10.8 Shape Computations from Multiple Sensors

Chapter Contents (Back)
These papers generally combine different types of data for shape computations. Fusion. Multiple Sensors. Sensor Fusion.

Ivancevic, N.S.[Nebojsa S.],
Stereometric pattern recognition by artificial touch,
PR(6), No. 2, October 1974, pp. 77-83.
WWW Link. 0309

Kinoshita, G.I.[Gen-Ichiro], Aida, S.[Shuhei], Mori, M.[Masahiro],
A pattern classification by dynamic tactile sense information processing,
PR(7), No. 4, December 1975, pp. 243-251.
WWW Link. 0309

Okada, T., Tsuchiya, S.,
Object recognition by grasping,
PR(9), No. 3, October 1977, pp. 111-119.
WWW Link. 0309

Magee, M.J., and Aggarwal, J.K.,
Using Multisensory Images to Derive the Structure of Three-Dimensional Objects: A Review,
CVGIP(32), No. 2, November 1985, pp. 145-157.
WWW Link. Review of intensity only or range only analysis in order to produce a better combined method. BibRef 8511

Wang, Y.F., and Aggarwal, J.K.,
Integration of Active and Passive Sensing Techniques for Representing Three-Dimensional Objects,
RA(5), No. 4, August 1989, pp. 460-471. BibRef 8908
Earlier: Univ. of TexasTR-CV TR 87-1-33, March 1987. Multiple viewing directions with both passive and active sensors. It seems to combine several methods. BibRef

Wang, Y.F., Lee, J.F.[Jeng-Feng], and Wang, J.F.[Jih-Fang],
Unification Scheme for 3D Surface Reconstruction Using Physically Based Models,
IJIST(3), 1991, pp. 279-299. BibRef 9100

Wang, Y.F., Wang, J.F.,
On 3D Model Construction By Fusing Heterogeneous Sensor Data,
CVGIP(60), No. 2, September 1994, pp. 210-229.
WWW Link. BibRef 9409
And: With Lee, J.F.[Jeng-Feng], as the second author:
On 3D Model Construction by Fusing Heterogeneous Sensor Data,
DraftGeneral discussion, but mostly intensity and structured light. BibRef

Wang, Y.F., and Cheng, D.I.[David I.],
Three-Dimensional Shape Construction and Recognition by Fusing Intensity and Structured Lighting,
PR(25), No. 12, December 1992, pp. 1411-1425.
WWW Link. Sensor Fusion. BibRef 9212

Richardson, J.M., and Marsh, K.A.,
Fusion of Multisensor Data,
IJRR(7), No. 6, 1988, pp. 78-96. BibRef 8800

Leonard, J.J., Durrant-Whyte, H.F., and Cox, I.J.,
Dynamic Map Building for an Autonomous Mobile Robot,
IJRR(11), No. 4, 1992, pp. 286-298. See also Mobile Robot Location by Tracking Geometric Beacons. BibRef 9200

Cox, I.J.[Ingemar J.],
Stereoscopic computer vision system,
US_Patent5,383,013, Jan 17, 1995
WWW Link. Find best match between features in 2 images. BibRef 9501

Cox, I.J.,
A Maximum Likelihood N-Camera Stereo Algorithm,
IEEE DOI Improve stereo results by using multiple images. BibRef 9400

Cox, I.J., Hingorani, S.L., Rao, S.B., Maggs, B.M.,
A Maximum-Likelihood Stereo Algorithm,
CVIU(63), No. 3, May 1996, pp. 542-567.
DOI Link 9606

Cox, I.J., Hingorani, S., Naggs, B.M., and Rao, S.B.,
Stereo without Disparity Gradient Smoothing: A Bayesian Sensor Fusion Solution,
PDF File. See related code:
WWW Link. BibRef 9200

Durrant-Whyte, H.F.,
Integration, Coordination and Control of Multi-Sensor Robot Systems,
Norwell, MA: KluwerAcademic Publishers, November 1990. ISBN 0-89838-247-5 Sensor Fusion. Stereo analysis and tactile gripper on a robot arm.
WWW Link. BibRef 9011

Durrant-Whyte, H.F.,
Sensor Models and Multisensor Integration,
IJRR(7), No. 6, 1988, pp. 97-113. BibRef 8800
Earlier: SRMSF87(303-312). See also Consistent Integration and Propagation of Disparate Sensor Observations. BibRef

Henderson, T.C., Weitz, E., Hansen, C., and Mitiche, A.,
Multisensor Knowledge Systems: Interpreting 3D Structure,
IJRR(7), No. 6, 1988, pp. 114-137. BibRef 8800

Porrill, J.,
Optimal Combination and Constraints for Geometrical Sensor Data,
IJRR(7), No. 6, 1988, pp. 66-77. BibRef 8800

Shekhar, S., Khatib, O., and Shimojo, M.,
Object Localization with Multiple Sensors,
IJRR(7), No. 6, 1988, pp. 34-44. BibRef 8800

Stansfield, S.A.,
A Robotic Perceptual System Utilizing Passive Vision and Active Touch,
IJRR(7), No. 6, 1988, pp. 138-161. BibRef 8800

Bolle, R.M., and Cooper, D.B.,
On Optimally Combining Pieces of Information, with Application to Estimating 3-D Complex-Object Position from Range Data,
PAMI(8), No. 5, September 1986, pp. 619-638. Probability. A Bayesian, probabilistic approach to dividing problems and data. An attempt to put everything in a probabilistic framework, models and matching etc. There is either a lot in the paper or it is empty. BibRef 8609

Negahdaripour, S.[Shahriar],
Epipolar Geometry of Opti-Acoustic Stereo Imaging,
PAMI(29), No. 10, October 2007, pp. 1776-1788.
Geometric analysis of 3-D using stereo and sonar together. BibRef

Negahdaripour, S., Sekkati, H., Pirsiavash, H.,
Opti-Acoustic Stereo Imaging: On System Calibration and 3-D Target Reconstruction,
IP(18), No. 6, June 2009, pp. 1203-1214.
Opti-Acoustic Stereo Imaging, System Calibration and 3-D Reconstruction,

Sekkati, H., Negahdaripour, S.,
Direct and Indirect 3-D Reconstruction from Opti-Acoustic Stereo Imaging,

Negahdaripour, S., Sarafraz, A.,
Improved Stereo Matching in Scattering Media by Incorporating a Backscatter Cue,
IP(23), No. 12, December 2014, pp. 5743-5755.
backscatter BibRef

Chatterjee, A.[Avishek], Govindu, V.M.[Venu Madhav],
Photometric refinement of depth maps for multi-albedo objects,

Arsenio, A.M.[Artur M.],
Map Building from Human-Computer Interactions,
Build 3-D map from human cues. Figure/Ground, furniture detection. BibRef

Kanade, T., Saito, H., and Vedula, S.,
The 3D Room: Digitizing Time-Varying 3D Events by Synchronized Multiple Video Streams,
CMU-RI-TR-98-34, December, 1998. BibRef 9812

Chapter on Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular continues in
Shape from Two or More Properties .

Last update:Sep 18, 2017 at 11:34:11