article

Are Edges Incomplete?

Author:

James H. ElderAuthors Info & Claims

International Journal of Computer Vision, Volume 34, Issue 2-3

Pages 97 - 122

https://doi.org/10.1023/A:1008183703117

Published: 01 October 1999 Publication History

Abstract

We address the problem of computing a general-purpose early visual representation that satisfies two criteria. 1) Explicitness: To be more useful than the original pixel array, the representation must take a significant step toward making important image structure explicit. 2) Completeness: To support a diverse set of high-level tasks, the representation must not discard information of potential perceptual relevance. The most prevalent representation in image processing and computer vision that satisfies the completeness criterion is the wavelet code. In this paper, we propose a very different code which represents the location of each edge and the magnitude and blur scale of the underlying intensity change. By making edge structure explicit, we argue that this representation better satisfies the first criterion than do wavelet codes. To address the second criterion, we study the question of how much visual information is lost in the representation. We report a novel method for inverting the edge code to reconstruct a perceptually accurate estimate of the original image, and thus demonstrate that the proposed representation embodies virtually all of the perceptually relevant information contained in a natural image. This result bears on recent claims that edge representations do not contain all of the information needed for higher level tasks.

References

[1]

Adelson, E. 1991. The plenoptic function and the elements of early vision. In Computational Models of Visual Processing, M. Landy and J. Movshon (Eds.), MIT Press: Cambridge, MA.

[2]

Adelson, E., Simoncelli, E., and Hingorani, R. 1987. Orthogonal pyramid transforms for image coding. In Proc. of SPIE, Vol. 845, Cambridge, MA, pp. 50-58.

[3]

Adini, Y., Moses, Y., and Ullman, S. 1997. Face recognition: the problem of compensating for illumination changes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7): 721-732.

Digital Library

[4]

Barlow, H. 1961. The coding of sensory messages. In Current Problems in Animal Behavior, W. Thorpe and O. Zangwill (Eds.), Cambridge U. Press, pp. 331-360.

[5]

Barrow, H. and Tenenbaum, J. 1981. Computational vision. Proc. IEEE, 69:572-595.

[6]

Barth, E., Caelli, T., and Zetzsche, C. 1993. Image encoding, labeling and reconstruction from differential geometry. CVGIP: Graphical Models and Image Processing, 55(6):428-446.

Digital Library

[7]

Belhumeur, P. and Kriegman, D. 1996. What is the set of images of an object under all possible lighting conditions? In Proc. IEEE Conf. Computer Vision Pattern Recognition, San Francisco. IEEE Computer Society, IEEE Computer Society Press, pp. 270-277.

Digital Library

[8]

Canny, J. 1983. Finding edges and lines in images. Master's thesis, MIT Artificial Intelligence Laboratory.

[9]

Carlsson, S. 1984. Sketch based image coding. In Proc. of Premier Colloque Image, Biarritz, France, pp. 71-77.

[10]

Carlsson, S. 1988. Sketch based coding of grey level images. Signal Processing, 15:57-83.

Digital Library

[11]

Cox, I., Boie, R., and Wallach, D. 1990. Line recognition. In Proc. Int. Conf. on Pattern Recognition, Atlantic City, NJ, pp. 639-645.

[12]

Cox, I., Rehg, J., and Hingorani, S. 1993. A Bayesian multiple-hypothesis approach to edge grouping and contour segmentation. Int. J. Comp. Vision, 11(1):5-24.

Digital Library

[13]

Cumani, A., Grattoni, P., and Guiducci, A. 1991. An Edge-Based Description of Color Images. CVGIP: Graphical Models and Image Processing, 53(4):313-323.

Digital Library

[14]

Curtis, S., Shitz, S., and Oppenheim, A. 1987. Reconstruction of nonperiodic two-dimensional signals from zero crossings. IEEE Trans. Acoust., Speech, Signal Processing, 35:890-893.

[15]

Daubechies, I. 1991. Ten lectures on wavelets. In CBMS-NSF Series Appl. Math., SIAM.

