10.1.5 Stereo Analysis - Gradient Based

Chapter Contents (Back)
Gradient Techniques. Stereo, Gradient Approach.

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.[Guimei],
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


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 .


Last update:May 25, 2017 at 22:18:08