Lucas, B.D.[Bruce D.],
Generalized Image Matching by the Method of Differences,
Ph.D.Thesis (CS), 1985.
BibRef
8500
CMU-CS-TR-85-160, CMU CS Dept.
Optical Flow.
BibRef
Lucas, B.D., and
Kanade, T.,
Optical Navigation by the Method of Differences,
IJCAI85(981-984).
BibRef
8500
And:
DARPA84(272-281).
Use the intensity gradient in an iterative matching
scheme to find the correspondence between two images.
BibRef
Lucas, B.D., and
Kanade, T.,
An Iterative Image Registration Technique with an
Application to Stereo Vision,
DARPA81(121-130).
HTML Version.
BibRef
8100
And:
IJCAI81(674-679).
HTML Version.
Code, Registration.
WWW Link. Another version in Matlab.
WWW Link. Uses differences in intensity between the two images and the
local gradient of one image (both?) to compute the shift.
A registration problem, but very applicable to stereo.
For more generalization:
See also Shape and Motion from Image Streams: A Factorization Method Part 3 - Detection and Tracking of Point Features.
BibRef
Lucas, B.D.,
Automatic Generation of Depth maps from Stereo Images,
DARPA82(309-314).
Basically assumes L(x,y)=R(x+h(x,y),y) and find h that minimizes
the error in the mapping. Find local errors in a neighborhood of
each point (use constraint of "real world" - smoothly varying
disparities). Apply different smoothing windows and h can be
computed at each of these (using the old value to limit the new
possibilities). The computations are essentially smoothing
operations. Also a Lucas paper at the Vancouver IJCAI.
BibRef
8200
Li, Z.N.,
Hu, G.Z.,
Analysis of Disparity Gradient-Based Cooperative Stereo,
IP(5), No. 11, November 1996, pp. 1493-1506.
IEEE DOI
9611
BibRef
Baker, S.[Simon],
Matthews, I.[Iain],
Lucas-Kanade 20 Years On: A Unifying Framework,
IJCV(56), No. 3, February-March 2004, pp. 221-255.
DOI Link
0402
BibRef
And:
Lucas-Kanade 20 Years On: A Unifying Framework: Part 1,
CMU-RI-TR-02-16, July 2002.
WWW Link.
0211
BibRef
And:
Lucas-Kanade 20 Years On,
CMU-RI2006, Project Description.
HTML Version.
Code, Tracking. Matlab code is available.
See also Generalized Image Matching by the Method of Differences.
See also Iterative Image Registration Technique with an Application to Stereo Vision, An.
BibRef
Baker, S.,
Gross, R.,
Matthews, I.,
Ishikawa, T.,
Lucas-Kanade 20 Years On: A Unifying Framework: Part 2,
CMU-RI-TR-03-01, February, 2003.
HTML Version.
0306
BibRef
Baker, S.,
Gross, R.,
Matthews, I.,
Lucas-Kanade 20 Years On: A Unifying Framework: Part 3,
CMU-RI-TR-03-35, November, 2003.
HTML Version.
0501
BibRef
Baker, S.,
Gross, R.,
Matthews, I.,
Lucas-Kanade 20 Years On: A Unifying Framework: Part 4,
CMU-RI-TR-04-14, February, 2004.
HTML Version.
0501
BibRef
Baker, S.,
Patil, R.,
Cheung, K.M.,
Matthews, I.,
Lucas-Kanade 20 Years On: Part 5,
CMU-RI-TR-04-64, November, 2004.
HTML Version.
0501
BibRef
Baker, S.,
Datta, A., and
Kanade, T.,
Parameterizing Homographies,
CMU-RI-TR-06-11, March, 2006.
HTML Version.
BibRef
0603
De-Maeztu, L.[Leonardo],
Villanueva, A.[Arantxa],
Cabeza, R.[Rafael],
Stereo matching using gradient similarity and locally adaptive
support-weight,
PRL(32), No. 13, 1 October 2011, pp. 1643-1651.
Elsevier DOI
1109
Stereo vision; Local correspondence search; Window-based; Adaptive
support-weight; Gradient
BibRef
de-Maeztu, L.[Leonardo],
Mattoccia, S.[Stefano],
Villanueva, A.[Arantxa],
Cabeza, R.[Rafael],
Linear stereo matching,
ICCV11(1708-1715).
