3.6 Computer Vision Meta Discussions

Chapter Contents (Back)
General Topics.

Brady, M.,
Computer Vision: Correspondent's Report,
AI(19), No. 1, September 1982, pp. 7-16.
WWW Link. BibRef 8209

Brady, M.,
Parallelism in Vision: Correspondent's Report,
AI(21), No. 3, September 1983, pp. 271-284.
Elsevier DOI BibRef 8309

Nevatia, R.,
Characterization and Requirements of Computer vision Systems,
CVS78(81-87). BibRef 7800 USC Computer Vision BibRef

Reddy, R.,
Pragmatic Aspects of Machine Vision,
CVS78(89-98). BibRef 7800

Longuet-Higgins, H.C., Sutherland, N.S., (Eds.)
The Psychology of Vision,
Royal(B-290), 1980, pp. 1-218. Discussion at the Royal Society, and the special issue with the papers. BibRef 8000

Kanade, T., Reddy, R.,
Computer Vision: The Challenge of Imperfect Inputs,
Spectrum(20), No. 11, November 1983, pp. 88-91. BibRef 8311

Trivedi, M.M.[Mohan M.], and Rosenfeld, A.[Azriel],
On Making Computers See,
SMC(19), No. 6, November/December 1989, pp. 1333-1335. Special issue introduction. BibRef 8911

Aloimonos, Y., Fermüller, C., Rosenfeld, A.,
Seeing and Understanding: Representing the Visual World,
Surveys(27), No. 3, September 1995, pp. 307-309. Part of a series of articles in the volume of Surveys. BibRef 9509

Fermüller, C., Aloimonos, Y.,
Ordinal Representations of Visual Space,
ARPA96(897-904). BibRef 9600

Rosenfeld, A.[Azriel],
Recognizing Unexpected Objects: A Proposed Approach,
PRAI(1), No. 1, 1987, pp. 71-84. Recognize General Objects. Discussion of how people seem to recognize unexpected objects very quickly and a proposal of how it might be done: parallel recognition of parts and parallel combination at the object level. BibRef 8700

Granlund, G.H.[Gösta H.], Knutsson, H., Westelius, C.J., Wiklund, J.,
Issues in Robot Vision,
IVC(12), No. 3, April 1994, pp. 131-148.
WWW Link. BibRef 9404
Earlier: A1 only: BMVC93(xx-yy).
PDF File. BibRef

Bottoni, P., Mussio, P., Protti, M.,
Metareasoning in the Determination of Image Interpretation Strategies,
PRL(15), No. 2, February 1994, pp. 177-190. BibRef 9402

Bottoni, P., Cinque, L., Levialdi, S., Mussio, P., Nebbia, B.,
Structural characterisation of image processing operators,
CIAP97(I: 430-437).
Springer DOI 9709

Jolion, J.M.,
Computer Vision Methodologies,
CVGIP(59), No. 1, January 1994, pp. 53-71.
WWW Link. Analysis of the Marr paradigm, propose a new framework that eploits all sources of constraints. BibRef 9401

Fisher, R.B.,
Is Computer Vision Still AI?,
AIMag(15), No. 2, 1994, pp. 21-27. BibRef 9400 Edinburgh BibRef

Quek, F.K.H.,
Eyes in the Interface,
IVC(13), No. 6, August 1995, pp. 511-525.
WWW Link. BibRef 9508

Shafer, S.,
Panel on Computer Vision: Past, Present and Future,
CVPR94(no printed paper). Led by Shafer BibRef 9400

Marr, D., and Nishihara, H.K.,
Visual Information Processing: Artificial Intelligence and the Sensorium of Sight,
Technology Review(81), No. 1, January 1978, pp. 2-23. Reprinted: BibRef 7801 RCV87(616-637). General paper on vision in humans and the computer. BibRef

Haralick, R.M.[Robert M.], and Ullmann, J.R.[Julian R.],
A Report on the 1982 IEEE Computer Vision Workshop,
TRunpublished report, 1982. This report summarizes the papers presented at the workshop and gives some suggestions for the future of IU research. The usual plea for avoiding ad hoc solutions to particular problems and to address the real problems involved in computer vision. BibRef 8200

