Hi there,
I'm a PhD student working in the field of computer vision and recently used Perl for doing my research and am asking for an appropriate name for my Perl module(s). I implemented a geometrical toolbox called SUGR[1] for constructing and testing points, lines, planes and transformations in 2D and 3D. The toolbox is based on projective geometry and a simple but effective uncertainty model, though one can use the tool without statistics, too. See BACKGROUND section in this post for more details. So, the question of my post is: what name should be used for this Perl module? Currently, SUGR is not categorized to any toplevel name. IMHO there are several possibilities, I could categorize it to some computational geometry related field (Math::Geometry::SUGR) or some statistical field (Statistics::SUGR). Since I cannot decide for one option or the other, I'd like to use AI::SUGR since this toolbox can be used for reasoning with uncertain geometric objects as you could do reasoning with logical statements (plane $A is orthogonal to line $L, which in turn contains point $X, without "choosing the epsilon", see BACKGROUND section). So, what's the opinion of the community? There is already a preview version of my library, it also includes a special shell (based on the pdlshell) and a simple markup language for offline computations. This is available from http://www.ipb.uni-bonn.de/SUGR.html -Stephan Footnotes: [1] SUGR is an acronym for "Statistically Uncertain Geometric Reasoning" ############ # Details: # ############ BACKGROUND ---------- The (scientific) contribution of this work is the fact that the geometrical toolbox uses simple statistical description (second moments). This is helpful if you are not 100% sure about the exact values of your points, lines and planes. For example if you measure the length of your notebook with a simple ruler, you will never know the length in - say - Angstrom (=1 hundred-millionth of a centimeter), so you will always have some uncertainty about the correct length. Using simple statistics has several advantages: (i) you can propagate the uncertainty to new geometrical objects. (ii) you get rid of the "choosing-the-epsilon-problem"[2] when testing geometrical relations such as "Does point X lie on the 3D-line L?". (iii) you can do a weighted least-squares estimation to do _any_ kind of constructions involving points, lines and planes in 2D and 3D. Footnotes: [2] The "Choosing-the-epsilon-problem" appears for example when you want to test whether a number is zero. It is usually not really zero, but close to it - so you choose a special small number called epsilon and test if your number is smaller than epsilon. INTERFACE --------- # create a new 3D point, with an homogeneous 4-vector and # a homogeneous 4x4 covariance matrix $X = new SUGR::Point([0,1,1,1], [[0.01,0 ,0 ,0], [0 ,0.01,0 ,0], [0 ,0 ,0.01,0], [0 ,0 ,0 ,0]]); # create two other points with # the same covariance matrix as the first point $Y = new SUGR::Point([0,2,3,1],$X->cov); $Z = new SUGR::Point([2,2,0,1],$X->cov); # construct a new 3D line using $X and $Y, # including error propagation $L = new SUGR::Line($X,$Y); # and a plane with the third point $Z $A = new SUGR::Plane($L,$Z); # create a projective camera $P with given # projection center $X0 (as a SUGR point!), # rotation matrix $R and focal length $c with its # variance $c_var $X0 = new SUGR::Point([10,10,10,1],$X->cov); $R = new PDL::Matrix([[0,1,0],[1,0,0],[0,0,1]]); $P = sugr_ProjectiveCamera({ "X0" => $X0, "R" => $R, "c" => 1, "c_var" => 0.001, }); # construct a new 2D point as an image of $X with # the projective camera $P $x = new SUGR::Point2d($X,$P); # test if two points $X and $Y are identical $X == $Y # test if a line $L is parallel to a plane $A in 3D Parallel($A,$L) -- Stephan Heuel fon: +49 228 73 2711 Institute for Photogrammetry fax: +49 228 73 2712 University of Bonn, Nussallee 15 mailto:[EMAIL PROTECTED] 53115 Bonn - GERMANY http://www.ipb.uni-bonn.de/~steve
