Hanson, A.R., and
Riseman, E.M.,
VISIONS: A computer System for Interpreting Scenes,
CVS78(303-333).
Multiple Resolutions.
System: VISIONS. The basic outline of their system.
For the full set of papers and a more complete description:
See also University of Massachusetts VISIONS System.
BibRef
7800
Hanson, A.R., and
Riseman, E.M.,
The VISIONS Image-Understanding System,
ACV88(I: 1-114).
BibRef
8800
And:
COINS-TR-86-62, December 1986.
The work on the VISIONS System at the Univ. of Massachusetts has
extended over a number of years. Throughout the effort the work has
addressed the mapping of image regions to objects names in the context
of outdoor scenes. This work combines bottom-up (data driven)
processing with top-down (knowledge driven) approaches to identify
many object regions. Initial object hypotheses are formed based on
the appearance (intensity, color, etc.) using a trapezoidal weighting
function (similar to that discussed in section 4) that gives a maximum
rating (1) when the value is within the good range and a minimum value
(0) when it is in the bad range. Between these ranges, it is a linear
combination. These threshold values are derived from the histograms
of the feature value for known regions in a training set (e.g. grass,
sky, etc.). The initial hypotheses are extended by finding other
regions in the current image that are similar to the image region that
best matches the given values from the model. This allows for shifts
in the value of properties of objects between images, with some
controls provided by the model. The final choice of the object
identification depends on domain knowledge such as constraints on
relationships of the different objects (grass is below sky).
BibRef
Draper, B.A.,
Hanson, A.R.,
Riseman, E.M.,
Knowledge-Directed Vision: Control, Learning, and Integration,
PIEEE(84), No. 11, November 1996, pp. 1625-1637.
9611
PS File.
BibRef
Draper, B.A., and
Hanson, A.R.,
An Example of Learning in Knowledge-directed Vision,
SCIA91(189-201).
PS File.
BibRef
9100
Draper, B.A.,
Collins, R.T.,
Brolio, J.,
Hanson, A.R., and
Riseman, E.M.,
The Schema System,
IJCV(2), No. 3, January? 1989, pp. 209-250.
Springer DOI
BibRef
8901
And:
COINSTR 88-76, UMass.
Domain knowledge in VISIONS is represented by schemas, which provide a
means to represent structured descriptions using features, relations,
uncertain links, etc. Each schema while operating in parallel, tries
to find evidence to support (or reject) the interpretation of a region
as an object, by communicating with schema for conflicting
interpretations. The whole schema system gives a means of resolving
conflicting interpretations much like other tree searching or graph
matching systems.
HTML Version.
BibRef
Draper, B.A.,
Hanson, A.R.,
Riseman, E.M.,
Learning Blackboard-Based Scheduling Algorithms for Computer Vision,
PRAI(7), 1993, pp. 309-328.
BibRef
9300
And:
COINSTR-92-51, August 1992.
PS File.
BibRef
Hanson, A.R., and
Riseman, E.M.,
From Image Measurements to Object Hypotheses,
COINSTR 87-129, UMass., December 1987.
Color. Description of generating symbolic interpretations of outdoor scenes
by combining many different techniques. The mapping of color
parameters is a 0, slope, 1 function.
BibRef
8712
Draper, B.A.,
Collins, R.T.,
Brolio, J.,
Griffith, J.S.,
Hanson, A.R., and
Riseman, E.M.,
Tools and Experiments in the Knowledge-Directed Interpretation
of Road Scenes,
DARPA87(178-193).
BibRef
8700
And:
COINSTR-87-05, January 1987.
More of the UMass system work. Region hypothesis
aids in merging, etc. This looks a lot like Yakimovsky's work(?).
BibRef
Lehrer, N.B.,
Reynolds, G.,
Griffith, J.,
A Method for Initial Hypothesis Formation in Image Understanding,
ICCV87(578-585).
BibRef
8700
Lehrer, N.B.,
Reynolds, G.,
Griffith, J.,
Initial Hypothesis Formation in Image Understanding Using an
Automatically Generated Knowledge Base,
DARPA87(521-237).
BibRef
8700
Draper, B.A.,
Statistical Properties of Learning Recognition Strategies,
DARPA93(557-565).
BibRef
9300
And:
Learning Object Recognition Strategies,
UMassCS TR-93-50, May 1993.
BibRef
Ph.D.Thesis (CS), University of Massachusetts, 1993.
BibRef
Draper, B.A.[Bruce A.],
Learning from the Schema Learning System,
AAAI-MLCV93(75-79).
University of Massachusetts.
PS File.
BibRef
9300
Draper, B.A., and
Riseman, E.M.,
Learning 3D Object Recognition Strategies,
ICCV90(320-324).
IEEE DOI
BibRef
9000
Draper, B.A.,
Hanson, A.R., and
Riseman, E.M.,
Learning Knowledge-Directed Visual Strategies,
DARPA92(933-940).
More on the use of Schemas.
