Reference Projects

Image Processing, ECSE-529
Department of Electrical Engineering
September, 2005

SELECT A PROJECT BY SEPTEMBER 30, 2005

No. Topic Source McGill Student ID

1

- Finding object contours

- Contour curvature extimation

- Fast heuristic method

- Use COIL object database

Active Contours

D. Williams & M. Shah. A Fast Algorithm for Active Contours and Curvature Estimation, Computer Vision, Graphics and Image Processing, Vol 55, No.1, January 1992, pp 14-26.

260152625

2
- 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.

260098610

3
Text detection

- Content based Indexing

- SVM
Automatic character detection

Datong Chen, Jean-Marc Odobez, Jean-Philippe Thiran, A localization/verification scheme for finding text in images and video frames based on contrast independent features and machines learning methods, Signal Processing: Image Communication, Vol. 19, 2004, pp 205-217

110227966

4

-Advanced method

-How do we deal with temporarily static objects?

-What happens if a surveillance camera is suddenly switched to view a different perspective?

Background Estimation

M. Pic, L. Berthouze, T. Kurita, Adaptive Background Estimation, Proc. MVA 2002, IAPR Workshop on Machine Vision Applications, Dec. 11-13, 2002, Nara-ken New Public Hall, Nara, Japan, pp. 451-454

Copy of paper: pdf,gzipped ps.
Another paper availabe at: http://staff.aist.go.jp/luc.berthouze/papers/abstract.html#ieice03

5
- Many Moving Objects

- Video-Object Segmentation

-Background/Foreground Analysis
Background Estimation

Farin, D. de With, P.H.N. Effelsberg, W., Robust Background Estimation for Complex Video Sequences Internation Conference on Image Processing, Sep 2003, pp 145-148

6
-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.

7
-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.

110037000

8
-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.

110036234

9
-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.

110033624

10
-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.

260143816

11
-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.

12

- One-dimensional color constancy

- Function of RGB

- Depends solely on surface reflectance

Color Constancy

G. D. Finlayson, S. D. Hordley, Color Constancy at a Pixel, Journal of the Optical Society A, Vol.18, No. 2, February 2001, pp253-264.

13

- Computational color constancy

- Illuminant estimation

- Specularities

- Physics-based vision

Color Constancy

G. D. Finlayson, G. Shaefer, Solving for Color Constancy using a Constrained Dichromatic Reflection Model, International Journal of Computer Vision, Vol. 42, No. 3, 2001, pp127-124

14
-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.

15
-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

110023631

16
-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

17
-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.

18
-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.

19
- 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.

-Tone reproduction

-Dynamic Range Reduction

-Photoreceptor Physiology

Dynamic Range Reduction

E. Reinhard & K. Devlin, Dynamic Range Reduction Inspired by Photoreceptor Physiology, IEEE Transactions on Visualization and Computer Graphics, Vol. 11, No. 1, January/February 2005, pp. 13-24.

20
- Popular Method
- Shown to be very accurate
- Compare with Sobel and Canny
Edge Detection

C. Harris and M. Stephens, A Combined corner and edge detector, Proceedings Fourth Alvey Vision Conference, pp 147-151, 1988

119927835

21

- Data hiding

- Human vision sensitivity

Embedding Secret Messages

D.-C. Wu, W.-H. Tsai, A Steganographic for Image by Pixel-Value Differencing, Pattern Recognition Letters, Vol. 24, 2003, pp1613-1626

110136219

22
- 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.

http://www.dcs.ex.ac.uk/people/wangjunl/up88.pdf

110126988

23
- 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.

24
- 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

110135771

25
- 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.

26
- Simple holistic facial code
- Face recognition

- Compute code using the Daubechies Wavelet Transform

- Experiment with different components of the transform

-Decompose the transform into several levels

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.

http://citeseer.nj.nec.com/sim00memorybased.html

110136975

27

-Face detection

-Head pose classification

Face Pose Classification

C. Lin, K.-C. Fan, Pose Classification of Human Faces by weighting mask function Approach (sic), Pattern Recognition Letters, Vol. 24, 2003, pp1857-11869

28
- 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.

260128753

29
- 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 version)

30
- 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)

31

- Find skin regions in poor lighting conditions and different illuminations

- Uses an eigenmask to verify the face

Face recognition

K.-W. Wong, K.-M. Lam, W.-C. Siu, A Robust Scheme for Live Detection of Human Faces in Live Images, Signal Processing: Image Communication, Vol. 18, 2003, pp103-114

32
- Parts-Based Representation

- Face Recognition
Face representation and recognition

Stan Z. Li, Xin Wen Hou, HongJiang Zhang, QianSheng Cheng, Learning Spatially Localized, Parts-Based Representation

33
- 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.

34
- 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.

http://www.ri.cmu.edu/pubs/pub_3771.html

35
- 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.)