Haralick, R.M.[Robert M.],
Computer Vision Theory: The Lack Thereof,
CVGIP(36), No. 2/3, November/December 1986, pp. 372-386.
WWW Link. BibRef 8611
Earlier: CVWS85(113-121). The paper explores some theories of matching and explores how to compare them. BibRef

Asada, M., and Tsuji, S.,
Introduction: Machine Vision Research at Osaka University,
IJCV(9), No. 1, October 1992, pp. 5-11.
Springer DOI The overview for the special issue with many references for the Osaka work. BibRef 9210

Ahuja, N., Horaud, R.,
Special Volume on Computer Vision: Introduction,
AI(78), No. 1-2, October 1995, pp. 1-3.
WWW Link. BibRef 9510

di Ruocco, N., Vitale, A., Vitulano, S.,
Artificial Intelligence In Vision,
IEEE DOI BibRef 9200

Shirai, Y.,
3D Computer Vision and Applications,
IEEE DOI BibRef 9200

Kummert, F., Sagerer, G.F., Niemann, H.,
A Problem-Independent Control Algorithm for Image Understanding,
IEEE DOI BibRef 9200

Kanatani, K.,
Geometric Computation for Machine Vision,
Oxford, UK: Oxford University Press1993. Covers computational projective geometry applied to 3D shape, camera calibration, motion, and optical flow. BibRef 9300

Kanatani, K.,
Group Theoretic Methods in Image Understanding,
Berlin: Springer1990. BibRef 9000

Livingston, M., and Huber, D.,
Segregation of Form, Color, Movement, and Depth: Anatomy, Physiology, and Perception,
Science(240), May 6, 1988, pp. 740-749. Human vision discussion. BibRef 8805

Kender, J.R.[John R.],
Environmental Relations in Image Understanding: The Force of Gravity,
DARPA83(249-256). BibRef 8300
Environmental Labelings in Low Level Image Understanding,
IJCAI83(1104-1107). Use horizontal and vertical assumptions to aid in interpreting an image. BibRef

Tsotsos, J.K.,
A 'Complexity Level' Analysis of Immediate Vision,
IJCV(1), No. 4, January, 1988, pp. 303-320.
Springer DOI BibRef 8801
Earlier: ICCV87(346-355). Award, Marr Prize, HM. BibRef
Analyzing Vision at the Complexity Level,
BBS(13), 1990, pp. 423-469. BibRef
Analyzing Vision at the Complexity Level: Constraints on an Architecture, An Explanation for Visual Search Performance, and Computational Justification for Attentive Processes,
RBCV-TR-87-20, September, 1987, Toronto. What are the complexity constraints imposed by the human visual system on the type of processing? Vision, only bottom-up, not goal directed is exponential in pixels, but structure wins. BibRef

Tsotsos, J.K.,
The Complexity of Perceptual Search Tasks,
IJCAI89(1571-1577), BibRef 8900
And: RBCV-TR-89-28, April 1989, Toronto. This one states that vision is NP-complete (bottom-up) or linear (task directed). BibRef

Haber, R.N.,
Toward a Theory of the Perceived Spatial Layout of Scenes,
CVGIP(31), No. 3, September 1985, pp. 282-321.
WWW Link. BibRef 8509

Herman, G.T.,
On Topology as Applied to Image Analysis,
CVGIP(52), No. 3, December 1990, pp. 409-415.
WWW Link. Extends the claim of Kovalevsky regarding topology of cellular complexes. BibRef 9012

Brown, C.M.[Chris M.],
Computer Vision and Natural Constraints,
Science(224), No. 4655, 22 June 1984, pp. 1299-1305. Discussion of the various constraints that can be applied to the computer vision problem, such as geometry, sensor, coherence, physics, etc. BibRef 8406

Kak, A.C.,
Sensory Aspects of Robotic Intelligence,
AIMag(13), No. 1, Spring 1992, pp. 20-21. BibRef 9200

Koza, J.R.,
Automated Discovery of Detectors and Interation -- Performing Calculations to Recognize Patterns in Protein Sequences Using Genetic Programming,
IEEE Abstract. Genetic Algorithms. BibRef 9400

Brooks, R.A.,
Intelligence without Representation,
AI(47), No. 1-3, January 1991, pp. 139-159.
WWW Link. His argument for no representation -- i.e. reactive systems. BibRef 9101

