Vision-List-Request@ADS.COM (Vision-List moderator Phil Kahn) (01/25/91)
VISION-LIST Digest Thu Jan 24 09:47:29 PDT 91 Volume 10 : Issue 4 - 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: Inquiry into VME based digitizers Tracking stereo Request for references Item for Distribution ---------------------------------------------------------------------- Date: Tue, 22 Jan 91 19:15:01 PST From: grendel@opos.arc.nasa.gov (That monstrous man-eating descendant of Cain) Subject: Inquiry into VME based digitizers I am in the market for a new image digitizer for a VME based sun4/260 workstation. I would appreciate it if you have a VME based digitizer to email me an outline of it's capability along with any subjective qualifiers you may wish to add. I will summarize and post the responses to the VL. Capabilities I am *really* interested in: 1) Real time acquisition... up to onboard memory capacity. 2) Multichannel (minimum of 3). 3) Standard video formats RS-170, NTSC ... plus 4) Programmable video formats: Noninterlaced, 1024x1024, etc. 5) In regard to item 4) what is the maximum pixel rate? 6) 'C' callable subroutine library. 7) Minimal UN*X kernel hacking. 8) What, if any, onboard image processing. Ray Suorsa | Technological progress has merely grendel@opos.arc.nasa.gov | provided us with more efficient means NASA/Ames (415) 604-6334 | for going backwards. -- Aldous Huxley ------------------------------ Date: Wed, 23 Jan 91 12:15:40 MET DST From: bellutta@irst.it (Paolo Bellutta) Organization: I.R.S.T. 38050 POVO (TRENTO) ITALY Subject: tracking stereo Has anybody references for a stereo matching algorithm which uses a single camera sliding from one position to the other. The correspondence problem would be easied by tracking the principal edges while the camera is sliding, then depth estimation would be done using the two most distant views. I'm quite sure that this has been done before. Please e-mail. I'll summarize to the Vision List. Paolo Bellutta I.R.S.T. vox: +39 461 814417 loc. Pante' di Povo fax: +39 461 810851 38050 POVO (TN) e-mail: bellutta@irst.uucp ITALY bellutta%irst@uunet.uu.net ------------------------------ Date: 23 Jan 91 23:19:29 GMT From: boris@ogicse.cse.ogi.edu (Borislav Agapiev) Subject: Request for references Keywords: Computer Vision References Organization: Oregon Graduate Institute (formerly OGC), Beaverton, OR Hello, I'm not sure this is the appropriate group for this kind of request so if it isn't I apologize. A friend of mine, who is in Yugoslavia needs some references and it is practically impossible for him to obtain them. I couldn't find them in our library so I'm trying to get them somehow for him. Here is the list: S. A. Dudani, User's Manual and Tables of Moment Invariants for an On-Line Automatic Aircraft Identification System, Commun. Contr. System Lab., Ohio State University, Columbus, OH, Tech. Note 15, Jan. 1974. B. Schachter, A nonlinear mapping algorithm for large data bases, Computer Graphics Image Processing, vol. 7, pp. 271-278, 1978. G. H. Ball, Data analysis in the social sciences - What about the details?, in Proceedings Fall Joint Computer Conference, 1965, pp. 553-554. W. H. Highleyman, Data for character recognition studies, IEEE Trans. Electronic Computers (Correspondence), vol. EC-12, pp. 135-136, April 1963. The first one is the most important. If anybody has these or knows how to get them I would appreciate very much to let me know. Ideally, the best thing would be if somebody has them and simply sends a copy to me. Of course, I'll pay for any expenses the sender might incur. I'm using the world distribution, in case somebody in Europe has the references, so he can send them directly to my friend (I would provide his address). Thanks, Borislav Agapiev boris@cse.ogi.edu Dept. of Computer Science 19600 NW Von Neumann Dr. Beaverton, OR 97006 USA ------------------------------ Date: Wed, 23 Jan 91 15:01:36 GMT From: B M Smith <bms@dcs.leeds.ac.uk> Subject: Preliminary Call for Participation: AISB91 PRELIMINARY CALL FOR PARTICIPATION ================================== AISB91 University of Leeds 16-19 April 1991 Interested to know what is happening at the forefront of current AI research? Tired of going to AI conferences where you hear nothing but talk about applications? Bored at big AI conferences where there are so many parallel sessions that you don't know where to go? Saturated with small workshops that focus only on one narrow topic in AI? ==> the 1991 AISB conference may be just the thing for you ! AISB91 is organized by the Society for the Study of Artificial Intelligence and Simulation of Behaviour. It is not only the oldest regular conference in Europe on AI - which spawned the ECAI conferences in 1982 - but it is also the conference that has a tradition of focusing on research as opposed to applications. The 1991 edition of the conference is no different in this respect. The conference has a single session and covers the full spectrum of AI work, from robotics to