36
- 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.

260141800

37
- 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.

38
-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

39
- 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.

40
- 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.

41
- 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.

42
-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.

110234271

43
- Locally adaptive
- Uses human JND curve
- Medical and other applications

Image Enhancement
T.-L. Ji, M. K. Sundareshan, and H. RoehrigJi, Adaptive Image Contrast Enhancement Based on Human Visual Properties, IEEE Transactions in Medical Imaging Vol. 13, No. 4, Dec. 1994 pp 573-586

260160670

44
- 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.

45

- Gray-scale Image comparison

- Similarity measure

- based on distance transform

Image Matching

D. Coquin & Ph. Bolon, A New Method for Computing the Distortion Vector Field From Two Images, Proceeding International Conference on Pattern Recognition, August 2002, Quebec City. (Available on IEEExplore.)

D. Coquin & Ph. Bolon, Application of Baddeley’s Distance to Dissimilarity Measurement Between Gray Scale images, Pattern recognition Letters, Vol. 22, 2003, pp. 1483-1502.

119434994

46
- 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.

110137216

47

-Matching images

-Correlation filters

-Face verification

Matching Faces

M. Savvides, B.V.K. Vijaya Kumar, Efficient Design of Advanced Correlation Filters for Robust Distortion-Tolerant Face Identification, IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) 2003.

B.V.K. Vijaya Kumar, M. Savvides, K. Venkataramani, C. Xie, Spatial frequency domain image processing for biometric recognition, Proc. of Intl. Conf. on Image Processing (ICIP), Vol. I, 53-56, 2002

48

-Non-negative matrix factorization

-Sparse coding

-Apply to face recognition using an existing database

for which results already exist for other methods.

Modeling Receptive Fields

P. O. Hoyer. Non-negative sparse coding. Neural Networks for Signal Processing XII, Martigny, Switzerland, 2002.

P. O. Hoyer. Modeling receptive fields with non-negative sparse coding. Computational Neuroscience: Trends in Research 2003 (Proceedings of CNS*2002)

Software available: http://www.cns.nyu.edu/~phoyer/code/

49
- Don't build Camera
- Sharpening an image
- Inverse Filtering
- Power Spread Function
Motion Deblurring

Nayar, S.K., Ben-ezra, M., Motion-based motion deblurring, IEEE transaction on Pattern Analysis and Machine Intelligence, Vol.26, No.6, June 2004 pp689-698

119921175

50
- Motion tracking

- Uses background maintenance
Motion 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.

51
- 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.

110047407

52
- 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.

53
-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.

54

-Anisotropic diffusion

-Color represented in the complex plane

Segmentation

L. Luchese & S. K. Mitra, Color Segmentation Through Independent Anisotropic Diffusion of Complex Chromiticity and Lightness.

Paper available at: http://vision.ece.ucsb.edu/segmentation/color.html
Related Publications: http://eecs.oregonstate.edu/research/members/lucchese/pubs.html

55

- Shading, color

- Illuminant invariance

- Requires a calibrated camera; simple method siggested.

Shadow Removal

G. D. Finlayson, S. D. Hordley, M. S. Drew, Removing Shadows from Images, Proc. ECCV 2002, 7th European Conference on Computer Vision, Copenhagen, Denmark, May 28-31, 2002, Part IV, Springer, Lecture Notes in Computer Science,2353, pp 823-836.

56
- Eye Detection

- Face Detection
Focus-of-Attention from Local Color Symmetries, IEEE transaction on Pattern Analysis and Machine Intelligence Vol.26, No. 7, July 2004, pp 817-830

260120126

57
- 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.

58
- 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.

59

- Method for creating plausible high frequency details in zoomed images

- Training-based super-resolution algorithm

Super-Resolution

W. T. Freeman, t. R. Jones, E. C. Pasztor, Example-Based Super-Resolution, IEEE Computer Graphics and Applications, March/April 2002, pp56-65

110227622

60
-Text Localization

-Text Segmentation

-Text Recognition
Text Detection And Recognition

Datong Chen, Jean-Marc Odobez, Herve Bourlard, Text detection and recognition in images and video frames, Pattern Recognition, Vol. 37, No. 3, March 2004, pp 595-609

119830075

61
- Texture

- Connected Components

- Multi-Layer Perceptron
Text Localization

Keechul Jung and JungHyun Han, Hybrid Approach to efficient text extraction in complex color images, Pattern Recognition Letters, Vol.25, No.6, April 2004, pp 679-699

62
-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.

260159716

63
- 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.

64

- Threshold selection (YATA)

-Image segmentation

-Clustering Algorithm

Thresholding

L. Wang, J. Bai Tsai, Threshold Selection by Clustering Gray Levels of Boundary(sic), Pattern Recognition Letters, Vol. 24, 2003, pp1983-1099

65
- Achieves illumination invariance using
homomorphic filtering
- Use downloaded video sequences to test the algorithm
Tracking 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.