Kirsh, D.,
Today the Earwig, Tomorrow Man?,
AI(47), No. 1-3, January 1991, pp. 161-184.
WWW Link. The response to Brooks on why representations are necessary. BibRef 9101

Onuallain, S., Smith, A.G.,
An Investigation Into The Common Semantics Of Language And Vision,
AIR(8), No. 2-3, 1994, pp. 113-122. BibRef 9400

Herzog, G., Wazinski, P.,
Visual Translator: Linking Perceptions And Natural-Language Descriptions,
AIR(8), No. 2-3, 1994, pp. 175-187. BibRef 9400

Nagel, H.H.,
A Vision of Vision and Language Comprises Action: An Example From Road Traffic,
AIR(8), No. 2-3, 1994, pp. 189-214. BibRef 9400

Nagel, H.H.[Hans-Hellmut],
Reflections on Cognitive Vision Systems,
CVS03(34 ff).
Springer DOI 0306

Oka, R.,
The Real-World Computing Program,
AIR(8), No. 2-3, 1994, pp. 105-111. BibRef 9400

Oka, R.,
Hierarchical Labeling For Integrating Images And Words,
AIR(8), No. 2-3, 1994, pp. 123-145. BibRef 9400

Firschein, O.,
The Image Understanding Program at ARPA,
IEEE_EXPERT(10), No. 5, October 1995, pp. 8-10. BibRef 9510

Firschein, O.,
Defense Applications of Image Understanding,
IEEE_EXPERT(10), No. 5, October 1995, pp. 11-17. BibRef 9510

Grimson, W.E.L.,
Medical Applications of Image Understanding,
IEEE_EXPERT(10), No. 5, October 1995, pp. 18-28.
PDF File. Application, Medical. BibRef 9510

Inoue, H.,
Vision-Based Robotics: A Challenge to Real-World Artificial-Intelligence,
AdvRob(9), No. 4, 1995, pp. 351-366. BibRef 9500

Loughlin, C.,
Vision Of The Future,
IndRob(22), No. 6, 1995, pp. 2-2. Industrial Applications. BibRef 9500

Braggins, D.,
A Critical-Look At Robot Vision,
IndRob(22), No. 6, 1995, pp. 9-12. BibRef 9500

Pavlidis, T.,
A Critical Survey of Image Analysis Methods,
ICPR86(502-511), expanded version. Survey, Image Analysis. Image Analysis, Survey. Generally we have been very slow in integrating results into useful programs. BibRef 8600

Fairhurst, M.C.,
Special Section from IPA95,
VISP(143), No. 4, August 1996, pp. 233-233. 9611

Petkovic, D., Wilder, J.,
Machine Vision in the 1990s: Applications and How to Get There,
MVA(4), 1991, pp. 113-126. BibRef 9100

Mason, M.T.[Matthew T.],
Kicking the Sensing Habit,
AIMag(14), No. 1, Spring 1993, pp. 58-59. Real sensing for robots is hard and may not help. BibRef 9300

Miller, D.P.[David P.],
A Twelve-Step Program to More Efficient Robotics,
AIMag(14), No. 1, Spring 1993, pp. 60-63. See the previous paper. BibRef 9300

Miller, D.P.[David P.],
The Long-Term Effects of Secondary Sensing,
AIMag(15), No. 1, Spring 1994, pp. 52-56. See the previous two papers. Keep the sensing simple and it will be faster. BibRef 9400

Pavlidis, T.,
Why Progress in Machine Vision Is so Slow,
PRL(13), 1992, pp. 221-225. BibRef 9200

Chella, A.[Antonio], Frixione, M.[Marcello], Gaglio, S.[Salvatore],
A Cognitive Architecture for Artificial Vision,
AI(89), No. 1-2, January 1997, pp. 73-111.
WWW Link. 9704

Chella, A.[Antonio], Frixione, M.[Marcello], Gaglio, S.[Salvatore],
Conceptual Spaces for Computer Vision Representations,
AIR(16), No. 2, October 2001, pp. 137-152.
WWW Link. 1208

Granlund, G.H.[Goesta H.],
The complexity of vision,
SP(74), No. 1, 1 April 1999, pp. 101-126. BibRef 9904

