4.6 General Computational Vision

Chapter Contents (Back)
Computational Vision.

Fornay, G.D.,
The Viterbi Algorithm,
PIEEE(61), No. 3, March 1973, pp. 268-277. An algorithm to compute the optimal (most likely) state sequence in a hidden Markov model given a sequence of observed outputs. (in TN**2 steps). See also Error bounds for convolutional codes and an asymptotically optimal decoding algorithm. BibRef 7303

Barrow, H.G., and Tenenbaum, J.M.,
Computational Vision,
PIEEE(69), No. 5, May 1981, pp. 572-595. BibRef 8105

Brady, M.,
Computational Approaches to Image Understanding,
Surveys(14), No. 1, March 1982, pp. 3-71. BibRef 8203
And: MIT AI Memo-653, October 1981. Survey, Computational Vision. Computational Vision, Survey. Survey of much of the DARPA research (up to early 81). Concentrates on general processing techniques and ignores applications. Separates the descriptions based on the level of representation being used, especially those that relate to 3-D descriptions. Mostly a review of MIT work with references to other similar and related work, but little unrelated work. Good references on topics covered. BibRef

Brady, M.[Mike],
Toward a Computational Theory of Early Visual Processing in Reading,
MIT AI Memo-593, September 1980. BibRef 8009

Ullman, S.,
Visual Routines,
Cognition(18), 1984, pp. 97-156. BibRef 8400 RCV87(298-328). BibRef
And: MIT AI Memo-723, June 1983. BibRef
And:
Visual Routines: Where bottom-Up and Top-down Processing Meet,
PR(2), 1986, pp. 159-213. This second reference cannot be correct, not for the Pattern Recognition journal. General discussion of the later processing of visual information. BibRef

Lee, D.,
Some Computational Aspects of Low-Level Computer Vision,
PIEEE(76), No. 8, August 1988, pp. 890-898. BibRef 8808

Uttal, W.R., Liu, N., and Kalki, J.,
An Integrated Computational Model of Three-Dimensional Vision,
SV(9), 1996, pp. 393-422. BibRef 9600

Ritter, G.X., (Guest ed.),
Special Issue on Mathematical Imaging,
JMIV(5), No. 4, December 1995, 275-358. BibRef 9512

Jobson, D.J., Rahman, Z.U., Woodell, G.A.,
Properties and Performance of a Center/Surround Retinex,
IP(6), No. 3, March 1997, pp. 451-462.
IEEE DOI 9703
BibRef

Daugman, J.G.,
Neural Image-Processing Strategies Applied in Real-Time Pattern-Recognition,
RealTimeImg(3), No. 3, June 1997, pp. 157-171. 9708
BibRef

Fejes, S., Rosenfeld, A.,
Migration Processes I: the Continuous Case,
JMIV(8), No. 1, January 1998, pp. 5-25.
DOI Link 9803
BibRef

Fejes, S., Rosenfeld, A.,
Migration Processes II: the Discrete Case,
JMIV(8), No. 1, January 1998, pp. 27-40.
DOI Link 9803
BibRef

Fejes, S., Rosenfeld, A.,
Migration Processes,
ICPR96(II: 345-349).
IEEE DOI 9608
(Univ. of Maryland, USA) BibRef

Fejes, S.,
Migration Processes: Theory and applications,
UMDTechnical report. CS-TR-3603, CAR-TR-813TR, November 1995.
WWW Link. BibRef 9511

Ritter, G.X., Shi, H.C.,
Special Section on Advances in Mathematical Imaging,
JEI(6), No. 4, October 1997, pp. 393-394. 9807
BibRef

Zhu, S.C.[Song Chun], Yuille, A.L.[Alan L.], Mumford, D.[David],
Guest Editorial: Statistical and Computational Theories of Vision: Modeling, Learning, Sampling and Computing, Part I,
IJCV(40), No. 1, October 2000, pp. 5-6.
DOI Link 0101
Introduction to the special issue. BibRef

