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VISION-LIST Digest Volume 10 Issue 21
VISION-LIST Digest Thu May 09 14:41:01 PDT 91 Volume 10 : Issue 21
- 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:
Range image archive status
Share a room during "Geometric Methods in Computer Vision of SPIE"
pgmtxtur - statistical approach to texture
Re: matching sets of points in 2 space
Hough transform code
Looking for implemented chainning algorithms in C
Edge tracer
literature search
Stereo vision and sensor fusion
Performance Evaluation (long)
IJCAI-91 Programme Schedule (long)
----------------------------------------------------------------------
Date: Sun, 5 May 91 12:51:52 EST
From: Patrick J. Flynn <flynn@shillelagh.cse.nd.edu>
Subject: Range image archive status
The range image archive on shillelagh.cse.nd.edu (129.74.9.7) will be
removed and anonymous ftp disabled on or before May 30, 1991. When I
get settled in at Washington State University, I'll re-install the
archive on a machine there. This might take a few months, so anyone
interested in retrieving the images before the end of the summer
should do it now. Please restrict anonymous ftp traffic to
non-business hours.
I have logged several hundred ftp sessions since I made the archive available
last year, and I hope people are finding the data useful. As always,
e-mailed questions and suggestions regarding the archive are welcome.
Patrick J. Flynn
Now: Dept. of Comp. Sci. & Engineering, Univ. of Notre Dame (flynn@cse.nd.edu)
Soon: School of EE & CS, Washington State University (flynn@eecs.wsu.edu)
------------------------------
Date: Tue, 7 May 91 17:02:04 EDT
From: gong@division.cs.columbia.edu (Yitao Gong)
Subject: share a room during "Geometric Methods in Computer Vision of SPIE"
Hi,
I am a PhD student of Computer Science of Columbia. I'd like to share a room
with someone during "Geometric Methods in Computer Vision of SPIE", July 21-26
in San Diego. If interested, send email to: gong@cs.columbia.edu
Yitao
------------------------------
Date: Mon, 29 Apr 91 20:35:11 CDT
From: jdm5548@diamond.tamu.edu (James Darrell McCauley)
Subject: pgmtxtur - statistical approach to texture
I've posted source to the USENET newsgroup alt.sources that calculates
textural features using the statistical approach. You must have
PBMPLUS to compile. If you don't get that newsgroup or don't have a
news feed and you would like to receive these, send me e-mail and
I'll send you a copy.
Thanks to those who tried to find texture images with ground truth.
I finally calculated everything by hand for a small image to do my
debugging.
James Darrell McCauley, Grad Res Asst, Spatial Analysis Lab
Dept of Ag Engr, Texas A&M Univ, College Station, TX 77843-2117, USA
(jdm5548@diamond.tamu.edu, jdm5548@tamagen.bitnet)
------------------------------
Date: 3 May 91 00:26:46 GMT
From: tmb@ai.mit.edu (Thomas M. Breuel)
Organization: MIT Artificial Intelligence Lab
Subject: Re: matching sets of points in 2 space
> Point Set Matching
> ------------------
> (Barrodale Computing Services Ltd, May 1991).
>
> Problem Definition:
> We are given two sets of points in the plane. These points could represent
> two `simplified' images or output from some sensors. The first set
> contains M points. The second set is similar to the first set, except
> that some of the points from the first set are missing and some new
> points, not in the first set, are present. The second set contains N
> points. The positions of the points in the second set are, within a
> given tolerance, the same as common points in the first set. However,
> within this tolerance fairly large local distortions can occur.
>
> The problem has three parts:
> 1. Find all the points in the first set which do not have a match in
> the second set.
> 2. Find all points in the second set which do not have a match in
> the first set.
> 3. For all points in the first set which have a common point in the
> second set find the correct match.
> Questions:
> We are interested in hearing from anyone who has worked on the above
> problem or has worked on related problems. We are also interested in
> looking at the possibility of using artificial intelligence
> techniques, like neural networks, for solving the problem.
[since this question seems to come up from time-to-time, I'm posting
this response]
The following papers will give you a good start at the literature
(Eric Grimson's book has an extensive bibliography of the pre-1990
work on the subject; you should look there for other references):
Alt H., Mehlhorn K., Wagener H., Welzl E., 1988, Congruence,
Similarity, and Symmetries of Geometric Objects., Discrete and
Computational Geometry.
Baird H. S., 1985, Model-Based Image Matching Using Location, MIT
Press, Cambridge, MA.
Breuel T. M., 1991, An Efficient Correspondence Based Algorithm for 2D
and 3D Model Based Recognition, In Proceedings IEEE Conf. on Computer
Vision and Pattern Recognition.