Jain, R.,
Perception engineering,
MVA(1), No. 2, 1988, pp. 73-74. BibRef 8800

Bleicher, A.,
Eyes in the Sky That See Too Much,
Spectrum(47), No. 10, October 2010, pp. 16-16.
Update BibRef

Meer, P.[Peter],
Are we making real progress in computer vision today?,
IVC(30), No. 8, August 2012, pp. 472-473.
Elsevier DOI 1209
Opinion paper; Computer vision; Human vision BibRef

Yuille, A.L.,
Computer vision needs a core and foundations,
IVC(30), No. 8, August 2012, pp. 469-471.
Elsevier DOI 1209
Opinion paper; Core; Foundations BibRef

Huang, T.S.[Thomas S.],
Can the World-Wide Web Bridge the Semantic Gap?,
IVC(30), No. 8, August 2012, pp. 463-464.
Elsevier DOI 1209
Opinion paper; New frontiers in computer vision BibRef

Thenkabail, P.S.[Prasad S.],
Remote Sensing Open Access Journal: Increasing Impact through Quality Publications,
RS(6), No. 8, 2014, pp. 7463-7468.
DOI Link 1410

Thenkabail, P.S.[Prasad S.],
Remote Sensing Best Paper Award for the Year 2015,
RS(7), No. 5, 2015, pp. 5370-5372.
DOI Link 1506

Carter, E., Paragreen, J., Valfre`, G., Fletcher, D.,
Passenger acceptance of counter-terrorism security measures in stations,
IET-ITS(10), No. 1, 2016, pp. 2-9.
DOI Link 1602
security BibRef

Parhami, B.,
Low Acceptance Rates of Conference Papers Considered Harmful,
Computer(49), No. 4, April 2016, pp. 70-73.
Market research BibRef

Weber, A.[Andreas], Piesche, C.[Claudia],
Requirements on Long-Term Accessibility and Preservation of Research Results with Particular Regard to Their Provenance,
IJGI(5), No. 4, 2016, pp. 49.
DOI Link 1604

Sesartic, A.[Ana], Fischlin, A.[Andreas], Töwe, M.[Matthias],
Towards Narrowing the Curation Gap: Theoretical Considerations and Lessons Learned from Decades of Practice,
IJGI(5), No. 6, 2016, pp. 91.
DOI Link 1608
Issues of maintaining results of research. BibRef

Kang, K.C.[Kyu-Chang], Kwon, Y.J.[Yong-Jin], Moon, J.Y.[Jin-Young], Bae, C.[Changseok],
Challenging Issues in Visual Information Understanding Researches,
MMMod15(II: 458-469).
Springer DOI 1501

Miller, G.[Gregor], Fels, S.S.[Sidney S.], Oldridge, S.[Steve],
A Conceptual Structure for Computer Vision,
Describe computer vision in terms of descriptions of the problem, not the algorithm. BibRef

Oertel, C., Colder, B., Colombe, J.B., High, J., Ingram, M., Sallee, P.,
Current challenges in automating visual perception,

Koenderink, J.J.[Jan J.],
Something Old, Something New, Something Borrowed, Something Blue,
ECCV08(I: 1).
Springer DOI 0810
Invited talk. Discuss general computer vision from a 30+ year perspective. BibRef

Ishii, H.,
Tangible bits: designing the boundary between people, bits, and atoms,
ICPR02(III: 277-277).

Pinz, A.,
Consistent Visual Information Processing Applied to Object Recognition, Landmark Definition, and Real-Time Tracking,
PDF File. 0209

Geman, S.,
Hierarchy in Machine and Natural Vision,
SCIA99(Invited Talk). BibRef 9900

Bennett, B.M., Hoffman, D.D., Prakash, C.,
Perception and Computation,
ICCV87(356-364). BibRef 8700

Stockman, G.C., and Bowyer, K.W.,
Education for Computer Vision: Panel,
CVPR96(TA2). The results will become available. BibRef 9600

Horn, B.K.P.,
Towards a Science of Image Understanding,
IJCAI77(648). BibRef 7700

Chapter on Books, Collections, Overviews, General, and Surveys continues in
Computer Vision Surveys .

Last update:Mar 13, 2017 at 16:25:24