Image Processing, 304-529A
Department of Electrical Engineering
September, 2002
Image Processing, 304-529A
Department of Electrical Engineering
September, 2002
CATALOGUE OF CURRENT PERIODICAL SUBSCRIPTIONS IN THE MONTREAL ENGINEERING UNIVERSITY LIBRARIES: http://www.law.library.mcgill.ca/mtlcat/mtlcaten.htm
Topic Source McGill Student ID 1. - Adaptive method - Useful for medical imagery
Adaptive Histogram Equalization Paranjape, R. B., Morrow, W. M., Rangayyan, Adaptive-Neighborhood Histogram Equalization for Image Enhancement, CVGIP: Graphical Models and Image Processing, Vol. 54, No. 3, May 1992, pp. 259-267.
Dale-Jones, R., Tjahjadi, T., A Study and Modification of the Local Histogram Equalization Algorithm, Pattern Recognition, vo.26, No.9, 1993, pp. 1373-1381.
110245796 2. -Foreground tracking -Compensates for illumination variations
-Surveillance
Background Maintenance Kentaro Toyama, John Krumm, Barry Brumitt, Brian Meyers, Wallflower: Principles and Practice of Background Maintenance, Proceedings of the International Conference on Computer Vision, 20 - 25 September, 1999,Corfu, Greece.
3. -Surveillance --Tracking
-Initialization problem
Background Maintenance D. Gutchess, M. Trajkovic, E. Cohn-Solal, D. Lyons, and A.K. Jain, A background model initialization algorithm, International Conference on Computer Vision, ICCV 2001, vol. 1, pp. 733-740.
110249282 4. -Background maintenance -Copes with ambient illumination changes such as shadows and highlights
Background Subtraction for Foreground Detection T. Horprasert, D. Harwood, and L.S. Davis, A Robust Background Subtraction and Shadow Detection, Proc. Fourth Asian Conf. on Computer Vision, Taipei, Taiwan, Jan. 2000.
110247856 5. -Used for stereo matching and motion estimation Block matching Y.-S. Chen, Y.-P. Hung, C.-S, Fuh, A Fast Block Matching Algorithm Based on the Winner-Update Strategy, Proc. Fourth Asian Conf. on Computer Vision, Taipei, Taiwan, Jan. 2000, Vol. 2, pp.977-982.
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6. -Deformable models -Fourier descriptors
-Uses a priori global shape information
Boundary Finding Staib, LH., Duncan, J.S., Boundary Finding With Parametrically Deformable Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-14, No.11, November. 1992, pp.1061-1075.
110246033 7. -Lens distortion -Optimization method
Camera Calibration J. Weng, P. Cohen, and M. Herniou, Camera Calibration with Distortion Models and Accuracy Evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-14, No. 10, October. 1992, pp.965-980.
8. -Image indexing and comparison -Content-based image retrieval
Color Analysis J. Huang, S. R. Kumar, M. Mitra, W.-J. Zhu, R. Zabih, Multichannel Image Indexing Using Color Correlograms, 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.762-768.
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9. -Color image enhancement -Retinex model
-See also Website of Brian Funt at Simon Fraser
Color Image Enhancement Jobson, D. J., Rahman, Zia-ur, Woodell, G. A., A Multiscale Retinex for Bridging the Gap Between Color Images and the Human Observation of Scenes, IEEE Transactions on Image Processing, Vol. 6, No.3, July 1997, pp 965-976.
10. -Color image enhancement -Model based on human vision
Color Image Enhancement Wolf, S.G., Ginosar, R, Zeevi, Y., Spatio-Chromatic Image Enhacement Based on a Model of Human Visual Information Processing, Nov. 13, 1997. http://www.ee.technion.ac.il/people/members.html
11. -Color constancy - Deals with specularity and illumination direction
Color Object Recognition Daniel Berwick & Sang Wook Lee, A Chromaticity Space For Specularity-, Illumination Color- And Illumination Pose-Invariant 3-D Object Recognition, Proc. International Conference on Computer Vision, 1998, pp. 165-170
12. -Color Images -Searching Image Databases
-Color constancy
Color Object Recognition Funt, B.V., Finlayson, G.D., Color Constant Color Indexing, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-17, No.5, May. 1995, pp.522-529.