No.	Topic	Source	McGill Student ID
1	- Finding object contours - Contour curvature extimation - Fast heuristic method - Use COIL object database	Active Contours D. Williams & M. Shah. A Fast Algorithm for Active Contours and Curvature Estimation, Computer Vision, Graphics and Image Processing, Vol 55, No.1, January 1992, pp 14-26.	260152625
2	- 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.	260098610
3	Text detection - Content based Indexing - SVM	Automatic character detection Datong Chen, Jean-Marc Odobez, Jean-Philippe Thiran, A localization/verification scheme for finding text in images and video frames based on contrast independent features and machines learning methods, Signal Processing: Image Communication, Vol. 19, 2004, pp 205-217	110227966
4	-Advanced method -How do we deal with temporarily static objects? -What happens if a surveillance camera is suddenly switched to view a different perspective?	Background Estimation M. Pic, L. Berthouze, T. Kurita, Adaptive Background Estimation, Proc. MVA 2002, IAPR Workshop on Machine Vision Applications, Dec. 11-13, 2002, Nara-ken New Public Hall, Nara, Japan, pp. 451-454 Copy of paper: pdf,gzipped ps. Another paper availabe at: http://staff.aist.go.jp/luc.berthouze/papers/abstract.html#ieice03
5	- Many Moving Objects - Video-Object Segmentation -Background/Foreground Analysis	Background Estimation Farin, D. de With, P.H.N. Effelsberg, W., Robust Background Estimation for Complex Video Sequences Internation Conference on Image Processing, Sep 2003, pp 145-148
6	-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.
7	-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.	110037000
8	-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.	110036234
9	-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.	110033624
10	-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.	260143816
11	-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.
12	- One-dimensional color constancy - Function of RGB - Depends solely on surface reflectance	Color Constancy G. D. Finlayson, S. D. Hordley, Color Constancy at a Pixel, Journal of the Optical Society A, Vol.18, No. 2, February 2001, pp253-264.
13	- Computational color constancy - Illuminant estimation - Specularities - Physics-based vision	Color Constancy G. D. Finlayson, G. Shaefer, Solving for Color Constancy using a Constrained Dichromatic Reflection Model, International Journal of Computer Vision, Vol. 42, No. 3, 2001, pp127-124
14	-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.
15	-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	110023631
16	-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
17	-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.
18	-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.
19	- 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.
	-Tone reproduction -Dynamic Range Reduction -Photoreceptor Physiology	Dynamic Range Reduction E. Reinhard & K. Devlin, Dynamic Range Reduction Inspired by Photoreceptor Physiology, IEEE Transactions on Visualization and Computer Graphics, Vol. 11, No. 1, January/February 2005, pp. 13-24.
20	- Popular Method - Shown to be very accurate - Compare with Sobel and Canny	Edge Detection C. Harris and M. Stephens, A Combined corner and edge detector, Proceedings Fourth Alvey Vision Conference, pp 147-151, 1988	119927835
21	- Data hiding - Human vision sensitivity	Embedding Secret Messages D.-C. Wu, W.-H. Tsai, A Steganographic for Image by Pixel-Value Differencing, Pattern Recognition Letters, Vol. 24, 2003, pp1613-1626	110136219
22	- 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. http://www.dcs.ex.ac.uk/people/wangjunl/up88.pdf	110126988
23	- 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.
24	- 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	110135771
25	- 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.
26	- Simple holistic facial code - Face recognition - Compute code using the Daubechies Wavelet Transform - Experiment with different components of the transform -Decompose the transform into several levels	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. http://citeseer.nj.nec.com/sim00memorybased.html	110136975
27	-Face detection -Head pose classification	Face Pose Classification C. Lin, K.-C. Fan, Pose Classification of Human Faces by weighting mask function Approach (sic), Pattern Recognition Letters, Vol. 24, 2003, pp1857-11869
28	- 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.	260128753
29	- 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 version)
30	- 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)
31	- Find skin regions in poor lighting conditions and different illuminations - Uses an eigenmask to verify the face	Face recognition K.-W. Wong, K.-M. Lam, W.-C. Siu, A Robust Scheme for Live Detection of Human Faces in Live Images, Signal Processing: Image Communication, Vol. 18, 2003, pp103-114
32	- Parts-Based Representation - Face Recognition	Face representation and recognition Stan Z. Li, Xin Wen Hou, HongJiang Zhang, QianSheng Cheng, Learning Spatially Localized, Parts-Based Representation
33	- 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.
34	- 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. http://www.ri.cmu.edu/pubs/pub_3771.html