BibRef
9200
Brolio, J.,
Draper, B.A.,
Beveridge, J.R., and
Hanson, A.R.,
ISR: A Database for Symbolic Processing in Computer Vision,
Computer(22), No. 12, December 1989, pp. 22-30.
BibRef
8912
And:
COINSTR-89-111, November 1989.
Issues of the database for the VISIONS system.
BibRef
Draper, B.A.,
Beveridge, J.R.,
Brolio, J.,
Hanson, A.R.,
Heller, R., and
Williams, L.R.,
ISR2: User's Guide,
COINSTR 90-52, UMass., July 1990.
New version of ISR database system for VISIONS system.
BibRef
9007
Draper, B.A.,
Hanson, A.R., and
Riseman, E.M.,
ISR3: A Token Database for Integration of Visual Modules,
DARPA93(1155-1161).
BibRef
9300
Draper, B.A.,
Kutlu, G.,
Riseman, E.M.,
Hanson, A.R.,
ISR3: Communication and Data Storage for an Unmanned Ground Vehicle,
ICPR94(A:833-836).
IEEE DOI
BibRef
9400
Kutlu, G.,
Draper, B.A.,
Moss, J.E.B.,
Riseman, E.M.,
Hanson, A.R.,
Persistent Data Management for Visual Applications,
ARPA96(1519-1524).
How to manage persistent objects
PS File.
BibRef
9600
Reynolds, G., and
Beveridge, J.R.,
Searching for Geometric Structure in Images of Natural Scenes,
DARPA87(257-271).
BibRef
8700
And:
COINSTR-87-03, January 1987.
Generate basic groupings based on transitive relations of parallel,
corners, collinear, etc. The clusters are then used for more
analysis. Schema are used to describe the objects and their
appearance in the image.
BibRef
Belknap, R.[Robert],
Riseman, E.M.,
Hanson, A.R.,
The Information Fusion Problem and Rule Based Hypothesis Applied
to Complex Aggregations of Image Events,
CVPR86(227-234).
BibRef
8600
And:
DARPA85(279-292).
BibRef
And:
COINS-TR-85-39, December 1985.
Data Fusion. This year's paper on the overall system. Edges and lines which
support the same interpretation.
BibRef
Reynolds, G.[George],
Irwin, N.[Nancy],
Hanson, A.R.[Allen R.], and
Riseman, E.M.[Edward M.],
Hierarchical Knowledge-Directed Object Extraction using a Combined Region
and Line Representation,
DARPA84(195-204).
BibRef
8400
And:
CVWS84(238-247).
Application, Cartography. Cartography application using very large images. The program
transforms the detailed pixel level representation into intermediate
general representations that combine regions and lines. Only some
areas are analyzed, based on the location of probable features by the
coarse analysis.
BibRef
Weymouth, T.E.,
Griffith, J.S.,
Hanson, A.R., and
Riseman, E.M.,
Rule Based Strategies for Image Interpretation,
DARPA83(193-202).
BibRef
8300
And:
AAAI-83(429-432).
Interpretation system, use knowledge about the appearance of objects
(houses) and how they combine to interpret the outdoor scenes.
BibRef
Weymouth, T.E.,
Experiments in Knowledge-Driven Interpretation of Natural Scenes,
IJCAI81(628-630).
BibRef
8100
Williams, T.D., and
Lowrance, J.,
Hanson, A.R.,
Riseman, E.M.,
Model Building in the Visions System,
IJCAI77(644-645).
BibRef
7700
Williams, T.D.,
Glazer, F.,
Comparison of Feature Operators for Use in Matching Image Pairs,
ISPDSA83(395-423).
BibRef
8300
Draper, B.A.,
Brolio, J.,
Collins, R.T.,
Hanson, A.R.,
Riseman, E.M.,
Image Interpretation by Distributed Cooperative Processes,
CVPR88(129-135).
IEEE DOI
BibRef
8800
Parma, C.C.,
Hanson, A.R.,
Riseman, E.M.,
Experiments in Schema-Driven Interpretation of a Natural Scene,
COINSTR 80-10, 1980.
BibRef
8000
Wesley, L.P., and
Hanson, A.R.,
The Use of an Evidential Based Model for Representing Knowledge
and Reasoning about Images in the VISIONS System,
CVWS82(14-25).
BibRef
8200
And:
COINSTR 82-29, December 1982.
System: VISIONS. Outlines some of the ideas behind Shafer and Dempster to combine
evidence. Basically evidence is a pair [support, plausibility],
minimum and maximum amount that the evidence confirms the proposition.
See also Mathematical Theory of Evidence, A.
BibRef
Konolige, K.G.,
York, B.W.[Bryant W.],
Hanson, A.R.,
Riseman, E.M.,
Between Regions and Objects: Surfaces and Volumes,
IJCAI77(646-647).
BibRef
7700
Riseman, E.M.,
Hanson, A.R.,
Design of a Semanitcally Directed Vision Processor,
COINSTR 74C-1, January 1974.
BibRef
7401
Kohler, R.R.,
Hanson, A.R.,
The VISIONS Image Operating System,
ICPR82(71-74).
BibRef
8200
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Knowledge Distillation .