knowledge systems. It is designed for researchers active in AI who want to follow the complete field. Papers were selected that are representative for ongoing research, particularly for research topics that promise new exciting avenues into a deeper understanding of intelligence. There will be a tutorial programme on Tuesday 16 April, followed by the technical programme from Wednesday 17 to Friday 19 April. The conference will be held at Bodington Hall, University of Leeds, a large student residence and conference centre. Bodington Hall is 4 miles from the centre of Leeds and set in 14 acres of private grounds. Leeds/Bradford airport is 6 miles away, with frequent flights from London Heathrow, Amsterdam and Paris. Leeds itself is easily accessible by rail (2 and a half hours from London) and the motorway network. The Yorkshire Dales National Park is close by, and the historic city of York is only 30 minutes away by rail. TECHNICAL PROGRAMME Wednesday 17 - Friday 19 April 1991 ======================================================== The technical programme sessions are organized around problem areas, not around approaches. This means sessions show how different schools of AI - knowledge-based approaches, logic based approaches, and neural networks - address the fundamental problems of AI. The technical programme lasts 2 and a half days. Each day has a morning session focusing on a particular area of AI. The first day this area is distributed AI, the second day new modes of reasoning, and the third day theorem proving and machine learning. The afternoon is devoted to research topics which are at the forefront of current research. On the first afternoon this topic is emergent functionality and autonomous agents. It presents the new stream of ideas for building autonomous agents featuring concepts like situatedness, physical symbol grounding, reactive systems, and emergence. On the second day the topic is knowledge level expert systems research. It reflects the paradigm shift currently experienced in knowledge based systems away from the symbol level and towards the knowledge level, both for design and knowledge acquisition. Each session has first a series of accepted papers, then two papers which treat the main theme from a principled point of view, and finally a panel. In addition the conference features three exciting invited speakers: Andy Clark who talks about the philosophical foundations of AI, Rolf Pfeifer who reflects on AI and emotion, and Tony Cohn who looks at the formal modeling of common sense. The conference is closed by the Programme Chairman, Luc Steels, who speculates on the role of consciousness in Artificial Intelligence. Here is a more detailed description of the various sessions and the papers contained in them: Distributed Intelligent Agents ============================== Research in distributed AI is concerned with the problem of how multiple agents and societies of agents can be organized to co-operate and collectively solve a problem. The first paper by Chakravarty (MIT) focuses on the problem of evolving agents in the context of Minsky's society of mind theory. It addresses the question how new agents can be formed by transforming existing ones and illustrates the theory with an example from game playing. Smieja (GMD, Germany) focuses on the problem of organizing networks of agents which consist internally of neural networks. Smieja builds upon the seminal work of Selfridge in the late fifties on the Pandemonium system. Bond (University of California) addresses the problem of regulating co-operation between agents. He seeks inspiration in sociological theory and proposes a framework based on negotiation. Finally Mamede and Martins (Technical University of Lisbon) address the problem of resource-bounded reasoning within the context of logical inference. Situatedness and emergence in autonomous agents =============================================== Research on robots and autonomous agents used to be focused strongly on low level mechanisms. As such there were few connections with the core problems of AI. Recently, there has been a shift of emphasis towards the construction of complete agents. This has lead to a review of some traditional concepts, such as the hierarchical decomposition of an agent into a perception module, a decision module and an action module and it has returned robotics research to the front of the AI stage. This session testifies to the renewed interest in the area. It starts with a paper by Bersini (Free University of Brussels) which is strongly within the new perspective of emphasizing situatedness and non-symbolic relations between perception and action. It discusses the trade-offs between reactive systems and goal-oriented systems. Seel (STC Technology, Harlow, UK) provides some of the formal foundations for understanding and building reactive systems. Jackson and Sharkey (University of Exeter) address the problem of symbol grounding: how signals can be related to concepts. They use a