Abubakar, A., van den Berg, P.M.,
Total variation as a multiplicative constraint for solving inverse problems,
IP(10), No. 9, September 2001, pp. 1384-1392.
IEEE DOI 0108
BibRef

Cohen, L.D.[Laurent D.],
Guest Editorial: Special Issue on Mathematics and Image Analysis,
JMIV(20), No. 1-2, January-March 2004, pp. 5-5.
DOI Link 0403
BibRef

Cohen, L.D.[Laurent D.],
Guest Editorial,
JMIV(25), No. 3, October 2006, pp. 287.
Springer DOI 0611
Special issue intro. BibRef

Cohen, L.D.[Laurent D.], Sochen, N.A.[Nir A.], Vese, L.A.[Luminita A.],
Guest Editorial, Special Issue Introduction,
JMIV(33), No. 2, February 2009, pp. xx-yy.
Springer DOI 0903
BibRef

Prabhu, N.[Nagabhushana], Chang, H.C.[Hung-Chieh], de Guzman, M.[Maria],
Optimization on Lie manifolds and pattern recognition,
PR(38), No. 12, December 2005, pp. 2286-2300.
WWW Link. 0510
Reduce vision problem to optimizing nonlinear function over a Lie manifold. BibRef

Han, Y.,
Newton type algorithm on Riemannian manifolds applied to robot vision, and suggestions for improvement of its performance,
VISP(152), No. 3, June 2005, pp. 275-282.
DOI Link 0510
BibRef

Kragic, D.[Danica], Kyrki, V.[Ville], (Eds.)
Unifying Perspectives in Computational and Robot Vision,
Springer2008, ISBN: 978-0-387-75521-2 Survey, Computational Vision.
WWW Link. Buy this book: Unifying Perspectives in Computational and Robot Vision (Lecture Notes in Electrical Engineering) BibRef 0800

Kokkinos, I.[Iasonas], Yuille, A.L.[Alan L.],
Inference and Learning with Hierarchical Shape Models,
IJCV(93), No. 2, June 2011, pp. 201-225.
WWW Link. 1104
BibRef
Earlier:
HOP: Hierarchical object parsing,
CVPR09(802-809).
IEEE DOI 0906
BibRef
And:
Inference and learning with hierarchical compositional models,
SIG09(6-6).
IEEE DOI 0906
BibRef

Yuille, A.L.,
Towards a theory of compositional learning and encoding of objects,
ITCVPR11(1448-1455).
IEEE DOI 1201
BibRef

Kokkinos, I.[Iasonas], Maragos, P.[Petros], Yuille, A.L.[Alan L.],
Bottom-Up and Top-down Object Detection using Primal Sketch Features and Graphical Models,
CVPR06(II: 1893-1900).
IEEE DOI 0606
BibRef

Kokkinos, I.[Iasonas], Deriche, R.[Rachid], Maragos, P.[Petros], Faugeras, O.D.[Olivier D.],
A Biologically Motivated and Computationally Tractable Model of Low and Mid-Level Vision Tasks,
ECCV04(Vol II: 506-517).
Springer DOI 0405
BibRef

Gill, P.R.[Patrick R.],
Enabling a computer to do the job of a lens,
SPIE(Newsroom), September 4, 2013.
DOI Link 1310
A new kind of diffractive phase grating permits computational imaging of polychromatic distant objects in situations where focal optics are not convenient. BibRef

Yang, C.H.[Chang-Huei],
Computational microscopy improves resolution, field of view,
SPIE(Newsroom), October 23, 2013.
DOI Link 1310
A complete data set derived from low-resolution snapshots could lead to cost-effective autonomous digital pathology. BibRef