Digital Library

[16]

David, C. and Zucker, S. 1990. Potentials, valleys and dynamic global coverings. Int. J. Computer Vision, 5:219-238.

Digital Library

[17]

Deriche, R. 1987. Using Canny's criteria to derive a recursively implemented optimal edge detector. Int. J. Computer Vision, 1(2): 167-187.

[18]

Dron, L. 1977. The multiscale veto model: a two-stage analog network for edge detection and image reconstruction. Int. J. Comp. Vision, 56:487-510.

[19]

Elder, J. 1997. Brightness filling-in of natural images. European Conf. on Visual Perception, Helsinki, Finland, In Perception, 26(Suppl.) 57.

[20]

Elder, J., Beniaminov, D., and Pintilie, G. 1999. Edge classification in natural images. J. Invest. Opthalm. Visual Sci. 40(4):1897.

[21]

Elder, J. and Goldberg, R. 1998. Interactive contour editing. In Proc. IEEE Conf. Computer Vision Pattern Recognition, Santa Barbara, CA, IEEE Computer Society, IEEE Computer Society Press, pp. 374-381.

Digital Library

[22]

Elder, J., Trithart, S., Pintilie, G., and MacLean, D. 1998. Rapid processing of cast and attached shadows. J. Invest. Opthalm. Visual Sci., 39(4):S853.

[23]

Elder, J. and Zucker, S. 1995 The local character of generalized luminance transitions. J. Invest. Opthalm. Visual Sci., 36(4):S836.

[24]

Elder, J. and Zucker, S. 1998. Local scale control for edge detection and blur estimation. IEEE Pattern Anal. Machine Intell., 20(7): 699-716.

Digital Library

[25]

Elder, J. and Zucker, S. 1996a. Computing contour closure. In Proc 4th European Conf. on Computer Vision, Lecture Notes in Computer Science, Springer Verlag, New York, pp. 399-412.

Digital Library

[26]

Elder, J. and Zucker, S. 1996b. Local scale control for edge detection and blur estimation. In Proc. 4th European Conf. on Computer Vision, Lecture Notes in Computer Science, Springer Verlag, New York, pp. 57-69.

Digital Library

[27]

Elder, J. and Zucker, S. 1996c. Scale space localization, blur and contour-based image coding. In Proc. IEEE Conf. Computer Vision Pattern Recognition, San Francisco. IEEE Computer Society, IEEE Computer Society Press, pp. 27-34.

Digital Library

[28]

Ens, J. and Lawrence, P. 1993. Investigation of methods for determining depth from focus. IEEE Trans. Pattern Anal. Machine Intell., 15(2):97-108.

Digital Library

[29]

Fischler, M., Tenenbaum, J., and Wolf, H. 1981. Detection of roads and linear structures in low-resolution aerial imagery using a multisource knowledge integration technique. Computer Graphics and Image Processing, 18(4):201-223.

[30]

Freeman, W. and Adelson, E. 1991. The design and use of steerable filters. IEEE Trans. Pattern Anal. Machine Intell., 13(9):891-906.

Digital Library

[31]

Grattoni, P. and Guiducci, A. 1990. Contour Coding for Image Description. Pattern Recognition Letters, 11:95-105.

Digital Library

[32]

Hummel, R. and Moniot, R. 1989. Reconstructions from zero crossings in scale space. IEEE Trans. on Acoustics, Speech, and Signal Processing, 37(12):2111-2130.

[33]

Iverson, L. and Zucker, S. 1995. Logical/linear operators for image curves. IEEE Trans. Pattern Anal. Machine Intell., 17(10):982- 996.

Digital Library

[34]

Kersten, D., Mamassian, P., and Knill, D. 1997. Moving cast shadows induce apparent motion in depth. Perception, 26(2):171-192.

[35]

Koenderink, J. 1984. The structure of images. Biol. Cybern., 50: 363-370.

[36]

Leclerc, Y. and Zucker, S. 1987. The local structure of image discontinuities in one dimension. IEEE Trans. Pattern Anal. Machine Intell., 9(3):341-355.

Digital Library

[37]

Lindeberg, T. 1990. Scale-space for discrete signals. IEEE Trans. Pattern Anal. Machine Intell., 12(3):234-254.