Cass T. A., 1990, Feature Matching for Object Localization in the
Presence of Uncertainty, In Proceedings of the International
Conference on Computer Vision, Osaka, Japan, IEEE, Washington, DC.
Grimson E., 1990, Object Recognition by Computer, MIT Press,
Cambridge, MA.
State-of-the-art algorithms running on a SparcStation can find optimal
solutions (either maximal size of match at given error or minimum
error at given size of match) to this kind of bounded error
recognition problem on the average in under a minute, for models
consisting of hundreds points of and images consisting of 1000-2000
unlabeled, oriented features.
------------------------------
Date: Sun, 5 May 91 14:21:30 EDT
From: Zhengbin Wang <math4811@nexus.yorku.ca>
Subject: Hough transform code
Does anyone has the Hough transform code for me to share?
Thanks in advance.
Richard
------------------------------
Date: Tue, 07 May 91 00:59:00 +0100
From: A.Etemadi@ee.surrey.ac.uk
Subject: Looking for implemented chainning algorithms in C
G'day,
I am looking for any C programs for chainning edge data. I
would be most grateful if anyone could send me either programs,
or a pointer as to where to get them. I would implement the
ones mentioned in Rosenfeld & Kak, 1982, Chapter 10.3 and
Ballard & Brown, 1982, Chapters 4 & 8, but I'm tired of
reinventing the wheel.
Thanks in advance
Dr. A. Etemadi, | Phone: (0483) 571-281 Ext. 2311
V.S.S.P. Group, | Fax : (0483) 300-803
Dept. of Electronic and Electrical Eng., | Email:
University of Surrey, | Janet: a.etemadi@ee.surrey.ac.uk
Guildford, | ata@c.mssl.ucl.ac.uk
Surrey GU2 5XH | SPAN : ata@mssl
United Kingdom | ata@msslc
------------------------------
Date: 1 May 91 19:57:01 GMT
From: bedros@agnes.cs.umn.edu
Organization: University of Minnesota, Minneapolis
Subject: Edge tracer
Keywords: edge detection, edge tracing
I am working on postprocessing a low bitrate coded image, thus
trying to enhance the edges in the image. I am looking for some
references on edge tracing for an edge detected image. Also, any code
would be greatly appreciated.
Thanks,
Saad J Bedros
please reply to bedros@ee.umn.edu
------------------------------
Date: Thu, 9 May 91 17:16:31 +0100
From: sjd@computing-maths.cardiff.ac.uk (Molly)
Organization: University of Wales College of Cardiff, Cardiff, WALES, UK.
Subject: literature search
I am looking for a referance entitled :
INTEGRATING VISION MODULES WITH COUPLED MRF'S by T.POGGIO .
TECHNICAL REPORT WORKING PAPER 285, ARTIFICIAL INTELLIGENCE
LABORATORY, MASSACHUSETTS INSTITUTE OF TECHNOLOGY, 1985.
Can anybody help me aquire this paper by sending me the address of the author
or literature in which i might find it.
My e-mail address is sjd@uk.ac.cf.cm
Thanks in advance.
------------------------------
Date: Tue, 30 Apr 91 06:03:39 PDT
From: 30-Apr-1991 1502 <pau@yippee.enet.dec.com>
Subject: Stereo vision and sensor fusion
Further to the request for information on stereo vision,but with a
slant towards sensor diversity and multi-sensor inputs,many knowledge
representation architectures and fusion algorithms are given in :
L.F.Pau,Sensor and data fusion,Academic Press,NY,1991
------------------------------
Date: Wed, 1 May 91 13:03:01 PDT
From: tapas@george.ee.washington.edu
Subject: Performance Evaluation (long)
I have appended all the responses I received for a request I had sent
out sometime back. The request was for papers on "Performance
Evaluation" in the context of image processing/vision algorithms.
Also, I have appended a list of papers we came across and the abstract
of our paper on performance evaluation of algorithms which can be
posed as performing a detection task.
Thanks to every one who responded and a million apologies for
the delay on my part in submitting the responses to the list.
Also, if you have come across anymore references in the meantime,
I will appreciate it if you could post them to the list.
Thanks in advance.
Tapas Kanungo
tapas@george.ee.washington.edu
Intelligent Systems Laboratory
Department of Electrical Engineering, FT-10
University of Washington
Seattle, WA 98195
================================
Note:
1) The paper by Raudys and Jain has many references which you might want
to look at.
2) The January issue of CVGIP:Image Understanding has few papers that
discuss the need for performance evaluation.
3) Our paper, Kanungo, Jasimha, Haralick, Palmer, talks about how to
characterize the performance of ANY system that does a detection task.