35	- 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.)
36	- 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.	260141800
37	- 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.
38	-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
39	- 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.
40	- 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.
41	- 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.
42	-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.	110234271
43	- Locally adaptive - Uses human JND curve - Medical and other applications	Image Enhancement T.-L. Ji, M. K. Sundareshan, and H. RoehrigJi, Adaptive Image Contrast Enhancement Based on Human Visual Properties, IEEE Transactions in Medical Imaging Vol. 13, No. 4, Dec. 1994 pp 573-586	260160670
44	- 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.
45	- Gray-scale Image comparison - Similarity measure - based on distance transform	Image Matching D. Coquin & Ph. Bolon, A New Method for Computing the Distortion Vector Field From Two Images, Proceeding International Conference on Pattern Recognition, August 2002, Quebec City. (Available on IEEExplore.) D. Coquin & Ph. Bolon, Application of Baddeley’s Distance to Dissimilarity Measurement Between Gray Scale images, Pattern recognition Letters, Vol. 22, 2003, pp. 1483-1502.	119434994
46	- 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.	110137216
47	-Matching images -Correlation filters -Face verification	Matching Faces M. Savvides, B.V.K. Vijaya Kumar, Efficient Design of Advanced Correlation Filters for Robust Distortion-Tolerant Face Identification, IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) 2003. B.V.K. Vijaya Kumar, M. Savvides, K. Venkataramani, C. Xie, Spatial frequency domain image processing for biometric recognition, Proc. of Intl. Conf. on Image Processing (ICIP), Vol. I, 53-56, 2002
48	-Non-negative matrix factorization -Sparse coding -Apply to face recognition using an existing database for which results already exist for other methods.	Modeling Receptive Fields P. O. Hoyer. Non-negative sparse coding. Neural Networks for Signal Processing XII, Martigny, Switzerland, 2002. P. O. Hoyer. Modeling receptive fields with non-negative sparse coding. Computational Neuroscience: Trends in Research 2003 (Proceedings of CNS*2002) Software available: http://www.cns.nyu.edu/~phoyer/code/
49	- Don't build Camera - Sharpening an image - Inverse Filtering - Power Spread Function	Motion Deblurring Nayar, S.K., Ben-ezra, M., Motion-based motion deblurring, IEEE transaction on Pattern Analysis and Machine Intelligence, Vol.26, No.6, June 2004 pp689-698	119921175
50	- Motion tracking - Uses background maintenance	Motion 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.
51	- 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.	110047407
52	- 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.
53	-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.
54	-Anisotropic diffusion -Color represented in the complex plane	Segmentation L. Luchese & S. K. Mitra, Color Segmentation Through Independent Anisotropic Diffusion of Complex Chromiticity and Lightness. Paper available at: http://vision.ece.ucsb.edu/segmentation/color.html Related Publications: http://eecs.oregonstate.edu/research/members/lucchese/pubs.html
55	- Shading, color - Illuminant invariance - Requires a calibrated camera; simple method siggested.	Shadow Removal G. D. Finlayson, S. D. Hordley, M. S. Drew, Removing Shadows from Images, Proc. ECCV 2002, 7th European Conference on Computer Vision, Copenhagen, Denmark, May 28-31, 2002, Part IV, Springer, Lecture Notes in Computer Science,2353, pp 823-836.
56	- Eye Detection - Face Detection	Focus-of-Attention from Local Color Symmetries, IEEE transaction on Pattern Analysis and Machine Intelligence Vol.26, No. 7, July 2004, pp 817-830	260120126
57	- 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.
58	- 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.
59	- Method for creating plausible high frequency details in zoomed images - Training-based super-resolution algorithm	Super-Resolution W. T. Freeman, t. R. Jones, E. C. Pasztor, Example-Based Super-Resolution, IEEE Computer Graphics and Applications, March/April 2002, pp56-65	110227622
60	-Text Localization -Text Segmentation -Text Recognition	Text Detection And Recognition Datong Chen, Jean-Marc Odobez, Herve Bourlard, Text detection and recognition in images and video frames, Pattern Recognition, Vol. 37, No. 3, March 2004, pp 595-609	119830075
61	- Texture - Connected Components - Multi-Layer Perceptron	Text Localization Keechul Jung and JungHyun Han, Hybrid Approach to efficient text extraction in complex color images, Pattern Recognition Letters, Vol.25, No.6, April 2004, pp 679-699
62	-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.	260159716
63	- 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.
64	- Threshold selection (YATA) -Image segmentation -Clustering Algorithm	Thresholding L. Wang, J. Bai Tsai, Threshold Selection by Clustering Gray Levels of Boundary(sic), Pattern Recognition Letters, Vol. 24, 2003, pp1983-1099
65	- Achieves illumination invariance using homomorphic filtering - Use downloaded video sequences to test the algorithm	Tracking 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.