13. -Color Images -Searching Image Databases
-Ilumination-invariance
Color Object Recognition D. Slater, G. Healey, The Illumination-Invariant Matching of Deterministic Local Structure in Color Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-19, No.10, October. 1997, pp.1146-1151.
14. - Finding scene changes in videos - First test phase correlation of the feature finding problem.
Cut Detection Theodore Vlachos, Cut Detection in Video Sequences Using Phase Correlation, IEEE Signal Processing Letters, Vol. 7, No. 7, July 2000, pp. 173-175.
118810051 15. - Biological Model - Dynamic Images
- Uses micro saccades
Edge Detection Prokopowicz, P.N., Cooper P.R., The Dynamic Retina: Contrast and Motion for Active Vision, International Journal for Computer Vision, November, 1995.
16. - Eye detection - Face recognition
- Eye variance filter.
Eye Finder Feng, G.C, Yuen, P.C., Multi-Cue(s) Eye Detection On Gray Intensity Image, Pattern Recognition, Vol. 34, No. 5, May 2001, pp. 1033-1046.
119923391 17. - Medium - Robot eye using biology
Face Detection Bernhard Fröba and Christian Küblbeck, "Orientation Template Matching for Face Localization in Complex Visual Scenes", International Conference on Image Processing ICIP2000, 2000 , pp.251-254.
18. - Uses skin color - Illumination
compensation- practical approach
Face Detection Rein-Lien Hsu, Mohamed Abdel, and Anil K. Jain, Face Detection in Color Images, Michigan State University, Technical Report, MSU-CSE-01-7. March, 2001
19. - Uses skin color - CIE Lab color model
Face Detection J. Cai and A. Goshtasby, Detecting Faces in Color Images, Image and Vision Computing, Vol. 18, 1999, pp.63-75.
110028793 20. - Create new faces from inages using morphing - Well-explained on web site
- Add line through mouth and centerline to provide more triangles
Face Morphing http://www.inf.uszeged.hu/~ssip/2001/projects/files/project17/projekt17.ppt
http://www.stanford.edu/~jacobliu/368Report/ee368_finalReport.html
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21. - Simple holistic facial code - Face recognition
- Compute code using Wavelet Transform
- Experiment with different components of the transform
Face Recognition T. Sim, R. Sukthankar, M. Mullin and S. Baluja, Memory-Based Face Recognition For Visitor Identification, Proc. 4th Intl. Conf. on Face and Gesture Recognition, Grenoble, France, March 2000, pp. 214-220.
119831670 22. - Fourier transform - Wavelet transform
- Spectroface
- Invariant to translation, scale and in-plane rotation
Face Recognition Lai J.H., Yuen P. C and Feng G.C., Face Recognition Using Holistic Fourier Invariant Features, Pattern Recognition, Vol. 34, No.1, 2001, pp. 95-109.
23. - Uses a midrange subband as a
biometric template- Test using other subbands
Face Recognition G. C. Feng, P. C. Yuen and D. Q. Dai, Human Face Recognition Using PCA on Wavelet Subband, Journal of Electronic Imaging, Vol. 9, No.2 pp.226-233, 2000.
http://imaging.comp.glam.ac.uk/scripts/constructor.exe?request=publication (pdf persion)
110045669 24. - Feature histograms are used for face comparisons - local features: surface shape index, gradient angle
- Compare with using only one of the features
Face Recognition S. Ravela and Allen Hanson, On Multi-Scale Differential Features for Face Recognition, Vision Interface, Ottawa, June 2001
http://ciir.cs.umass.edu/~ravela/pubs.html (pdf version)
110248651 25. - Fast algorithm for face tracking - Based on histograms
- Uses kalman filter for prediction
Face Tracking R.J. Qian, M.I. Sezan, and K.E. Matthews. A Robust Real-Time Face Tracking Algorithm, Int. Conf. on Image Processing, Vol. 1, pp. 131--135, Chicago, Illinois, October 4-7 1998.
0146130 26. - Statistical facial asymmetry under expression variations - Provides discriminating power orthogonal to conventional face identification methods.