That is, input to the system is an image with or without a target and
the output of the system is just a YES or NO.
================================
ingemar@robata.nec.com
Tapas,
I recently submitted a paper entitled ``Optimum and Practical
Filters for edge Detection'' to IEEE PAMI. This paper derives
optimum filters and then presents a methodology for quantitatively comparing
practical filters with the optimum ones. I'd be happy to send you a copy
if you let me know your mailing address.
Ingemar J. Cox,NEC Research Institute, 4 Independence Way, Princeton,
NJ 08540
phone: 609 951 2722
fax: 609 951 2482
email: ingemar@research.nec.com (Inet)
uucp: princeton!nec!ingemar
Note email will change to ingemar@research.nj.nec.com soon
==================================
munnari!wacsvax.cs.uwa.oz.au!wang@uunet.UU.NET
In response to your request of Oct 11, I recommend P.K. Sahoo's paper
"A Survey of Thresholding Techniques", published in CVGIP 41, 1988, to
you. He used some measures for evaluating different thresholding
methods which you may be interested in, and have learned as well.
C.Y. Wang
===================
ace@ecn.purdue.edu
I saw your note on the net. We did some evalaution of edge operators.
You may want to look at:
1) Delp and Chu, "Detecting Edge Segments", IEEE Trans. Systems, Man,
and Cybernetics, Jan. 1985, pp. 144-152.
2)Eichel and Delp, "Quantitative Analysis of a Moment Based Edge
Operator", IEEE Trans. Systems, Man, and Cybernetics, Jan. 1990, pp.59-66.
We also did some evaluation in:
Tan, Gelfannd, and Delp, "A Comparative Cost Function Approach to Edge
Detection", IEEE Trans. Systems, Man, and Cybernetics, Dec. 1989, pp. 1337-1349
Prof. E.J. Delp, Purdue University, School of Electrical Engineering
===========================
pkahn@ads.com
Please take a look at a paper by Kahn, Kitchen, & Riseman, to appear in
this Nov's PAMI entitled "A Fast Line Finder for Vision-Guided Robot
Navigation." It discusses performance design for fast low-level vision
computations.
...note Kitchen and Malin's paper in the bibliobgraphy of this paper:
they do some very good performance assessments there.
Please note there is the issue of performance from the standpoint of
"how well does x perform at doing y?" and there is performance from
the standpoint of complexity of computation. Our paper primarily
addresses complexity of computation and executional performance.
You might also want to look at "The Complexity of Perceptual Search
Tasks," J.K. Tsotsos, IJCAI89, pp. 1571-1577.
regards,
phil...
===========================
vistnes@prl.dec.com
See
"Texture models and image measures for texture discrimination,"
IJCV 3(4), 1989, 311-336.
in which I discuss a method for evaluating texture discrimination algorithms.
Richard Vistnes
========================
laine@wave.cis.ufl.edu
I response to your request, may I suggest a paper on the
performance of stereo matching algorithms executing on the
gerneral class of SIMD machines.
Laine, Andrew F., "A Parrallel Algorithm for Incremental Stereo
Matching on SIMD Machines".
To appear IEEE Transactions on Robotics and Automation, 1990.
I will gladly provide preprints upon request.
The section on Performance Evaluation, contains a general formulation
and methodology for the performance evaluation of stereo matching
algorithms over the class of SIMD machines. I believe the methodogy
may be appealing to reformulate other computer vision alogithms as
well.
** An abridged version of this paper was
presented at the IEEE 10th International Conference on Pattern
Recognition, Atlantic City, NJ, June 16-21, 1990.
