A surprising Radon transform result and its application to motion detection

An elliptical region of the plane supports a positive-valued function whose Radon transform depends only on the slope of the integrating line. Any two parallel lines that intersect the ellipse generate equal line integrals of the function. We prove that this peculiar property is unique to the ellipse; no other convex, compact region of the plane supports a nonzero-valued function whose Radon transform depends only on slope. We motivate this problem by considering the detection of a constant-velocity moving object in a sequence of images. In the presence of additive, white, Gaussian noise. The intensity distribution of the object is known, but the velocity is only assumed to lie in some known set, for example, an ellipse or a rectangle. The object is to find a space-time linear filter, operating on the image sequence, whose minimum output signal-to-noise ratio (SNR) for any velocity in the set is maximized. For an ellipse (and its special cases, the disk and the line-segment) the special Radon transform property of the ellipse enables us to obtain a closed-form, analytical solution for the minimax filter, which significantly outperforms the conventional three-dimensional (3-D) matched filter. This analytical solution also suggests a constrained minimax filter for other velocity sets, obtainable in closed form, whose SNR can be very close to the minimax SNR.

[1]  J. N. Sanders A method for determining filter spacing in assumed velocity filter banks , 1993 .

[2]  R.M. Gagliardi,et al.  Application of Three-Dimensional Filtering to Moving Target Detection , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Benjamin Friedlander,et al.  A Frequency Domain Algorithm for Multiframe Detection and Estimation of Dim Targets , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Thomas L. Marzetta,et al.  Velocity filtering of acoustic well logging waveforms , 1989, IEEE Trans. Acoust. Speech Signal Process..

[5]  Y. Chen On suboptimal detection of 3-dimensional moving targets , 1989 .

[6]  Thomas L. Marzetta,et al.  Fan filters, the 3-D Radon transform, and image sequence analysis , 1994, IEEE Trans. Image Process..

[7]  S. C. Pohlig,et al.  An algorithm for detection of moving optical targets , 1989 .

[8]  Y. Barniv Application of velocity filtering to optical-flow passive ranging , 1992 .

[9]  Larry B. Stotts,et al.  Optical moving target detection with 3-D matched filtering , 1988 .

[10]  Leonard T. Bruton,et al.  The enhancement and tracking of moving objects in digital images using adaptive three-dimensional recursive filters , 1986 .

[11]  J. P. Fail,et al.  LES FILTRES EN EVENTAIL , 1963 .

[12]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[13]  Gilbert Strang,et al.  Introduction to applied mathematics , 1988 .

[14]  Milo M. Backus,et al.  WIDE‐BAND VELOCITY FILTERING—THE PIE‐SLICE PROCESS , 1963 .

[15]  広 久保田,et al.  Principle of Optics , 1960 .

[16]  Thomas L. Marzetta Uniformly optimal 3-D fan filters for optical moving target detection , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  Thomas L. Marzetta Optimal detection of known moving objects in a noisy image sequence with velocity uncertainty , 1994, Proceedings of 1st International Conference on Image Processing.