connectionist mechanism to relate spatial descriptions with results from perception. Cliff (University of Sussex) discusses an experiment in computational neuroethology. The next paper is from the Edinburgh Really Useful Robot project which has built up a strong tradition in building autonomous mobile robots. The paper will be given by Hallam (University of Edinburgh) and discusses an experiment in real-time control using toy cars. The final paper is by Kaelbling (Teleos Research, Palo Alto, California) who elaborates her proposals for principled programming of autonomous agents based on logical specifications. The panel which ends the session tries to put the current work on autonomous agents into the broader perspective of AI. The panel includes Smithers (University of Edinburgh), Kaelbling, Connah (Philips Research, UK), and Agre (University of Sussex). Following this session, on Wednesday evening, the conference dinner will be held at the National Museum of Photography, film and Television at Bradford. The evening will include a special showing in the IMAX auditorium, which has the largest cinema screen in Britain. New modes of reasoning ====================== Reasoning remains one of the core topics of AI. This session explores some of the current work to find new forms of reasoning. The first paper by Hendler and Dickens (University of Maryland) looks at the integration of neural networks and symbolic AI in the context of a concrete example involving an underwater robot. Euzenat and Maesano (CEDIAG/Bull, Louveciennes, France) address the problem of forgetting. Pfahringer (University of Vienna) builds further on research in constraint propagation in qualitative modelling. He proposes a mechanism to improve efficiency through domain variables. Ghassem-Sani and Steel (University of Essex) extend the arsenal of methods for non-recursive planning by introducing a method derived from mathematical induction. The knowledge level perspective =============================== Knowledge systems (also known as expert systems or knowledge-based systems) continue to be the most successful area of AI application. The conference does not focus on applications but on foundational principles for building knowledge systems. Recently there has been an important shift of emphasis from symbol level considerations (which focus on the formalism in which a system is implemented) to knowledge level considerations. The session highlights this shift in emphasis. The first paper by Pierret-Golbreich and Delouis (Universite Paris Sud) is related to work on the generic task architectures. It proposes a framework including support tools for performing analysis of the task structure of the knowledge system. Reichgelt and Shadbolt (University of Nottingham) apply the knowledge level perspective to the problem of knowledge acquisition. Wetter and Schmidt (IBM Germany) focus on the formalization of the KADS interpretation models which is one of the major frameworks for doing knowledge level design. Finally Lackinger and Haselbock (University of Vienna) focus on domain models in knowledge systems, particularly qualitative models for simulation and control of dynamic systems. Then there are two papers which directly address foundational issues. The first one by Van de Velde (VUB AI Lab, Brussels) clarifies the (difficult) concepts involved in knowledge level discussions of expert systems, particularly the principle of rationality. Schreiber, Akkermans and Wielinga (University of Amsterdam) critically examine the suitability of the knowledge level for expert system design. The panel involves Leitch (Heriot Watt University, Edinburgh), Wielinga, Van de Velde, Sticklen (Michigan State University), and Pfeifer (University of Zurich). Theorem proving and Machine learning =============== ================ The final set of papers focuses on recent work in theorem proving and in machine learning. The first paper by Giunchiglia (IRST Trento, Italy) and Walsh (University of Edinburgh) discusses how abstraction can be used in theorem proving and presents solid evidence to show that it is useful. Steel (University of Essex) proposes a new inference scheme for modal logic. Then there are two papers which represent current work on machine learning. The first one by Churchill and Young (University of Cambridge) reports on an experiment using SOAR concerned with modelling representations of device knowledge. The second paper by Elliott and Scott (University of Essex) compares instance-based and generalization-based learning procedures. TUTORIAL PROGRAMME - Tuesday 16 April 1991 ========================================== Six full-day tutorials will be offered on 16 April (subject to sufficient registrations for each.) Tutorial 1 Knowledge Base Coherence Checking Professor Jean-Pierre LAURENT University of Savoie FRANCE Like conventional software, AI Systems also need validation tools. Some of these tools must be specific, especially for validating Knowledge-Based Systems, and in