=================
Following papers are also into performance evaluationi:
@article{ DeF:eval,
author = "Deutsch, E. S. and J. R. Fram",
title = "A quantitative study of the
Orientational Bias of some Edge Detector Schemes",
journal = "IEEE Transactions on Computers",
month = "March",
year = 1978}
@article{FrD:human,
author = "Fram, J.R. and E.S. Deutsch",
title = "On the quantitative evaluation of edge detection schemes and
their comparisions with human performance",
journal = "IEEE Transaction on Computers",
volume = "C-24",
number = "6",
pages = "616-627"
year = 1975}
@article{AbP:eval,
author = "Abdou, I.E. and W. K. Pratt",
title = "Qualitative design and evaluation of enhancement/thresholding
edge detector",
journal = "Proc. IEEE",
volume = "67",
number = "5",
pages = "753-763",
year = 1979}
@article{PeM:eval,
author = "Peli, T. and D. Malah",
title = "A study of edge detection algorithms",
journal = "Computer Graphics and Image Processing",
volume = "20",
pages ="1-21",
year = 1982}
@article{KiR:eval,
author = "Kitchen, L. and A. Rosenfeld",
title = "Edge Evaluation using local edge coherence",
journal = "IEEE Transactions on Systems, Man and Cybernetics",
volume = "SMC-11",
number = "9",
pages = "597-605",
year = 1981}
@article{HaL:eval,
author = "Haralick, R.M. and J. S. J. Lee",
title = "Context dependent edge detection and evaluation",
journal = "Pattern Recognition",
volume = "23",
number = "1/2",
pages = "1-19",
year = 1990}
@article{Har:performance,
author = "Haralick, R.M.",
title = "Performance assessment of near perfect machines",
journal = "Machine Vision and Applications",
volume = "2",
number = "1",
pages = "1-16",
year = 1989}
@inproceedings{KJHP:performance,
author = "Kanungo, T. and M.Y. Jaisimha and R.M. Haralick and J. Palmer",
booktitle = "Proc. SPIE vol. 1385 Optics, Illumination, and Image Sensing
for Machine Vision V",
pages = "104-112",
month = "November",
year = 1990}
@article{HNR:hough,
author = "Hunt, D.J. and L.W. Nolte and A.R. Reibman and W.H. Ruedger",
title = "Hough Transform and Signal Detection Theory Performance for Images
with Additive Noise",
journal = cvgip,
volume = 52,
pages = "386-401",
year = 1990}
@article{RJ:smallsample,
author = " Raudys, J.S. and A.K. Jain",
title = "Small Sample Size Effects in Statistical Pattern Recognition:
Recommendations for Practitioners",
journal = pami,
volume = 13,
number = 3,
pages = "252-263",
year = 1990}
===================
Abstract of our paper follows:
\title{An Experimental Methodology for Performance Characterization of a Line
Detection Algorithm }
%
\author{\dag T. Kanungo, \dag M. Y. Jaisimha, \dag R. M. Haralick and
\ddag J. Palmer \\
\\
\dag Department of Electrical Engineering, FT-10 \\
\ddag Department of Psychology, NI-25 \\
University of Washington \\
Seattle WA 98195 \\
U.S.A.}
%
\date{ \today \\
\presenttime }
\maketitle
\begin{abstract}
With the burgeoning of computer vision algorithms, it has
become increasingly necessary to characterize and evaluate
their performance in a quantitative fashion. In the vast
majority of the existing literature, the assessment of an
algorithm is usually done by analyzing its results on just
two to three images. There is no mention of the population of
images the algorithm is supposed to work on. No effort is made
to address the concerns of whether or not the sample set used
is representative of the population. In addition, the analysis
of the accuracy and level of confidence in the results
is often not specified.
In this paper, we present a methodology for designing experiments
to characterize low level computer vision algorithms which
addresses these issues. The methodology is illustrated by
applying it to the specific case where a line detection algorithm
is used to detect the presence or absence of a vertical
edge in the presence of a masking grating. The line detection
algorithm consists of edge detection using the second directional
derivative edge detector followed by a mapping to Hough space.
The performance of the algorithm is studied with respect to
the edge contrast, the image noise, orientation, and
phase. The eventual objective of the experiment is to study
the orientation sensitivity of the line detection algorithm.
A set of experiments were performed to obtain the operating curves
relating the probability of misdetection and the probability of
false alarm of the algorithm. The contrast threshold which is the
measure of the sensitivity a representative set of the control
parameter values. Thresholds representing meaningful measures
of the performance levels are then extracted from the operating
curves. These thresholds are statistically consolidated to get a
combined performance versus grating orientation curve, and a
measure of the overall performance level.
The line detection algorithm is thus characterized by the
operating curves, the combined performance curve and
the overall performance level. The results also show
that the performance of the line detection algorithm is not
affected by the orientation of the masking grating.
