Vision-List-Request@ADS.COM (Vision-List moderator Phil Kahn) (04/05/91)
VISION-LIST Digest Thu Apr 04 09:55:55 PDT 91 Volume 10 : Issue 16 - Send submissions to Vision-List@ADS.COM - Send requests for list membership to Vision-List-Request@ADS.COM - Access Vision List Archives via anonymous ftp to ADS.COM Today's Topics: Re: GEOMED Re: Lip reading Re: stereo ground truth Graphics ==> Modelling <== Vision IEEE Workshop on Directions in Automated CAD-Based Vision Tech reports available: Constraint Networks in Vision ---------------------------------------------------------------------- From: brooks@ai.mit.edu (Rodney A. Brooks) Date: Tue, 2 Apr 91 00:40:10 EST Subject: Re: GEOMED That was the system implemented by Bruce Baumgart at the Stanford AI Lab for his PhD thesis. It uses winged edge polyhedra. It is described in great detail there in Stanford AI Memo 249, 1974. It was written in SAIL (an ALGOL like language). Most of the code is included in the thesis, although I recall that there was at least one serious typo. I remimplemented this in Lisp for the MIT AI Lab CADR's (the pre-commercial lisp machines) in about 1981. The bits are old and dusty on archive tapes somewhere and would take at least a week of somebody's work just to get them online. Unless you have a really, really, really good reason to want GEOMED I suggest you try a more recent modelling system---99% of all work (at least quantity-wise) on geometric modelling has been done since Bruce finished. ------------------------------ Date: Tue, 2 Apr 91 15:43:33 EST From: Sandy (Alex) Pentland <sandy@westminster.media.mit.edu> Subject: Lip reading Other machine vision references on lip reading are: Mase, K. and Pentland, A., (1990) Automatic Lipreading by Computer, {\sl Trans. Inst. Elec. Info. and Comm. Eng.}, vol. J73-D-II, No. 6, pp. 796-803, June 1990. Mase, K. and Pentland, A., (1989) Lip Reading: Automatic Visual Recognition of Spoken Words, {\sl Optical Society of America Topical Meeting on Machine Vision}, pp. 1565-1570, June 12-15, 1989, North Falmouth, MA. These are also available as technical reports from Vision And Modeling Group, c/o Prof. Alex Pentland E15-387, The Media Lab, M.I.T. 20 Ames St., Cambridge, MA 02139 E. Petajohn of Bell Labs has also published on lip reading in IEEE CVPR, in '85, ACM SigChi in '88. ------------------------------ Date: Tue, 2 Apr 91 18:20:18 EST From: mccool@dgp.toronto.edu (Michael McCool) Subject: Re: stereo ground truth In comp.ai.vision you write: >> Derek Tolley (tolley@eola.cs.ucf.edu) writes: >> We are interested in finding disparity maps with ground truth >> for some stereo pairs. We are trying to compare the accuracy of some >> stereo algorithms, and would like to have a verified disparity map to >> compare with the results. If anyone has the sites where these pairs >> and their disparity maps can be found or other information, please >> email me. Thank you. >GOOD LUCK!!!! If you find any, let me know, as I've been looking for >such data for YEARS! Why can't artificial images be used? Good reflectance models are now available in computer graphics. "Photorealistic" images, i.e. images with a large amount of complexity, can now be generated. It might be argued that such images would not be as good as a test as real images, yet random-dot stereograms are commonly used as test cases, and are blatantly artificial. Use of computer-generated imagery would allow precise control over image features and camera geometry, all without building photography jigs. AND the ground truth would be trivially available. Michael McCool Dynamic Graphics Project, University of Toronto ------------------------------ Date: Tue, 2 Apr 91 18:56:31 EST From: mccool@dgp.toronto.edu (Michael McCool) Subject: Graphics ==> Modelling <== Vision Keywords: Stereo, Model-based Reconstruction This posting has two parts: a plea for corrected images for a project I am contempating, and a flame-attractor general comment on computer graphics and vision. Plea for Data: =============| I am interested in reconstructing models for use in computer graphics from multiple views of a single object, given a simple hypothesised model. I need pictures of objects with a simple underlying shape, but complex surface features (for example, buildings). I don't particularly want to deal with having to correct for lens distortion, etc, which is why I am posting here. If anyone has any precorrected images of such objects from multiple angles, I would be very appreciative. Accurate positions of cameras with respect to one another and possibly the object would also be nice, etc. Does anyone have such data or know of a site from which it can be ftp'd? Graphics, Modelling, and