Hi David!
> As far as I know, foreground and background are still objectively different from the computer's point of view and our point of view; they have different characteristics. A background tends to > be less detailed than a foreground; also, the definition of background is further muddied by the possibility of having multiple overlapping objects at different depths.
I see your point. The problem is that humans might have an intuition for background although it does not work for all pictures. If there were any objective difference between background and foreground then a foreground selection tool would exist with a hundert percent robustness and nobody would care about extracting manually or even semi-automatically because the entire problem would not exist. One could just use this objective criterion on any image without any user interaction and let the computer do the segmentation (even in batch mode). The reality is that there is no unique definition to distinguish foreground from background in every picture. You can prove this easily: Given a picture that consist of one white pixel and one black pixel. What is foreground now? Four possible answers: - "None" or "Both pixels" => No differentiation possible. Thanks, no further argumentation needed. - "White" => OK. You define all white pixels to be foreground. Given this definition of foreground, I won't have to show you millions of photographs where this definition does not work, will I? - "Black". See "white". - You give any other definition. This will not apply to the two-pixel checkerboard. => No unique definition for foreground or background exists that works for all all images. Sorry.
> You clearly understand the tool(and maybe the algorithym too) better than I. > However, my basic point is that 'what is not foreground' may mean something quite different from 'what is background'; the only case in which this will be false is when all objects are all at the same depth.
In this point you are right. As it is not clear what foreground and background is, it is well possible for a given picture, that a third, fourth, fifth class exists... SIOX is a heuristics and there are several assumptions behind it: 1.) The user wants to extract one object (one connected pixel area) or a set of objects (several connected pixel areas) of similar color structure [=foreground]. 2.) The foreground has an overall different color structure than the rest of the picture [=background]. 3.) The user provides the algorithm with information of the color structure of the background and gives a spatial hint where the foreground may reside. This is done by drawing the first lasso selection. Everything outside the lass is considered background. 4.) The user provides further information on the color structure of the foreground. This is done using the foreground brush marking. Then the SIOX algorithm classifies those pixels that are not background and not part of the foreground marking by comparing their "perceptual similarity" to these two classes. Further (foreground or background) markings can be added if SIOX' first guess wasn't satisfying. So given the heuristics defined by SIOX, background is the true opposite of foreground. If for some reason it might be hard to extract "background" with SIOX: Try to extract the "foreground" and invert the selection. However, because no unique definition of background or foreground exists, there will always be images where any automatic foreground extraction fails (even the one working in our brains). The good thing about SIOX is that it works better for more images than the other extraction tools that I know. Gerald -- Gerald Friedland Raum 164 Tel: ++49 (0)30/838-75134 Freie Universität Berlin Takustr. 9 http://www.gerald-friedland.org Institut für Informatik 14195 Berlin gfriedland@xxxxxxxxxxxx _______________________________________________ Gimp-developer mailing list Gimp-developer@xxxxxxxxxxxxxxxxxxxxxx https://lists.XCF.Berkeley.EDU/mailman/listinfo/gimp-developer