\end{abstract}
------------------------------
Date: Thu, 9 May 1991 10:44:51 -0400
From: Kimberlee Pietrzak-Smith <kim@cs.toronto.edu>
Subject: IJCAI-91 Programme Schedule
***INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 1991***
IJCAI-91 Programme Schedule
Monday, August 26, 1991
9-10am: Invited Speaker 1 - Ross Quinlan
10-10:30am: Coffee
10:30-12:30pm:
ML: Explanation Based Learning
Christer Samuelsson
Quantitative Evaluation of Explanation-Based Learning as an Optimization Tool
for a Large-Scale Natural Language System
Prasad Tadepalli
A Formalization of Explanation-Based Macro-Operator Learning
Masayuki Yamamura
An Augmented EBL and its Application to Utility Problem
Jungsoon Yoo
Concept Formation over Explanations and Problem-Solving Experience
NL: NL Processing
Dan Moldovan
High Performance Natural Language Processing on Semantic Network Array
Processor
Hiroaki Kitano
Massively Parallel Memory-Based Parsing
Esther Konig
Using Parallel Processing for Semantic Analysis
Karl Gregor Erbach
An environment for experimentation with parsing strategies
KR: Nonmonotonic Reasoning - Modal Logics
Vladimir Lifschitz
Nonmonotonic Databases and Epistemic Queries: Preliminary Report
Nicholas Asher
Commonsense Entailment: A Modal Theory of Nonmonotonic Reasoning
Mirek Truszczynski
Modal Interpretations of Default Logic
Ilkka Niemela
Constructive Tightly Grounded Autoepistemic Reasoning
AR: Theorem Proving I
Michael Fisher
Yet Another Resolution Method for Temporal Logic
Thomas Guckenbiehl
Formalizing and Using Persistency
Fausto Giunchiglia
Reflective reasoning with and between a declarative metatheory and the
implementation code
Nachum Dershowitz
Ordering-Based Strategies for Horn Clauses
Arch: Knowledge Base Management
G. Ravi Prakash
A Methodology for Systematic Verification of OPS5-based AI Applications
Loren Terveen
Intelligent Assistance through Collaborative Manipulation
Keith Decker
Effects of Parallelism on Blackboard System Scheduling
Rick Evertsz
The Automated Analysis of Rule-based Systems, Based on their Procedural
Semantics
12:30-2pm: Lunch
2-3:30pm:
Panel 1: AI in Telecommunications
ML: Classifiers/Genetic Algorithms
Wray Buntine
Classifiers: A Theoretical and Empirical Study
James Kelly
A Hybrid Genetic Algorithm for Classification
Kenneth A. De Jong
Learning Concept Classification Rules Using Genetic Algorithms
KR: Belief
Sukhamay Kundu
A New Logic of Beliefs: Monotonic Beliefs and Nonmonotonic Beliefs - Part 1
Gerhard Lakemeyer
A Model of Decidable Introspective Reasoning with Qualitifying-In
Anand S. Rao
Asymmetry Thesis and Side-effect Problems in Linear Time and Branching Time
Intention Logics
LP: Logic Programming I
Sieger van Denneheuvel
Weak equivalence for constraint sets
Chilukuri K. Mohan
Fitting Semantics for Conditional Term Rewriting
Luis Moniz Pereira
A Derivation Procedure for Extended Stable Models (Draft)
Phil: Philosophical Foundations I
Francis Jeffry Pelletier
The Philosophy of Automated Theorem Proving
Raymond Earl Jennings
Generalised Inference and Inferential Modelling
John Slaney
The Implications of Paraconsistency
3:30-4pm: Coffee
4-5:30pm:
AI On Line
ML: Inductive Learning I
Sholom M. Weiss
Reduced Complexity Rule Induction
Alen Varsek
Qualitative Model Evolution
Celine Rouveirol
Semantic Model for Induction of First Order Theories
AR: Search I
G.M.A. Provan
An Expected-Cost Analysis of Backtracking and Non-Backtracking Algorithms
Amitava Bagchi
Admissible Search Methods for Minimum Penalty Sequencing of Jobs with Setup
Times on One and Two Machines
Anna Bramanti-Gregor
Learning Admissible Heuristics while Solving Problems
LP: Logic Programming II
Mike Brayshaw
An Architecture for Visualising the Execution of Parallel Logic Programs
Kang Zhang
A Non-shared Binding Scheme for Parallel Prolog Implementation
KR: Reasoning with Inconsistency
Mamede Lima Marques
Contextual Negations and Reasoning with Contradictions
Gerd Wagner
Ex contradictione nihil sequitir
Rob: Architectures
Luc Steels
Emergent Frame Recognition And Its Use In Artificial Creatures
R. Peter Bonasso
Integrating Reaction Plans and Layered Competences through Synchronous Control
7:30pm: Computers & Thought Award: Martha Pollack and Rodney Brooks
Announcement of IJCAI Best Paper Award
Tuesday, August 27, 1991
9-10am: Invited Speaker 2- Shigeru Sato