Vision: ===============================| On a more general vein, I have just started my Ph.D. here in the Graphics Lab at the University of Toronto and am thrashing about, looking for a good topic. Something along the above line comes to mind: useful combinations of computer vision and graphics. One example: currently, graphics people spend a lot of time constructing complex models, which is just plain silly since computer vision, properly applied, could eliminate most of this work if a real object is being modelled. The questions are, how and when should these techniques be applied, what particular features of the computer-graphics modelling goal can be exploited to simplify the vision problem, and how do vision and graphics modelling techniques dovetail? For example, what should the roles of texture and displacement maps be? What should the role of human intervention be? Jumping completely off the wharf, the concept of a "cosmic derivative" has come up in talks I've had with Dimitri Terzopolous here at Toronto. Compare a rendered picture with a picture of the real object, and then optimize the parameters of the graphics model so that its rendered picture is maximally similar to the real picture. The graphics model should now be an accurate representation of the real object, at least given the information in the picture. The question is, how should this operation be performed? I can imagine this topic has probably been addressed before. Can anyone point me in the general direction of thought in this area? Can anyone relate why such techniques are not in more general use? I'm currently grinding my way through the computer vision literature, but some helpful hints and thought-provoking discussion is always welcome. Michael McCool@dgp.toronto.edu ------------------------------ Date: Tue, 2 Apr 91 11:39:51 EST From: Dr Kevin Bowyer <kwb@zoot.csee.usf.edu> Subject: IEEE Workshop on Directions in Automated CAD-Based Vision IEEE Workshop on Directions in Automated CAD-Based Vision Preliminary Program Sunday, June 2 8:00 - 10:00 Registration 9:15 - 9:30 Opening Remarks 9:30 - 10:50 Session Theme -- Use of Knowledge About Lighting Model based recognition of specular objects using sensor models Sato, Ikeuchi and Kanade Premio: an overview Camps, Shapiro and Haralick Automatic camera and light-source placement using CAD models Cowan and Bergman 10:50 - 11:05 Coffee Break 11:05 - 12:25 Session Theme -- Aspect Graph Variations On the characteristic views of quadric-surfaced solids Chen and Freeman Perspective projection aspect graphs of solids of revolution Eggert and Bowyer Viewpoint from occluding contour Seales and Dyer 12:25 - 1:30 Lunch 1:30 - 3:30 Discussion on the beach 3:30 - 5:15 Session Theme -- Geometry and Parallelism Computing stable poses of piecewise smooth objects Kriegman Implementation of geometric hashing on the connection machine Rigoutsos and Hummel From volumes to views: an approach to 3-D object recognition Dickinson, Pentland and Rosenfeld 5:15 - 5:30 Coffee Break 5:30 - 6:45 Panel Theme: Why Aspect Graphs Are Not (Yet) Practical Are Not: Olivier Faugeras, Joe Mundy, Narendra Ahuja Are Not Yet: Ramesh Jain, Alex Pentland, Chuck Dyer, Katsushi Ikeuchi 7:00 Reception Monday, June 3 9:30 - 10:50 Session Theme -- Feature Utility for Object Recognition CAD-based feature-utility measures for automatic vision programming Chen and Mulgaonkar 3D object recognition using invariant feature indexing of interpretation table Flynn and Jain Generating automatic recognition strategies using CAD models Arman and Aggarwal 10:50 - 11:05 Coffee Break 11:05 - 12:25 Session Theme -- Considerations in Building Systems On using CAD models to compute the pose of curved 3D objects Ponce, Hoogs and Kriegman CBCV: a CAD-based vision system Henderson, Evans, Grayston, Sanderson, Stoller and Weitz CAD based vision: using a vision cell demonstrator West, Fernando and Dew 12:25 - 1:30 Lunch 1:30 - 3:30 Discussion on the beach 3:30 - 5:15 Session Theme -- Toward ``Generic'' Representation and Recognition A robot vision system for recognition of generic shaped objects Vayda and Kak A generic bridge finder Vergnet, Saint-Marc and Jezouin Context-constrained matching of hierarchical CAD-based models for outdoor scenes Kadono, Asada and Shirai 5:15 - 5:30 Coffee Break 5:30 - 6:45 Panel Theme: State-of-the-Art in CAD-Based Vision Systems Session Organizer: Avi Kak Participants: to be announced 6:45 - 7:00 Closing Remarks Registration Information for CAD-Based Vision Workshop The workshop is on June 2-3, 1991, at the same location as CVPR '91. (The CVPR tutorials are on June 3. CVPR itself is on June 4-6.) The CVPR conference hotel is the MAUI MARIOTT on KAANAPALI RESORT. The CVPR conference rate is \$110 for single or double occupancy, with a \$25 charge for each additional person. The rate is good from MAY 30 until JUNE 10. Reservations should be made directly with the hotel at (808) 667-1200.