Digital Library

[38]

Lindeberg, T. 1996. Edge detection and ridge detection with automatic scale selection. In IEEE Conf. Computer Vision Pattern Recognition, San Francisco, IEEE Computer Society, IEEE Computer Society Press, pp. 465-470.

Digital Library

[39]

Logan, B.F. 1977. Information in the zero-crossings of bandpass signals. Bell Syst. Tech. J., 56:487-510.

[40]

Mallat, S. 1989. Multifrequency channel decompositions of images and wavelet models. IEEE Trans. Inform. Theory, 37(12):2091-2110.

[41]

Mallat, S. and Zhong, S. 1992. Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal. Machine Intell., 14: 710-732.

Digital Library

[42]

Marr, D. 1982. Vision. W.H. Freeman:New York.

[43]

Marr, D. and Hildreth, E. 1980. Theory of edge detection. Proc. R. Soc. Lond. B, 207:187-217.

[44]

Nayar, S. and Yasuo, N. 1994. Shape from focus. IEEE Trans. Pattern Anal. Machine Intell., 16(8):824-831.

Digital Library

[45]

Paradiso, M. and Nakayama, K. 1991. Brightness perception and filling-in. Vision Res., 7/8:1221-1236.

[46]

Pentland, A. 1987. A new sense for depth of field. IEEE Trans. Pattern Anal. Machine Intell., 9(4):523-531.

Digital Library

[47]

Perona, P. 1995. Deformable kernels for early vision. IEEE Trans. Pattern Anal. Machine Intell., 17(5):488-499.

Digital Library

[48]

Press, W., Teukolsky, S., Vetterling, W., and Flannery, B. 1992. Numerical Recipes in C (2 edition)., Cambridge University Press, chap 19, pp. 871-882.

Digital Library

[49]

Roberts, L. 1965. Machine perception of 3-dimensional solids. In Optical and Electro-Optical Information Processing, J. Tippett (Ed.), MIT Press: Cambridge, MA.

[50]

Sha'ashua, A. and Ullman, S. 1988. Structural saliency: the detection of globally salient structures using a locally connected network. In Proc. 2nd Int. Conf. on Computer Vision , Tampa, Florida. IEEE Computer Soc. Press, pp. 321- 327.

[51]

Simoncelli, E., Freeman, W., Adelson, E., and Heeger, D. 1992. Shiftable multiscale transforms. IEEE Trans. on Inf. Theory, 38(2):587-607.

[52]

Vetterli, M. 1984. Multidimensional subband coding: some theory and algorithms. Signal Processing, 6(2):97-112.

[53]

Werner, H. 1935. Studies on contour: I. Qualitative analyses. Amer. J. Psychol., 47:40-64.

[54]

Witkin, A. 1983. Scale space filtering. In Proc. Int. Joint Conf. on Artif. Intell., Karlsruhe, pp. 1019-1021.

[55]

Yuille, A. and Poggio, T. 1985. Fingerprints theorems for zero crossings. J. Opt. Soc. Am. A, 2(5):683-692.

[56]

Zeevi, Y. and Rotem, D. 1986. Image reconstruction from zero crossings. IEEE Trans. Acoust., Speech, Signal Processing, 34:1269- 1277.

[57]

Zucker, S. 1986. Early vision. In The Encyclopedia of Artificial Intelligence, S. Shapiro (Ed.), John Wiley.

[58]

Zucker, S., Hummel, R., and Rosenfeld, A. 1977. An application of relaxation labeling to line and curve enhancement. IEEE Trans. Comput., 26:394-403.

