Ikeuchi, K.[Katsushi],
Kanade, T.[Takeo],
Modeling Sensors:
Toward Automatic Generation of Object Recognition Program,
(sic),
CVGIP(48), No. 1, October 1989, pp. 50-79.
Elsevier DOI
BibRef
8910
Earlier:
Applying Sensor Models to Automatic Generation of Object
Recognition Programs,
ICCV88(228-237).
IEEE DOI
BibRef
And:
Modeling Sensors and Applying Sensor Model to Automatic
Generation of Object Recognition Program,
DARPA88(697-710).
BibRef
Earlier:
Modeling Sensor Detectability and Reliability for
Model-Based Vision,
CVWS87(288-294).
Recognize Three-Dimensional Objects. More on the basic bin-picking task, how to model the appearance
to aid in generating constraints.
See also Towards an Assembly Plan from Observation, Part I: Task Recognition with Polyhedral Objects.
BibRef
Sato, K.[Kosuke],
Ikeuchi, K.[Katsushi],
Kanade, T.[Takeo],
Model Based Recognition of Specular Objects Using Sensor Models,
CVGIP(55), No. 2, March 1992, pp. 155-169.
Elsevier DOI
BibRef
9203
Earlier:
CADBV91(2-10).
Generating and recognizing specular objects using aspect graph
techniques. Use a different aspect based on changes in specular
reflections.
BibRef
Ikeuchi, K.[Katsushi],
Kanade, T.[Takeo],
Automatic Generation of Object Recognition Programs,
PIEEE(76), No. 8, August 1988, pp. 1016-1035.
BibRef
8808
And:
Towards Automatic Generation of Object Recognition Programs,
CMU-CS-TR-88-138, CMU CS Dept., May 1988.
Similar to the above papers.
BibRef
Wheeler, M.D., and
Ikeuchi, K.,
Sensor Modeling, Probabilistic Hypothesis Generation, and
Robust Localization for Object Recognition,
PAMI(17), No. 3, March 1995, pp. 252-265.
IEEE DOI
BibRef
9503
Earlier:
Sensor Modeling, Markov Random Fields, and Robust Localization
for Reconstructing Partially Occluded Objects,
DARPA93(811-818).
BibRef
And:
Towards a Vision Algorithm
Compiler for Recognition of Partially Occluded 3-D Objects,
CMU-CS-TR-92-185, CMU CS Dept.
Hypothesize and Verify. Region matching of model and image.
Develop from the Vision Algorithm Compiler for occluded objects.
BibRef
Wheeler, M.D.[Mark Damon],
Automatic Modeling and Localization for Object Recognition,
CMU-CS-TR-96-188, October 1996.
BibRef
9610
Ph.D.Thesis
BibRef
Gremban, K.D., and
Ikeuchi, K.,
Planning Multiple Observations for Object Recognition,
IJCV(12), No. 2-3, April 1994, pp. 137-172.
Springer DOI
BibRef
9404
Earlier:
CMU-CS-TR-92-146, CMU CS Dept., December 1992.
BibRef
Earlier:
Appearance-Based Vision and the
Automatic Generation of Object Recognition Plans,
CMU-CS-TR-92-159, CMU CS Dept., July 1992.
How to generate the models needed for matching. Generate the model
using ananlytical technique and using an appearance simulator to
synthesize images.
BibRef
Hong, K.S.,
Ikeuchi, K., and
Gremban, K.D.,
Minimum Cost Aspect Classification:
A Module of a Vision Algorithm Compiler,
ICPR90(I: 65-69).
IEEE DOI
BibRef
9000
And:
CMU-CS-TR-90-124, April 1990.
BibRef
Ikeuchi, K.[Katsushi],
Hong, K.S.[Ki Sang],
Determining Linear Shape Change:
Towards Automatic Generation of Object Recognition Programs,
CVGIP(53), No. 2, March 1991, pp. 154-170.
Elsevier DOI
BibRef
9103
Earlier:
CVPR89(450-457).
IEEE DOI
BibRef
And:
CMU-CS-TR-88-188, CMU CS Dept., December 1989.
Recognize Three-Dimensional Objects. Automatically compile the object descriptions into the recognition
programs!
BibRef
Ikeuchi, K.,
Generating an Interpretation Tree from a CAD Model
for 3D-Object Recognition in Bin-Picking Tasks,
IJCV(1), No. 2, 1987, pp. 145-166.
Springer DOI
BibRef
8700
Earlier:
Precompiling a Geometric Model into an Interpretation Tree for
Object Recognition in Bin-Picking Tasks,
DARPA87(321-339).
CAD.
Recognize Three-Dimensional Objects. The 3D model is transformed into a set of general 2D views to
allow matching. These views are based on the visible surfaces.
First determine which of the groups of views is valid, then
determine the precise attitude of the object, then pick it up.
BibRef
Ikeuchi, K., and
Shirai, Y.,
A Model Based Vision System for Recognition of Machine Parts,
AAAI-82(18-21).
Recognize Three-Dimensional Objects. Needle maps (extended Gaussian sphere) are generated for the model
at a given viewing angle (determined from the image) and the image
based on the shading information (called photometric stereo - just
shape from shading). Three light sources are used to eliminate
ambiguities in surface orientation - it becomes a table lookup.
Attitude (of object) is reduced by analysis of possibilities - only
a few stable states. Thus a few models to check.
BibRef
8200
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
3-D Object Recognition Using Invariants .