} Please mention that you are attending the IEEE CVPR-91 conference. A 1-night deposit is required within 10 days of arrangement for guaranteed reservation. Registration fees: Advance Registration (received BEFORE May 7) IEEE Member ............................. $185 Non-member ............................. $230 IEEE member & full-time student ........ $100 Registration after May 7 IEEE Member ............................. $225 Non-member ............................. $280 IEEE member & full-time student ........ $125 The registration fee includes a copy of the proceedings, two lunches, four breaks and the Sunday reception. Registration fee should be paid by check or by money order made out to ``CAD-Based Vision Workshop.'' (Sorry-- we are not able to take credit cards. (clip \& mail) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \end{center} Name & mailing address: _________________________________________ _________________________________________ Mail to: _________________________________________ Steve Graham (CAD-Based Vision Workshop) _________________________________________ Electrical Engineering Department FT-10 _________________________________________ University of Washington Seattle, Washington 98195 _________________________________________ Phone: ___________________________ e-mail: ________________________________ IEEE membership number: ______________________ Registration fee: ___________ ------------------------------ Date: 4 Apr 91 04:37:23 GMT From: suter@latcs1.oz.au (David Suter) Organization: Comp Sci, La Trobe Uni, Australia Subject: Tech reports available: Constraint Networks in Vision Keywords: neural networks, finite elements, visual reconstruction Technical Reports Available Constraint Networks in Vision / Mixed Finite Elements The author has investigated methods of using (Augmented) Lagrangian approaches in various ways and in various problems in computer vision. These Augmented Lagrangian approaches generalise many of the (Penalty) based approaches used in most approaches. The major advances reulting from this approach are 1) The method can be used to reformulate visual reconstruction problems in a manner that uses mixed finite elements and thus allows the use of many computational schmes similar to those used in fluid mechanics. The mixed finite elements are simpler than those finite elements used previously. 2) The method lends itself to analog network solutions in a straightforward way by employing the approach of Platt. 3) The realization of these analog networks in terms of transconductance amplifiers (ala Mead) is simple - the networks produced by this method are far simpler than those proposed by others (Harris). Thus the work extends from problem reformulation right down to practical implementation using either more standard digital techniques (mixed finite element) or more exciting new analog networks (right down to the transistor level). Some aspects of the work are to shortly appear at IJCNN-91 (Seattle July) but more information can be obtained by requesting some or all of the following technichal reports: 1. D. Suter "Mixed Finite Element Formulation of Problems in Visual Reconstruction" La Trobe University Technical Report No. 2/90, Jan. 1990 This outlines the basic reformulation in Augmented Lagrangian terms and shows how this generalizes approaches such as Horns recent shape from shading approach. It also forshaddows the developments from here to analog networks that generalize that of Harris. 2. D. Suter "Visual Reconstruction and Edge Detection Using Coupled Deriviatives" La Trobe University Technical Report No. 7/90, June 1990 This contains examples obtained using Platts constraint networks. 3. D. Mansor and D. Suter "An Analogue Circuit for First Order Regularization" La Trobe University Technical Report No. 2/91, March 1991 This shows how to realize the networks using transconductance amps and how to simulate using SPICE (3b1). Copies of these reports can be obtained by email or by writing to the author: David Suter ISD: +61 3 479-2393 Department of Computer Science and Computer Engineering, STD: (03) 479-2393 La Trobe University, ACSnet: suter@latcs1.oz Bundoora, CSnet: suter@latcs1.oz Victoria, 3083, ARPA: suter%latcs1.oz@uunet.uu.net Australia UUCP: ...!uunet!munnari!latcs1.oz!suter TELEX: AA33143 FAX: 03 4785814 ------------------------------ End of VISION-LIST digest 10.16 ************************