Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Posting-Version: version B 2.10.2 9/18/84; site utastro.UUCP Path: utzoo!watmath!clyde!bonnie!akgua!whuxlm!whuxl!houxm!ihnp4!qantel!dual!mordor!ut-sally!utastro!nather From: nather@utastro.UUCP (Ed Nather) Newsgroups: net.ai Subject: Re: Computer Vision, Pattern Recognition Message-ID: <358@utastro.UUCP> Date: Mon, 15-Jul-85 11:18:47 EDT Article-I.D.: utastro.358 Posted: Mon Jul 15 11:18:47 1985 Date-Received: Wed, 17-Jul-85 21:02:41 EDT References: <10571@rochester.UUCP> Organization: U. Texas, Astronomy, Austin, TX Lines: 57 > Reconstruction vs Recognition Based systems: > > Many people (especially people at MIT) believe that a fundamental step > in computer vision is to reconstruct some set of intrinsic parameters > such as surface orientation, texture, illumination, reflectivity. I'm not sure where it fits into the theory, but we have operational an "image re-recognition" system that works fine for our (very restricted) astronomical image fields. We constuct (from the original image) a set of r-theta tables representing the distance and angle of each "nearby" star image to our target position, as well as the distance and angle of "neighbors" for every star image in the original field. The number of neighbors is an adjustable parameter, depending on the "richness" -- the density of star images -- in the field. When another image of this field is presented (at a later time, and offset in X and Y, usually) we can identify the target location in the (offset) field by comparing the r-theta values from the new image with the stored tables, by simple table look-up. Cross correlation is not needed. We can then locate the target position, and center it. I realize this is a very limited application -- it only works on images composed of point sources of light -- but the idea of transforming the original image into "symbolic" form for comparison and recognition may have some wider use. The trick would be to find a transformation that retains most of the information needed for recognition, and discards most of the rest. In this example, the chosen algoritm is very efficient. For a star field of average richness, only a few hundred bytes suffice to hold all of the transformed information. A 100 megabyte disk could hold all of the "electronic finding charts" ever used in astronomy on this planet. > Generalized Image Storage Format? > > I can tell every university stores images differently. As far as > other generalized images then every program stores them differently. > This I believe acts as a gigantic brake on vision research. Astronomers faced a similar problem, and seem to have solved it. We can trade images of star fields with other observatories if we just write them onto mag tape in FITS tape format -- a generalized bit-mapped image tranfer system. I can point you to a technical description of FITS if you're interested. > This seems enough to spark some discussion (though I've been wrong > before). Any more and people won't read it anyway. Probably true. I'm aware of three "automated telescope" projects in astronomy that required image recognition to work, and all were total failures. A little coaxing would bring out details of this past history, in hopes we won't be compelled to repeat it. -- Ed Nather Astronomy Dept, U of Texas @ Austin {allegra,ihnp4}!{noao,ut-sally}!utastro!nather nather%utastro.UTEXAS@ut-sally.ARPA