Digital Library

Cited By

Tian XGünther T(2024)A Survey of Smooth Vector Graphics: Recent Advances in Repr esentation, Creation, Rasterization, and Image VectorizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.322057530:3(1652-1671)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TVCG.2022.3220575
Khan NKim MTompkin J(2024)Are Multi-view Edges Incomplete for Depth Estimation?International Journal of Computer Vision10.1007/s11263-023-01890-y132:7(2639-2673)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11263-023-01890-y
Yoshizawa SYokota H(2021)Fast and faithful scale-aware image filtersThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-021-02249-537:12(3051-3062)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s00371-021-02249-5
Show More Cited By

Index Terms

Are Edges Incomplete?
1. Computing methodologies

Recommendations

Image restoration
Abstract
True images are usually degraded during image acquisition. Image restoration is for restoring true images from their observed but degraded versions; it is often used for preprocessing observed images so that subsequent image processing and ...
Ultrasound image restoration based on a learned dictionary and a higher-order MRF
Abstract
The restoration of images degraded by blur and multiplicative noise is a critical preprocessing step in medical ultrasound images which exhibit clinical diagnostic features of interest. This paper proposes a novel non-smooth non-convex ...
Blind image deblurring with reinforced use of edges

Blind image deblurring tries to restore a blurred image to a clear image without the blurring kernel known in advance, which is widely required in applications such as computer vision and medical image processing. With regard to this, the key issues ...

Comments

Information & Contributors

Information

Published In

cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 34, Issue 2-3

Special issue on computer vision research at NEC Research Institute

Nov. 1999

127 pages

ISSN:0920-5691

Editors:
Takeo Kanade
Carnegie Mellon Univ., Pittsburgh, PA
,
Olivier Faugeras
INRIA, France
,
Ingemar Cox
NEC Research Institute, Princeton, NJ

Issue’s Table of Contents

Copyright © Copyright © 1999 Kluwer Academic Publishers.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 October 1999

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

45
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tian XGünther T(2024)A Survey of Smooth Vector Graphics: Recent Advances in Repr esentation, Creation, Rasterization, and Image VectorizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.322057530:3(1652-1671)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TVCG.2022.3220575
Khan NKim MTompkin J(2024)Are Multi-view Edges Incomplete for Depth Estimation?International Journal of Computer Vision10.1007/s11263-023-01890-y132:7(2639-2673)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11263-023-01890-y
Yoshizawa SYokota H(2021)Fast and faithful scale-aware image filtersThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-021-02249-537:12(3051-3062)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s00371-021-02249-5
Chen KLuo YLai YChen YYao CChu HLee T(2020)Image Vectorization With Real-Time Thin-Plate SplineIEEE Transactions on Multimedia10.1109/TMM.2019.292212622:1(15-29)Online publication date: 3-Jan-2020
https://dl.acm.org/doi/10.1109/TMM.2019.2922126
Greeshma MBindu V(2020)Super-resolution Quality Criterion (SRQC): a super-resolution image quality assessment metricMultimedia Tools and Applications10.1007/s11042-020-09352-079:47-48(35125-35146)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s11042-020-09352-0
Peter PHoffmann SNedwed FHoeltgen LWeickert J(2016)Evaluating the true potential of diffusion-based inpainting in a compression contextImage Communication10.1016/j.image.2016.05.00246:C(40-53)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.image.2016.05.002
Bonneel NTompkin JSunkavalli KSun DParis SPfister H(2015)Blind video temporal consistencyACM Transactions on Graphics10.1145/2816795.281810734:6(1-9)Online publication date: 2-Nov-2015
https://dl.acm.org/doi/10.1145/2816795.2818107
Peter PSchmaltz CMach NMainberger MWeickert J(2015)Beyond pure qualityJournal of Visual Communication and Image Representation10.1016/j.jvcir.2015.06.01731:C(253-265)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1016/j.jvcir.2015.06.017
Gronde J(2015)Tensorial Orientation ScoresProceedings, Part II, of the 16th International Conference on Computer Analysis of Images and Patterns - Volume 925710.1007/978-3-319-23117-4_67(783-794)Online publication date: 2-Sep-2015
https://dl.acm.org/doi/10.1007/978-3-319-23117-4_67
Xie GSun XTong XNowrouzezahrai D(2014)Hierarchical diffusion curves for accurate automatic image vectorizationACM Transactions on Graphics10.1145/2661229.266127533:6(1-11)Online publication date: 19-Nov-2014
https://dl.acm.org/doi/10.1145/2661229.2661275
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents