Multidimensional digital filters for point-target detection in cluttered infrared scenes

Abstract. A three-dimensional (3-D) spatiotemporal prediction-error filter (PEF) is used to enhance foreground/background contrast in (real and simulated) sensor image sequences. Relative velocity is utilized to extract point targets that would otherwise be indistinguishable with spatial frequency alone. An optical-flow field is generated using local estimates of the 3-D autocorrelation function via the application of the fast Fourier transform (FFT) and inverse FFT. Velocity estimates are then used to tune in a background-whitening PEF that is matched to the motion and texture of the local background. Finite impulse response (FIR) filters are designed and implemented in the frequency domain. An analytical expression for the frequency response of velocity-tuned FIR filters, of odd or even dimension with an arbitrary delay in each dimension, is derived.

[1]  Marc M. Van Hulle,et al.  A phase-based approach to the estimation of the optical flow field using spatial filtering , 2002, IEEE Trans. Neural Networks.

[2]  Eduardo Ros,et al.  High-Performance Optical-Flow Architecture Based on a Multi-Scale, Multi-Orientation Phase-Based Model , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[3]  Weidong Yang,et al.  Moving dim point target detection with three-dimensional wide-to-exact search directional filtering , 2007, Pattern Recognit. Lett..

[4]  Shu-Mei Guo,et al.  Efficient block-matching motion estimation algorithm , 2013, J. Electronic Imaging.

[5]  Michael Elad,et al.  On the Design of Filters for Gradient-Based Motion Estimation , 2005, Journal of Mathematical Imaging and Vision.

[6]  N.G. Kingsbury,et al.  Frequency-domain motion estimation using a complex lapped transform , 1993, IEEE Trans. Image Process..

[7]  Kenta Fujii,et al.  Motion Analysis Using 3D High-Resolution Frequency Analysis , 2013, IEEE Transactions on Image Processing.

[8]  Leonard T. Bruton,et al.  Multidimensional filtering using combined discrete Fourier transform and linear difference equation methods , 1990 .

[9]  Marco Diani,et al.  Space-time processing for the detection of airborne targets in IR image sequences , 2001 .

[10]  Keith Langley,et al.  Recursive Filters for Optical Flow , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Yao Zhao,et al.  Bilateral two-dimensional least mean square filter for infrared small target detection , 2014 .

[12]  Leonard T. Bruton,et al.  3-D IIR filtering using decimated DFT-polyphase filter bank structures , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[13]  David W. Thomas,et al.  The two-dimensional adaptive LMS (TDLMS) algorithm , 1988 .

[14]  Minh N. Do,et al.  Multidimensional Directional Filter Banks and Surfacelets , 2007, IEEE Transactions on Image Processing.

[15]  Oscar Nestares,et al.  Automatic enhancement of noisy image sequences through local spatiotemporal spectrum analysis , 2000 .

[16]  I. Reed,et al.  A recursive moving-target-indication algorithm for optical image sequences , 1990 .

[17]  Taek Lyul Song,et al.  Aerial-target detection using the recursive temporal profile and spatiotemporal gradient pattern in infrared image sequences , 2012 .

[18]  Alexandre Bernardino,et al.  Fast IIR Isotropic 2-D Complex Gabor Filters With Boundary Initialization , 2006, IEEE Transactions on Image Processing.

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

[20]  Anton Kummert,et al.  On multidimensional velocity filter banks for video-based motion analysis of world-coordinate objects , 2011, The 2011 International Workshop on Multidimensional (nD) Systems.

[21]  G. Lampropoulos,et al.  Filtering of moving targets using SBIR sequential frames , 1995, IEEE Transactions on Aerospace and Electronic Systems.

[22]  Marco Diani,et al.  Optimal criterion to select the background estimation algorithm for detection of dim point targets in infrared surveillance systems , 2011 .

[23]  Anton Kummert,et al.  Motion-based object detection using 3D wave digital filters , 2008, 2008 8th IEEE International Conference on Computer and Information Technology.

[24]  Akira Kojima,et al.  Motion detection using 3D-FFT spectrum , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[25]  Dimitrios S. Alexiadis,et al.  Narrow directional steerable filters in motion estimation , 2008, Comput. Vis. Image Underst..

[26]  Xiangzhi Bai,et al.  New class of top-hat transformation to enhance infrared small targets , 2008, J. Electronic Imaging.

[27]  S. B. Williams,et al.  Multidimensional (MD) Circuits and Systems for Emerging Applications Including Cognitive Radio, Radio Astronomy, Robot Vision and Imaging , 2013, IEEE Circuits and Systems Magazine.

[28]  James R. Zeidler,et al.  Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data , 1993, IEEE Trans. Image Process..

[29]  Robin R. Murphy,et al.  A VLSI Architecture and Algorithm for Lucas–Kanade-Based Optical Flow Computation , 2010, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[30]  Tim J. Patterson,et al.  Detection algorithms for image sequence analysis , 1989, IEEE Trans. Acoust. Speech Signal Process..

[31]  Takeshi Takaki,et al.  High-Frame-Rate Optical Flow System , 2012, IEEE Transactions on Circuits and Systems for Video Technology.