particular for checking the coherence of a Knowledge Base (KB). In the introduction to this tutorial we will clarify the distinctions to be made between Validation, Verification, Static Analysis and Testing. We will present methods which try to check exhaustively for the coherence of a knowledge Base. Then we will present a pragmatic approach in which, instead of trying to assert the global coherence of a KB, it is proposed to check heuristically whether it contains incoherences. This approach is illustrated by the SACCO System, dealing with KBs which contain classes and objects, and furthermore rules with variables. Tutorial 2 Advanced Constraint Techniques Dr. Hans Werner Guesgen and Dr. Joachim Hertzberg German National Centre for Computer Science (GMD) Sankt Augustin, GERMANY This tutorial will present a coherent overview of the more recent concepts and approaches to constraint reasoning. It presents the concept of dynamic constraints as a formalism subsuming classical constraint satisfaction, constraint manipulation and relaxation, bearing a relationship to reflective systems; moreover, the tutorial presents approaches to parallel implementations of constraint satisfaction in general and dynamic constraints in particular. Tutorial 3 Functional Representation and Modeling Prof. Jon Sticklen and Dr. Dean Allemang* Michigan State University USA * Universitaet Zurich, SWITZERLAND A growing body of AI research centres on using the known functions of a device as indices to causal understanding of how the device "works". The results of functional representation and modeling have typically used this organization of causal understanding to produce tractable solutions to inherently complex modelling problems. In this tutorial, the fundamentals of functional representation and reasoning will be explained. Liberal use of examples throughout will illustrate the representational concepts underlying the functional approach. Contacts with other model based reasoning (MBR) techniques will be made whenever appropriate. Sufficient background will be covered to make this suitable for both those unacquainted with the MBR field, and for more experienced individuals who may be working now in MBR research. A general familiarity with AI is assumed. Participants should send in with their registration materials a one page description of a modeling problem which they face in their domain. Tutorial 4 Intelligent Pattern Recognition and Applications Prof. Patrick Wang M.I.T. Artificial Intelligence Laboratory and Northeastern University, Boston USA The core of pattern recognition, including "learning techniques" and "inference" plays an important and central role in AI. On the other hand, the methods in AI such as knowledge representation, semantic networks, and heuristic searching algorithms can also be applied to improve the pattern representation and matching techniques in many pattern recognition problems - leading to "smart" pattern recognition. Moreover, the recognition and understanding of sensory data like speech or images, which are major concerns in pattern recognition, have always been considered as important subfields of AI. This tutorial includes overviews of pattern recognition and articifical intelligence; including recent developments at MIT. The focus of the tutorial will be on the overlap and interplay between these fields. Tutorial 5 SILICON SOULS - Philosophical foundations of computing and AI Prof. Aaron Sloman University of Birmingham This will not be a technical tutorial. Rather the tutor will introduce a collection of philosophical questions about the nature of computation, the aims of AI, connectionist and non-connectionist approaches to AI, the relevance of computation to the study of mind, varieties of mechanism, consciousness, and the nature of emotions and other affective states. Considerable time will be provided for discussion by participants. Prof. Sloman has provided a list of pertinent questions, these will be sent to participants upon registration. Tutorial 6 Knowledge Acquisition Dr. Nigel Shadbolt Nottingham University Practical methods for acquiring knowledge from experts. The methods described have been shown to be effective through the pioneering research at Nottingham which compared common and less common methods for eliciting knowledge from experts. This tutorial is an updated version of the knowledge acquisition tutorial given at AISB'89 which was well-attended and enthusiastically received. ======================================================================== For further information on the tutorials, mail tutorials@hplb.hpl.hp.com or tutorials@hplb.lb.hp.co.uk or tutorials%hplb.uucp@ukc.ac.uk For a conference programme and registration form, or general information about the conference, mail aisb91@ai.leeds.ac.uk or write to: Barbara Smith AISB91 Local Organizer School of Computer Studies University of Leeds Leeds LS2 9JT U.K. ------------------------------ End of VISION-LIST digest 10.4 ************************