Matched filtering techniques

doi:10.1017/CBO9780511777684.006

5 - Matched filtering techniques

from PART II - Similarity Metrics for Image Registration

Published online by Cambridge University Press: 03 May 2011

Qin-Sheng Chen

Edited by

Jacqueline Le Moigne ,

Nathan S. Netanyahu and

Roger D. Eastman

Show author details

Qin-Sheng Chen: Affiliation:
Flower Hospital, Ohio
Jacqueline Le Moigne: Affiliation:
NASA-Goddard Space Flight Center
Nathan S. Netanyahu: Affiliation:
Bar-Ilan University, Israel and University of Maryland, College Park
Roger D. Eastman: Affiliation:
Loyola University Maryland

Book contents

Get access

Summary

Abstract

A matched filter is a linear filtering device developed for signal detection in a noisy environment. The matched filtering technique can be directly employed in image registration for the estimation of image translation. The transfer function of a classic matched filter is the Fourier conjugate of the reference image, divided by the power spectrum of the system noise. If only the phase term of the reference image is used in the construction of the transfer function, the so-called phase-only matched filter generates a sharper peak at the maximum output than that obtained by a classic matched filter. Thus, the image translation can be detected more reliably. If rotation and scale change are also involved, the images to be registered can first be transformed into the Fourier-Mellin domain. The Fourier-Mellin transform of an image is translation invariant and represents rotation and scale changes as translations in the angular and radial coordinates. Employing matched filtering or phase-only matched filtering on the Fourier-Mellin transforms of the images yields the estimation of the image rotation and scaling. After correcting for rotation and scaling, the image translation can be determined by using the matched filtering or phase-only matched filtering method.

Introduction

Matched filtering is a classic signal processing technique widely used in signal detection (Turin, 1960; Vanderluht, 1969; Whalen, 1971; Kumar and Pochapsky, 1986). A matched filter is a linear filter with a transfer function that maximizes the output signal-to-noise ratio (SNR) for an input signal with known properties.

Type: Chapter
Information: Image Registration for Remote Sensing , pp. 112 - 130

DOI: https://doi.org/10.1017/CBO9780511777684.006 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Altmann, J. and Reitbock, H. J. O. (1984). A fast correlation method for scale-, and translation-invariant pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 46–57.CrossRef Google Scholar PubMed

Cavanagh, P. (1981). Size and position invariance in the visual system. Perception, 10, 491–499.Google Scholar

Chen, Q. S. (1993). Image Registration and Its Applications in Medical Imaging. Ph.D. thesis, Vrije Universiteit Brussel, Brussels, Belgium.

Chen, Q. S., Crownover, R., and Weinhous, M. S. (1999). Subunity coordinate translation with Fourier transform to achieve efficient and quality three-dimensional medical image interpolation. Medical Physics, 26, 1776–1782.CrossRef Google Scholar PubMed

Chen, Q. S., Defrise, M., and Deconinck, F. (1994). Symmetric phase-only matched filtering of Fourier-Mellin transforms for image registration and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 1156–1168.CrossRef Google Scholar

Castro, E. and Morandi, C. (1987). Registration of translated and rotated images using finite Fourier transforms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, 700–703.CrossRef Google Scholar PubMed

Castro, E., Cristini, G., Martelli, A., Morandi, C., and Vascotto, M. (1989). Compensation of random eye motion in television ophthalmoscopy: Preliminary results. IEEE Transactions on Medical Imaging, 6, 74–81.CrossRef Google Scholar

Ersoy, O. K. and Zeng, M. (1989). Nonlinear matched filtering. Journal of Optical Society of America A, 6, 636–648.CrossRef Google Scholar

Flannery, D. L., Loomis, J. S., and Milkovich, M. E. (1988). Transform-ratio ternary phase-amplitude filter formulation for improved correlation discrimination. Applied Optics, 27, 4079–4083.CrossRef Google Scholar PubMed