10-10:30am: Coffee
10:30-12:30pm:
ML: Inductive Learning II
Robin Hanson
Bayesian Classification with Correlation and Inheritance
Der-Shung Yang
A Scheme for Feature Construction and a Comparison of Empirical Methods
Steven Salzberg
Learning with a Helpful Teacher
Stefan Wrobel
Towards a Model of Grounded Concept Formation
AR: Planning I
Stuart J. Russell
Composing Real-Time Systems
Eric Biefeld
Bottleneck Identification Using Process Chronologies
Jeffrey S. Rosenschein
Incomplete Information and Deception in Multi-Agent Negotiation
Marta Franova
Solving "How to Clear a Block" with Constructive Matching Methodology
NL: Pragmatics
Peter van Beck
Resolving Plan Ambiguity for Cooperative Response Generation
Yorick Wilks
Your metaphor or mine: Belief ascription and metaphor interpretation
Philip R. Cohen
Confirmations and Joint Action
QR: Diagnosis
Philippe Dague
When Oscillators Stop Oscillating
Gerhard Friedrich
Diagnosing Temporal Misbehavior
Franz Lackinger
Integrating Model-Based Monitoring and Diagnosis of Complex Dynamic Systems
David Poole
Representing diagnostic knowledge for probabilistic Horn abduction
Vis: Object Recognition
Yerucham Shapira
A Pictorial Approach to Object Classification
Thomas M. Strat
Natural Object Recognition: A Theoretical Framework and Its Implementation
John R. Kender
On Seeing Spaghetti: A Novel Self-Adjusting Seven Parameter Hough Space for
Analyzing Flexible Extruded Objects
Tomaso Poggio
HyperBF Networks for real object recognition
12:30-2pm: Lunch
2-3:30pm:
Panel 2: AI and Design
ML: Inductive Logic Programming
J.R. Quinlan
Determinate Literals as an Aid in Inductive Logic Programming
Charles X. Ling
Inductive Learning from Good Examples
Marc Kirschenbaum
Refinement Strategies for Inductive Learning of Simple Prolog Programs
KR: Nonmonotonic Reasoning - Conditional Logics
Hirofumi Katsuno
A Unified View of Consequence Relation, Belief Revision and Conditional Logic
Craig Boutilier
Inaccessible Worlds and Irrelevance: Preliminary Report
Didier Dubois
Possibilistic logic, preference models, non-monotonicity and related issues
AR: Search II
Hermann Kaindl
Using Aspiration Windows for Minimax Algorithms
Stephen V. Chenoweth
High Performance A* Search Using Rapidly Growing Heuristics
Toru Ishida
Moving Target Search
CM: Cognitive Modelling 1
Jacobijn Sandberg
How situated is cognition?
Katia P. Sycara
Index Transformation Techniques for Facilitating Creative Use of Multiple Cases
Gregg Collins
Plan debugging in an Intentional System
3:30-4pm: Coffee
4-5:30pm:
AI On Line
ML: Concept Formation
Jason Catlett
Overpruning Large Decision Trees
Larry Watanabe
Learning Structural Decision Trees From Examples
David Heath
Learning Nested Concept Classes with Limited Storage
Sunil Thakar
Acquiring Knowledge by Efficient Query Learning
KR: Concept Languages
Franz Baader
Augmenting Concept Languages by Transitive Closure of Roles: An Alternative
to Terminological Cycles
Franz Baader
A Scheme for Integrating Concrete Domains into Concept Languages
Maurizio Lenzerini
Tractable Concept Languages
AR: Theorem Proving II
Toni Bollinger
A Model Elimination Calculus for Generalized Clauses
Inside the LILOG Inference Machine
Elmar Eder
Consolution and its Relation with Resolution
Manfred Kerber
How to Prove Higher Order Theorems in First Order Logic
Hitoshi Iba
Reasoning of Geometric Concepts based on Algebraic Constraint-directed Method
Phil: Philosophical Foundations II
David Israel
Actions and Movements
Selmer Bringsjord
In Defense of Hyper-Logicist AI
Francesco Bergadano
The Problem of Induction and Machine Learning
QR: Qualitative Modelling
Erling A. Woods
The Hybrid Phenomena Theory
Feng Zhao
Extracting and Representing Qualitative Behaviors of Complex Systems in Phase
Spaces
Toyoaki Nishida
A Geometric Approach to Total Envisioning
Wednesday, August 28, 1991
9-10am: Distinguished Scientist Award & Lecture: Marvin Minsky
10-10:30am: Coffee
10:30-12:30pm:
KR: Topics in Knowledge Representation
Periklis Belegrinos
A Model for Actions and Processes
Hans Juergen Ohlbach
Parameter Structures for Parametrized Modal Operators
Russell Greiner
Measuring and Improving the Effectiveness of Representations
Gadi Pinkas
Propositional Non-Monotonic Reasoning and Inconsistency in Symmetric Neural
Networks
AR: Planning II