IEEE DOI
1201
BibRef
De-Maeztu, L.[Leonardo],
Villanueva, A.[Arantxa],
Cabeza, R.[Rafael],
Near Real-Time Stereo Matching Using Geodesic Diffusion,
PAMI(34), No. 2, February 2012, pp. 410-416.
IEEE DOI
1112
Aggregation inspired by anisotropic diffusion filtering. GPU implementation
possible.
BibRef
Niu, Y.[Yan],
Xu, Z.W.[Zhi-Wen],
Che, X.J.[Xiang-Jiu],
Dynamically Removing False Features in Pyramidal Lucas-Kanade
Registration,
IP(23), No. 8, August 2014, pp. 3535-3544.
IEEE DOI
1408
feature extraction
See also Iterative Image Registration Technique with an Application to Stereo Vision, An.
BibRef
Miao, J.[Jun],
Chu, J.[Jun],
Zhang, G.M.[Gui-Mei],
Disparity map optimization using sparse gradient measurement under
intensity-edge constraints,
SIViP(10), No. 1, January 2016, pp. 161-169.
WWW Link.
1601
BibRef
Park, J.M.[Jeong-Min],
Song, G.Y.[Gwang-Yul],
Lee, J.W.[Joon-Woong],
Shape-indifferent stereo disparity based on disparity gradient
estimation,
IVC(57), No. 1, 2017, pp. 102-113.
Elsevier DOI
1702
Dense stereo.
BibRef
Ahmed, S.,
Hansard, M.,
Cavallaro, A.,
Constrained Optimization for Plane-Based Stereo,
IP(27), No. 8, August 2018, pp. 3870-3882.
IEEE DOI
1806
gradient methods, image reconstruction, nonlinear programming,
parameter estimation, probability, stereo image processing,
surface normal
BibRef
Lin, C.H.[Chen-Hsuan],
Zhu, R.[Rui],
Lucey, S.[Simon],
The Conditional Lucas & Kanade Algorithm,
ECCV16(V: 793-808).
Springer DOI
1611
See also Iterative Image Registration Technique with an Application to Stereo Vision, An.
BibRef
Hermann, S.[Simon],
Vaudrey, T.[Tobi],
The gradient: A powerful and robust cost function for stereo matching,
IVCNZ10(1-8).
IEEE DOI
1203
BibRef
Rav-Acha, A.,
Peleg, S.,
Lucas-Kanade without Iterative Warping,
ICIP06(1097-1100).
IEEE DOI
0610
See also Iterative Image Registration Technique with an Application to Stereo Vision, An.
See also Mosaicing with Parallax using Time Warping.
BibRef
Zhang, H.S.[Hong-Sheng],
Negahdaripour, S.[Shahriar],
BC&GC-Based Dense Stereo By Belief Propagation,
CVS06(14).
IEEE DOI
0602
Brightness constancy and gradient constancy --
apply optical flow ideas to stereo.
See also Revised Definition of Optical Flow: Integration of Radiometric and Geometric Cues for Dynamic Scene Analysis.
BibRef
Twardowski, T.[Tomasz],
Cyganek, B.[Boguslaw],
Borgosz, J.[Jan],
Gradient Based Dense Stereo Matching,
ICIAR04(I: 721-728).
Springer DOI
0409
BibRef
Tardon-Garcia, L.J.[Lorenzo-Jose],
Portillo-Garcia, J.[Javier],
Alberola-Lopez, C.[Carlos],
Markov Random Fields and the Disparity Gradient Constraint Applied to Stereo
Correspondence,
ICIP99(III:901-905).
IEEE DOI
See also Hypothesis Testing for Coarse Region Estimation and Stable Point Determination Applied to Markovian Texture Segmentation.
BibRef
9900
Trucco, E.,
Roberto, V.,
Tinonin, S., and
Corbatto, M.,
SSD Disparity Estimation for Dynamic Stereo,
BMVC96(Motion-Based Reconstruction).
9608
Heriot-Watt University and University of Udine, Italy
BibRef
Chapter on Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular continues in
Line Segment Based Stereo Analysis, Line Matching .