[Digikam-devel] Digikam-devel Digest, Vol 58, Issue 95

[Kåre Särs]

On Tuesday 23 March 2010, Aditya Bhatt wrote:
> @K?re
> 
> Just a comment about the image size tradeoff.
> 
> > I have just updated the automatic image selection on previews in Skanlite
> > (actually libksane). What I did was to first do a rough auto selection on
> > a resized image ~100 * 150 pixels and then refine the selections on the
> > full- sized preview. It improved the speed dramatically and did not
> > decrease the accuracy. (actually it removed a lot of false positives)
> > 
> > Just an idea if you needed more :)
> 
> Hi, what are you detecting in the image? What are the selections that you
> talk of?
> ( I can't checkout code right now, since the firewall in my college is
> something of a joke ATM, it blocks svn://)
> I'm not entirely sure what you mean, but thanks anyway :)

I have a really simple problem compared to recognizing faces :) I try to 
detect the square areas around pictures in the preview on a flatbed image 
scanner.

> 
> Actually what I meant was that when I run multiple cascades (5 or 6) , the
> process is very slow. It would be faster if the image is smaller, but
> there's of course a limit on how small an image I can make. The sad part
> is, I can't resize it to 100*150 pixels and expect good detection
> accuracy, because there are supposed to be a lot of faces in a photo and
> such a tiny image size will cause a phenomenal loss of information :)
> 

Yep, as you and Michael noted 100*150 pixels is not enough for a group photo. 
The situation is a bit different for the scanner. There you probably do not 
want to have more than ~5 different selection and there a small resolution 
image is enough. 

The main idea could still be valid. The idea being to have a "two phase 
detection". The first phase would does a very quick inaccurate search (false 
positives?) and the second would only investigate the areas found in the first 
phase.


> If you can help here, it'd be great :)

I'm afraid I would not be of much help ;)


Kåre