Facial Biometric Y. Liu, R.L. Weaver, K. Schmidt, N. Serban, and J. Cohn, Facial Asymmetry: A New Biometric, Tech. Report CMU-RI-TR-01-23, Robotics Institute, Carnegie Mellon University, August, 2001.
27. - Fusing multiple exposure photographs - High dynamic range
High Dynamic Range Images Paul E. Debevec and Jitendra Malik. Recovering High Dynamic Range Radiance Maps from Photographs, Proceedings of ACM SIGGRAPH 97, August 1997, pp. 369-378.http:graphics3.isi.edu/~debevec/Research/HDR/#publication//
(Do not use the downloadable software package except for comparing your results.)
28. - Old problem using new approach Histogramming Brunelli, R., Optimal Histogram Partitioning Using a Simulated Annealing Technique, Pattern Recognition Letters, Vol. 13, No. 8, Aug. 1992, p. 581-586.
29. - Medium - Hardware implications
Hough Pyramids Neveu, C. F., Dyer, C. R., Chin, R. T., Object Recognition Using Hough Pyramids, Proc. IEEE Computer Society Conference Computer Vision and Pattern Recognition, San Francisco, CA., June 19-23, 1985, pp. 328-333.
30. -Important topic - Powerful method
Hough Transform Xu, L., Oja, E., Randomized Hough Transform(RHT): Basic Mechanisms, Algorithms, Computational Complexities, CVGIP: Image Understanding, Vol. 57, No. 2,March 1993, pp. 131-154
31. - Version of Land's retinex model for human vision - lightness and color constancy
Human Color Model Daniel J. Jobson, Zia-Ur Rahman, And Glenn A. Woodell, Properties And Performance Of A Center/Surround Retinex, IEEE Transactions On Image Processing, Vol. 6, No. 3, March 1997, pp. 451-462.
119921180 32. - Face viewed under different lighting conditions - New matching technique based on a linear model
Illumination Invariance Finlayson, G. D., Dueck, J., Funt, B.V., Drew, M.S., 'Colour Eigenfaces,' Proc. Third International Workshop on Image and Signal Processing Advances in Computational Intelligence, November 4-7, 1996, Manchester, UK.
33. - Color invariance - Compression
- Wavelets
- Indexing
Ilumination Invariance Mark S. Drew, Jie Wei, and Ze-Nian Li, Illumination-Invariant Image Retrieval and Video Segmentation, Pattern Recognition Vol. 32, 1999, pp.1369-1388.
34. -Illuminant invariance - Database indexing
Image Database Indexing J. Berens, and G.D. Finlayson, Log-Opponent Chromaticity Coding Of Colour Space, 15th Int. Conf. Pattern Recognition, , Barcelona, Spain, Vol. 1, Computer Vision and Image Analysis,1, 2000, pp.206-211.
35. - Contrast enhancement -Applicable to medical imaging
Image Enhancement Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., ter Har Romeny, B., Zimmerman, J.B., Zuiderveld, K., Adaptive Histogram Equalization and Its Variations, Computer Vision, Graphics and Image Processing, Vol. 39, No. 3, September. 1987, pp. 355-368.
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36. - Interesting model of human vision Image Enhancement Reese, G., Image Enhancement by Intensity-Dependent Spread Function, CVGIP: Graphical Models and Image Processing, Vol. 54, No. 1, Jan 1992, pp. 45-55.
Yellott, J. I., Photon Noise and Constant-Volume Operators, J. Opt. Soc. Amer. A., Vol. 4, No. 12, Dec. 1987, pp. 2418-2446.
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37. - Matching - Invariance to image plane variations
Image Matching A. Shashua, Anat Levin, and Shai Avidan. Manifold Pursuit: A New Approach to Appearance Based Recognition. ICPR, 2002.
The paper is available at following sites:
http://www.cs.huji.ac.il/~shashua/papers/manifold-icpr02.pdf
http://www.cs.huji.ac.il/~alevin/papers/manifoldPursuit.ps110247881 38. - Distinguishing images - Color blob histograms
Varying spatial scales
Image Retrieval Richard J. Qian, Peter J. L. Van Beek, and M. Ibrahim Sezan, Image Retrieval Using Blob Histograms, ICME-2000, IEEE International Conference on Multimedia and Expo, July 30 - August 2, 2000 New York City, NY, pp. 125-128.
