the previous image
and displayed in the current image window.
The chosen operation is displayed in a browser control below the
image window. Multiple operations can be successively applied by
concatenating them pushing the '+' button to the left of the browser.
As long as the operation is not appended to the existing list of
operations it may be replaced by some other selection from the dropchoice.
Some operations take arguments like the threshold function. Up to three
paramters can be set using the slider controls labeld p1, p2 and p3.
Paramter values lie in the range [0.0..1.0]. A 0.5 value for the threshold
functions means an actual greyscale threshold of 8 (4bpp = 16 different
grey values).


3. Modifying and deleting image processing operations

Changes of the parameter slider controls are effective for the most
recent selected image processing operation. Parameters can be changed
afterwards by first selecting them in the corresponding browser and then
adjusting the parameter slider. A selected operation gets highlighted the
'+' push button changes into a '-' push button. Pressing it will remove the
operation from the list.

4. The image processing operations

Here is a brief summary of the image processing operations available
in IMPROV.

NOP		No Operation
Identity  	Copy source image to target image
Negation	Negate source image (p`=maxcol-p)
Dither(2x2)	Target image contains only black/white pixels
Difference	Difference image of source and previous source
Count		Count pixels of a given (P1) grey value. Result is shown
		in text window.
Threshold	Binarize image. Threshold is given by P1

Minimum	3x3 Minimum Filter
Maximum	3x3 Maximum Filter
Mean   		3x3 Mean Filter
Median	   	3x3 Median Filter
Laplace   	3x3 Laplace Filter
Sobel		3x3 Sobel Filter (absolute gradient value)
Corner		3x3 Corner Filter

Erosion	    	3x3 Erosion
Dilation    	3x3 Dilation
Open	  	3x3 Erosion and then Dilation
Close		3x3 Dilation and the Erosion
Fill
Connected
Boundary
Skeleton

Noise		Generate Noise in target image (P1=degree of noise)
Grey stretch	Make use of all grey values
Grey reduce
Show (x,y)	Print grey value of pixel at (x=P1,y=P2)
Overlay		Copy all pixels of the original where source image pixels
		are black
Region (t)	Segementation (t=P1 as threshold)
Find Circles	Find small circles in source images



5. Links

IMPROV - Image Processing for Robot Vision
http://www.informatik.uni-stuttgart.de/ipvr/bv/improv
ftp://ftp.informatik.uni-stuttgart.de/pub/improv