Gallagher, N. C. and Liu, B. (1973). Method for computing kinoforms that reduces image reconstruction error. Applied Optics, 12, 2328–2335.CrossRef Google Scholar PubMed

Horver, J. H. and Gianino, P. D. (1984). Phase-only matched filtering. Applied Optics, 23, 812–816.Google Scholar

Horver, J. H. and Gianino, P. D. (1985). Pattern recognition with binary phase-only filters. Applied Optics, 24, 609–611.Google Scholar

Keller, Y. and Averbuch, A. (2007). A project-based extension to phase correlation image alignment. Signal Processing, 87, 124–133.CrossRef Google Scholar

Kermisch, D. (1970). Image reconstruction from phase information only. Journal of Optical Society of America A, 60, 15–17.CrossRef Google Scholar

Kottzer, T., Rosen, J., and Shamir, J. (1992). Phase extraction pattern recognition. Applied Optics, 31, 1126–1137.CrossRef Google Scholar

Kumar, B. V. K. and Pochapsky, E. (1986). Signal-to-noise ratio considerations in modified matched spatial filtering. Journal of Optical Society of America A, 3, 777–786.CrossRef Google Scholar

Lam, K. P. (1985). Contour map registration using Fourier descriptions of gradient codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7, 332–338.CrossRef Google Scholar

Noyer, J.–C., Lanvin, P., and Benjelloun, M. (2004). Non-linear matched filtering for object detection and tracking. Pattern Recognition Letters, 25, 655–668.CrossRef Google Scholar

Oppenheim, A. V. and Lim, J. S. (1981). The importance of phase in signals. Proceedings of the IEEE, 69, 529–541.CrossRef Google Scholar

Pratt, W. K. (1978). Digital Image Processing. New York: John Wiley & Sons, pp. 526–566.Google Scholar

Reddy, B. S. and Chatterji, B. N. (1996). An FFT-based technique for translation, rotation and scale-invariant image registration. IEEE Transactions on Image Processing, 5, 1266–1271.CrossRef Google Scholar PubMed

Schalkoff, R. J. (1989). Digital Image Processing and Computer Vision. New York: John Wiley & Sons, pp. 279–286.Google Scholar

Schwartz, E. L. (1980). Computational anatomy and functional structure of striate cortex: A spatial mapping approach to perceptual coding. Vision Research, 20, 645–669.CrossRef Google Scholar

Schwartz, E. L. (1981). Cortical anatomy and spatial frequency analysis. Perception, 10, 445–468.CrossRef Google Scholar PubMed

Segman, J. (1992). Fourier cross correlation and invariant transformations for an optimal recognition of functions deformed by affine groups. Journal of Optical Society of America A, 9, 895–902.CrossRef Google Scholar

Sheng, Z. Y. and Arsenault, H. H. (1986). Experiments on pattern recognition using invariant Fourier-Mellin descriptors. Journal of Optical Society of America A, 3, 771–776.CrossRef Google Scholar PubMed

Turin, G. L. (1960). An introduction to matched filters. IRE Transactions on InformationTheory, 6, 311–29.Google Scholar

Vanderluht, A. (1969). Signal detection by complex spatial filtering. IEEE Transactions on Information Theory, 10, 130–145.Google Scholar

Wang, Z. Q., Gillespie, W. A., Cartwright, C. M., and Soutar, C. (1993). Optical pattern recognition using a synthetic discriminant amplitude-compensated matched filter. Applied Optics, 32, 184–189.CrossRef Google Scholar PubMed

Whalen, A. D. (1971). Detection of Signal in Noise. New York: Academic Press, pp. 167–178.Google Scholar

Wu, Q. X., Bones, P. J., and Bates, R. H. T. (1989). Translation motion compensation for coronary angiogram sequences. IEEE Transactions on Medical Imaging, 8, 276–282.CrossRef Google Scholar