Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-22T07:44:14.553Z Has data issue: false hasContentIssue false
  • English
  • Français

An analysis of local energy and phase congruency models in visual feature detection

Published online by Cambridge University Press:  17 February 2009

Y. K. Aw ,
Robyn Owens  and
John Ross
Y. K. Aw
Affiliation:
Department of Computer Science, The University of Western Australia, Nedlands, WA 6907
Robyn Owens
Affiliation:
Department of Computer Science, The University of Western Australia, Nedlands, WA 6907
John Ross
Affiliation:
Department of Psychology, The University of Western Australia, Nedlands, WA 6907
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A variety of approaches have been developed for the detection of features such as edges, lines, and corners in images. Many techniques presuppose the feature type, such as a step edge, and use the differential properties of the luminance function to detect the location of such features. The local energy model provides an alternative approach, detecting a variety of feature types in a single pass by analysing order in the phase components of the Fourier transform of the image. The local energy model is usually implemented by calculating the envelope of the analytic signal associated with the image function. Here we analyse the accuracy of such an implementation, and show that in certain cases the feature location is only approximately given by the local energy model. Orientation selectivity is another aspect of the local energy model, and we show that a feature is only correctly located at a peak of the local energy function when local energy has a zero gradient in two orthogonal directions at the peak point.

Type
Research Article
Information
The ANZIAM Journal , Volume 40 , Issue 1 , July 1998 , pp. 97 - 122
Copyright
Copyright © Australian Mathematical Society 1998

References

[1]Aw, Y. K., Owens, R. A. and Ross, J., “Feature detection and classification by tuned-neurons”, in Proc. 4th Autralian Joint Conference on Artificial Intelligence, 21th–23rd, Nov 1990, Perth, (1990) 291–303.Google Scholar
[2]Aw, Y. K., Owens, R. A. and Ross, J., “Image compression and reconstruction using a 1-d feature catalogue”, in Proc. of the Second European Conference on Computer Vision, Santa Margherita Ligure, Italy, May 1992, (1992) 749–753.Google Scholar
[3]Aw, Y. K., Owens, R. A. and Ross, J., “Learning features in natural images”, in Proc. of the IEEE international conference on Neural Network Applications to Signal Processing, (1993).Google Scholar
[4]Aw, Y. K., Owens, R. A. and Ross, J., “A catalogue of 1-D features in natural image”, J. Comp. Vision, Graph, and Im. Processing 56 (1994) 173–181.Google Scholar
[5]Canny, J. F., “A computational approach to edge detection”, IEEE Trans. on PAMI 8 (1986) 679–697.CrossRefGoogle ScholarPubMed
[6]Fleet, D. J. and Jepson, A. D., “Computation of component image velocity from local phase information”, Int. J. Comp. Vision 5 (1991) 77–104.CrossRefGoogle Scholar
[7]Haralick, R. M., “Edge and region analysis for digital image data”, Comp. Graph, and Im. Processing 12 (1980) 60–73.CrossRefGoogle Scholar
[8]Haralick, R. M., “Digital step edges from zero crossings of second directional derivatives”, IEEE Trans. on PAMI 6 (1984) 58–68.CrossRefGoogle ScholarPubMed
[9]Kovesi, P., “A dimensionless measure of edge significance from phase congruency calculated via wavelets”, in Procs. 1st New Zealand Conf. on Image and Vision Computing, Auckland, (1994) 87–94.Google Scholar
[10]Marr, D. and Hildreth, E., “Theory of edge detection”, Proc. R. Soc. London Ser. B 207 (1980) 187–217.Google ScholarPubMed
[11]Morrone, C. and Owens, R. A., “Feature detection from local energy”, Pattern Recognition Letters 6 (1987) 303–313.CrossRefGoogle Scholar
[12]Morrone, M. C. and Burr, D. C., “A phase-dependent energy model”, Proc. R. Soc. London Ser. B 235 (1988) 221–245.Google ScholarPubMed
[13]Morrone, M. C., Ross, J., Burr, D. C. and Owens, R., “Mach bands are phase dependent”, Nature 324 (1986) 250–253.CrossRefGoogle Scholar
[14]Noble, A., “Morphological feature detection”, in Second Int. Conference on Comp. Vis., Dec 5–8, 1988 Florida, USA, 112–116.Google Scholar
[15]Oppenheim, Alan V. and Lim, Jae S., “The importance of phase in signals”, Proceedings of the IEEE 69 (1981) 529–541.CrossRefGoogle Scholar
[16]Owens, R. A., “Feature-free images”, Pattern Recognition Letters 15 (1994) 35–44.CrossRefGoogle Scholar
[17]Owens, R. A., Aw, Y. K. and Ross, J., “Stable feature structure in images”, in DICTA-91, Proceedings of the conference on Digital Image Processing: Techniques and Applications (1991) 357–364.Google Scholar
[18]Owens, R. A., Venkatesh, S. and Ross, J., “Edge detection is a projection”, Pattern Recognition Utters 9 (1989) 233–244.CrossRefGoogle Scholar
[19]Peli, T. and Malah, D., “A study of edge detection algorithms”, Comp. Graph, and Imag. Proc. 20 (1982) 1–21.CrossRefGoogle Scholar
[20]Robbins, B. and Owens, R., “The 2D local energy model”, Technical report 94/5, Department of Computer Science, The University of Western Australia, 1994.Google Scholar
[21]Smith, S. M. and Brady, J. M., “SUSAN - a new approach to low level image processing”, DRA Technical Report TR95SMS lb, Defence Research Agency, Farnborough, Hampshire, GUI4 6TD, UK, 1994.Google Scholar
[22]Venkatesh, S., “A study of energy based models for the detection and classification of image features”, Ph. D. Thesis, The University of Western Australia, Department of Computer Science, 1990.Google Scholar
[23]Venkatesh, S. and Owens, R. A., “On the classification of image features”, Pattern Recognition Utters 11 (1990) 339–349.CrossRefGoogle Scholar