Edwin P.D. Pednault
Generalizing Nonlinear Planning to Handle Complex Goals and Actions with
Context-Dependent Effects
Jens Christensen
A Formal Model for Classical Planning
Amy L. Lansky
Localized Search for Multiagent Planning
Steven Minton
Commitment Strategies in Planning: A Comparative Analysis
NL: NL Systems
Marie Meteer
POST: Using Probabilities in Language Processing
John A. Bateman
The rapid prototyping of natural language generation components: an
application of functional typology
Oliviero Stock
Natural Language and Exploration of an Information Space: the ALFRESCO
Interactive System
C. Rullent
Efficient Representation of Linguistic Knowledge for Continuous Speech
Understanding
QR: Qualitative Modelling, Temporal Reasoning
Ulf Soderman
Combining Qualitative and Quantitative Knowledge to Generate Models fo
Physical Systems
Judea Pearl
Directed Constraint Networks: A Relational Framework for Causal Modeling
Jan Top
Computational and Physical Causality
Antony Galton
Reified Temporal Theories And How To Unreify Them
Vis: Interpretation
Paul Cohen
Shading-Based Two-View Matching
Pascal Fua
Combining Stereo and Monocular Information: Computing Robust Dense Depth Maps
and Preserving Depth Discontinuities
R. Mike Cameron-Jones
Visual Interpretation of Lambertian Surface Deformation
Terry Regier
Line Labeling and Junction Labeling: A Coupled System for Image Interpretation
12:30-2pm: Lunch
2-5:30pm: Computer & Chess Afternoon
Panel and Chess Match
Thursday, August 29, 1991
9-10am: Invited Speaker 3 - Robert Kowalski
10-10:30am: Coffee
10:30-12:30pm:
ML: Inductive Learning III
Armand E. Prieditis
Machine Discovery of Effective Admissible Heuristics by Means-Ends Analysis
David Chapman
Learning from Delayed Reinforcement In a Complex Domain
Wayne Iba
Learning to Classify Observed Motor Behavior
Peter C-H. Cheng
Modelling Experiments in Scientific Discovery
AR: Reason Maintenance
Jean Christophe Madre
A Logically Complete Reasoning Maintenance System Based on a Logical
Constraint Solver
Jerome Euzenat
Contexts for Nonmonotonic RMSes
Wang Xianchang
On Semantics of TMS
Ulrich Junker
Prioritized Defaults: Implementation by TMS and Application to Diagnosis
NL: Representation and Semantics
Padraig Cunningham
Organizational Issues Arising from the Integration of Lexicon and Concept
Network in a Text Understanding System
Mark Johnson
Logic and Feature Structures
Leonardo Lesmo
Representation and Interpretation of Definite Noun Phrases
Stephen Busemann
Using Pattern-Action Rules for the Generation of GPSG Structures From
MT-Oriented Semantics
LP: Logic Programming III
Kienchung Kuo
Programming in Autoepistemic Logic
L. Thorne McCarty
Indefinite Reasoning with Definite Rules
Karen L. Kwast
The Incomplete Database
Mark Wallace
Compiling Integrity Checking into Update Procedures
Dinesh Gadwal
UMRAO: A Chess Endgame Tutor
Luigia Carlucci-Aiello
Reasoning about Student Knowledge and Reasoning
Tak-Wai Chan
Integration-Kid: A Learning Companion System
William R. Murray
An Endorsement-based Approach to Student Modeling for Planner-controlled Tutors
12:30-2pm: Lunch
2-3:30pm:
Panel 3: Multiple Approaches tp Mulitple Agent Problem Solving
ML: Case Based Learning
Diane J. Cook
The Base Selection Task in Analogical Planning
Scott Fertig
FGP: A Software Architecture for Acquiring Knowledge from Cases
James P. Callan
Adaptive Case-Based Reasoning
KR: Nonmonotonic Reasoning - Circumscription
Nicolas Helft
Query Answering in Circumscription
Yves Moinard
Circumscription and Definability
Zhaogang Qian
Circumscribing Defaults
AR: Theorem Proving III
Robert Demolombe
An Inference Rule for Hypothesis Generation
Katsumi Inoue
Consequence-Finding Based on Ordered Linear Resolution
Christoph Lingenfelder
Proof Transformation with Built-in Equality Predicate
Arch: Distributed AI I
Sarit Kraus
Negotiations over Time in A Multi Agent Environment: Preliminary Report
Piotr J. Gmytrasiewicz
A Decision-Theoretic Approach to Coordination Multiagent Interactions
Munindar P. Singh
Towards a Formal Theory of Communication for Multiagent Systems
3:30-4pm: Coffee
4-5:30pm:
AI On Line
ML: Classification & Generalization
Floriana Esposito
Flexible Matching for Noisy Structural Descriptions
Haym Hirsch
Theoretical Underpinnings of Version Spaces
Jacques Nicolas
Empirical Bias for Version Space
KR: Concept Languages, Inheritance Reasoning
Klaus Schild
A Correspondence Theory for Terminological Logics: Preliminary Report
John Yen
Generalizing Term Subsumption Languages to Fuzzy Logic
David S. Touretzky
A Skeptic's Menagerie: Conflictors, Preemptors, Reinstators, and Zombies in
Nonmonotonic Inheritance
AR: Constraint Satisfaction
Rina Dechter
On the Feasibility of Distributed Constraint Satisfaction
Pascal van Hentenryck
Efficient Arc Consistency Algorithm for a Class of CSP Problems
Peter Cheeseman
Where the Really Hard Problems Are
QR: Reasoning under Uncertainty I
Yen-Teh Hsia
Characterizing Belief with Minimum Commitment
Rudolf Kruse
On a Tool for Reasoning with Mass Distribution
Henry E. Kyburg
Evidential Probability
Rob: Navigation
Stephen F. Peters
Planning Robot Control Parameter Values with Qualitative Reasoning
Patrick Stelmasyk
Mobile Robot Navigation by an Active Control of the Vision System
Matthew Barth
Determining Robot Egomotion from Motion Parallax Observed by an Active Camera
5:30pm: General Meeting
Friday, August 30, 1991
9-10am: Invited Speaker 4- Takeo Kanade
10-10:30am: Coffee
10:30-12:30pm:
AR: Planning III
Christer Backstrom
Parallel Non-Binary Planning in Polynomial Time
Tom Bylander
Complexity Results for Planning
Dekang Lin
A Message Passing Algorithm for Plan Recognition
Fahiem Bacchus
The Downward Refinement Property
NL: Parsing and Morphology
Tsunenori Mine
Coordinative Parallel Morphological and Syntactical Analysis Method in
Japanese
Liang-Jyh Wang
A Parsing Method for Identifying Words in Mandarin Chinese Sentences
Harald Trost
X2MORF: A Morphological Component Based on Augmented Two-Level Morphology
Venu Dasigi
Parsing = Parsimonious Covering (Abduction in Logical Form Generation)
Arch: Connectionist & Parallel Rule Systems
Tony Plate
Holographic Reduced Representations: Convolution Algebra for Compositional
Distributed Representations
Andrew Sohn
A Macro Actor/Token Implemetation of Production Systems on Data-Flow
Multiprocessor
Dan Moldovan
Performance Comparison of Models for Multiple Rule Firing
Ian Nevill Robinson
On Supporting Associative Access and Processing over Dynamic Knowledge Bases
Summary Session: IJCAI-91, Learning and Knowledge Acquisition
Summary Session: KR'91, International Conference on Principles of Knowledge
Representation and Reasoning
12:30-2pm: Lunch
2-3:30pm:
Panel 4: Massively Parallel Computing in Artificial Intelligence: Bridging
Gaps Between Hardware and Applications
ML: Knowledge Acquisition
Kathleen McKusick
Constraints on Tree Structure in Concept Formation
Brian R. Gaines
An Interactive Visual Language for Term Subsumption Languages
Matthias Gutknecht
Cooperative Hybrid Systems
CM: Cognitive Modelling 2
N. Hari Narayanan
Reasoning Visually about Spatial Interactions
Akira Shimaya
A Cognitive Model for Figure Segregation
W.K. Yeap
An MFIS for Computing a Raw Cognitive Map
Summary Session: IJCAI-91, Automated Reasoning
Summary Session: International Symposium on AI and Mathematics
3:30-4pm: Coffee
4-5:30pm:
ML: Connectionist Models
Warren R. Becraft
Integration of Neural Networks and Expert Systems for Process Fault Diagnosis
Chilukuri Krishna Mohan
Analyzing Images Containing Multiple Sparse Patterns with Neural Networks
Selwyn Piramuthu
The Utility of Feature Construction for Back-Propagation
Arch: Distributed AI II
Hideyuki Nakashima
Communication and Inference through Situations
David N. Kinny
Commitment and Effectiveness of Situated Agents
Takashi Nishiyama
Generating Integrated Interpretation of Partial Information Based on
Distributed Qualitative Reasoning
QR: Reasoning under Uncertainty II
S.K.M. Wong
Propagation of Preference Relations in Qualitative Inference Networks
Wilson Xun Wen
Parallel Distributed Belief Networks That Learn
Summary Session: IJCAI-91, Natural Language
Summary Session: International Conference on Automated Deduction
LEGEND:
AR: Automated Reasoning
Arch: Architectures & Languages
CM: Cognitive Modelling
KR: Knowledge Representation
LP: Logic Programming
ML: Machine Learning
NL: Natural Language
Phil: Philosophical Foundations
QR: Qualitative Reasoning
Rob: Robotics
Vis: Vision
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End of VISION-LIST digest 10.21
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