0148175 39. -Adaptive Equalization Local Histogram Equalization H. Zhu, F. H. Y. Chan and F. K. Lam, Image Contrast Enhancement by Constarined Local Histogram Equalization, Computer Vision and Image Understanding. Vol. 73, No. 2, pp. 281-290, 1992 Feb.
119825114 40. - Motion tracking
- Uses background maintenanceMotion Tracking Chris Stauffer and W. Eric L. Grimson, Learning Patterns Of Activity Using Real-Time Tracking (only Sections 1-5), IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-22, No.8, August 2000, pp.747-757.
119829847 41. - Very fast method
- Apply to face detection
- Uses very simple featuresObject Detection P. Viola, M. Jones, Robust Real-Time Object Detection, Second International Workshop on Statistical and Computational Theories of Vision - Modeling, Learning, Computing and Sampling , Vancouver, Canada, July 13, 2001.
42. - Current - Interesting
- Easy
Object Recognition Using Color Swain, M.-J., Ballard, D. H., Color Indexing, International Journal of Computer Vision, Vol. 7, No. 1, 1991, pp. 11-32.
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43. - Inspection applications -Surface reflectance
-Test with both natural and manmade objects
Photometric Invariance Nayer, S. K., Bolle, R. M., Computing Reflectance Ratios From an Image, Pattern Recognition, Vol. 26, No. 10, October, 1993, pp. 1529-1542.
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44. -Video indexing -Video Shot detection
-Scene cut
Scene Cut Detection J. Yu & M. D. Srinath, An Efficient Method for Scene Cut Detection, Pattern Recognition Letters, Vol. 22, 2001, pp. 1379-1391.
119831558 45. - Difficult Shape Description Eklundh, J.-O., Howako, J., Robot Shape Description Based on Curve Fitting, 7th International Conference on Pattern Recognition, Montreal, Québec, July 30 - Aug. 2, 1984, pp. 109-112.
46. - Thinning - Constrained Delaunay triangulation
- Binary image processing
Shape Skeletonization J. J. Zou, H. H. Chang & H. Yan, Shape Skeletonization by Identifying Discrete Local symmetries, Pattern recognition, Vol. 34, 2001, pp. 1895-1905.
119827373 47. - Parametric models - Piecewise linear model to represent regions in disparity map
Stereo Correspondence A. S. Aguado & E. Montiel, Progressive Linear Search for Stereo Matching its Application to Interframe Interpolation, Computer Vision and Image Understanding, Vol. 81, No. 1, pp. 46-71, Jan. 2001.
48. - Hierarchical templates - Fast
Template Matching D. Keren, M. Osadchy, C. Gotsman, Antifaces: A Novel, fast Method for Image Detection, IEEE TRansactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-23, No.7, July. 2001, pp. 747-761.
110246212 49. - Texture - Medium
Texture Analysis Parkkinen, J., Oja, E., Co-occurrence Matrices and Subspace Methods in Texture Analysis, Proc. Eighth International Conference on Pattern Recognition, Paris, France, Oct. 27-31, 1986, pp. 405-408.
50. -Multichannel model -Gabor functions
-segmentation
Texture Analysis A. C. Bovik, M. Clark, W. S. Geisler, Multichannel Texture Analysis Using Localized Spatial Filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-129, No.1, January. 1990, pp.55-73.
51. - Biological model of visual perception - Segmenatation of natural scenes
Texture Segmentation Jain, A. Farrokhnia, F., Unsupervised Texture Segmentation Using Gabor Filters, Pattern Recognition, Vol. 24, 1991, pp. 1167-1186.
52. - Achieves illumination invariance using
homomorphic filtering
- Use downloaded video sequences to test the algorithmTracking Moving Objects D. Toth, T. Aach, and V. Metzler, Illumination-Invariant Change Detection, 4th IEEE Southwest Symposium on Image Analysis and Interpretation, Austin, TX, April 2-4 2000, pp.3-7.
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