Mitra, K.[Kaushik], Cossairt, O.S.[Oliver S.], Veeraraghavan, A.[Ashok],
A Framework for Analysis of Computational Imaging Systems: Role of Signal Prior, Sensor Noise and Multiplexing,
PAMI(36), No. 10, October 2014, pp. 1909-1921.
IEEE DOI 1410
Gaussian processes BibRef

Cossairt, O.S.[Oliver S.], Mitra, K.[Kaushik], Veeraraghavan, A.[Ashok],
Analyzing computational imaging systems,
SPIE(Newsroom), November 19, 2013.
DOI Link 1311
A novel framework, which takes into account optical multiplexing, sensor noise characteristics, and signal priors, can analyze any linear computational imaging camera. BibRef

Mitra, K.[Kaushik], Cossairt, O.S.[Oliver S.], Veeraraghavan, A.[Ashok],
Can we beat Hadamard multiplexing? Data driven design and analysis for computational imaging systems,
ICCP14(1-9)
IEEE DOI 1411
Gaussian processes BibRef

Greengard, S.[Samuel],
Seeing the Big Picture,
CACM(56), No. 12, December 2013, pp. 16-18.
DOI Link 1312
Lensless cameras and other advances in digital imaging, computational optics, signal processing, and big data are transforming how we think about photography. BibRef

Rasanen, O., Kakouros, S.,
Modeling Dependencies in Multiple Parallel Data Streams with Hyperdimensional Computing,
SPLetters(21), No. 7, July 2014, pp. 899-903.
IEEE DOI 1405
Context BibRef


Chang, Y.Y.[Yuan-Yang], Chen, H.T.[Hwann-Tzong],
Finding good composition in panoramic scenes,
ICCV09(2225-2231).
IEEE DOI 0909
Find good (artistic composition) sub views. BibRef

Soatto, S.[Stefano],
Actionable information in vision,
ICCV09(2138-2145).
IEEE DOI 0909
Complexity not of the image itself, but the image after removal of effects of viewpoint and illumination. BibRef

Franc, V.[Vojtech], Hlavác, V.[Václav], Navara, M.[Mirko],
Sequential Coordinate-Wise Algorithm for the Non-negative Least Squares Problem,
CAIP05(407).
Springer DOI 0509
Least Squares. The proposed algorithm showed promising performance in comparison to the Landweber method. BibRef

Fischer, S.[Sylvain], Bayerl, P.[Pierre], Neumann, H.[Heiko], Cristóbal, G.[Gabriel], Redondo, R.[Rafael],
Are Iterations and Curvature Useful for Tensor Voting?,
ECCV04(Vol III: 158-169).
Springer DOI 0405
Add iterations and curvature enhancements. See also Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data. BibRef

Nayar, S.K.,
Computational Imaging,
ICIP01(Invited Talk, Computational Imaging). 0108
Not in proceedings. BibRef

Woodham, R.J.,
A Computational Approach to Remote Sensing,
CVPR85(2-12). (UBC) Nice discussion of various techniques. BibRef 8500

Edelman, S.[Shimon], Weinshall, D.[Daphna],
Computational Vision: A Critical Review,
MIT AI Memo-1158, October 1989. BibRef 8910

Thompson, W.B.[William B.], and Yonas, A.[Albert],
What Should be Computed In Low Level Vision Systems,
AAAI-80(7-10). BibRef 8000

Hildreth, E.C.[Ellen C.], Ullman, S.[Shimon],
The Computational Study of Vision,
MIT AI Memo-1038, April 1988.
WWW Link. BibRef 8804

Hildreth, E.C.[Ellen C.], Hollerbach, J.M.[John M.],
The Computational Approach to Vision and Motor Control,
MIT AI Memo-846, August 1985.
WWW Link. BibRef 8508

Hollerbach, J.M.[John M.],
Hierarchical Shape Description of Objects by Selection and Modification of Prototypes,
MIT AI-TR-346 November 1975.
WWW Link. BibRef 7511

Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Physics Based Vision .


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