Detection algorithms for image sequence analysis

“…Other approaches to the problem of point-target detection in cluttered infrared scenes tend to either focus on 1-D temporal filters [15], or 2-D spatial filters [16], [17]. When 3-D approaches are considered, different logic is typically applied in the spatial and temporal dimensions [18].…”

Section: Introductionmentioning

confidence: 99%

Parallel software implementation of recursive multidimensional digital filters for point-target detection in cluttered infrared scenes

Kennedy

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A technique for the enhancement of point targets in clutter is described. The local 3-D spectrum at each pixel is estimated recursively. An optical flow-field for the textured background is then generated using the 3-D autocorrelation function and the local velocity estimates are used to apply high-pass velocity-selective spatiotemporal filters, with finite impulse responses (FIRs), to subtract the background clutter signal, leaving the foreground target signal, plus noise. Parallel software implementations using a multicore central processing unit (CPU) and a graphical processing unit (GPU) are investigated.

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“…2 Simply applying a threshold to extract possible target-detections for a tracker is an unsatisfactory solution due to the high number of correlated clutter-detections produced by background features. On the one hand, consecutive frame differencing or the application of a one-dimensional (1-D) high-pass filter of low order in the temporal domain 1 is a very simple and effective approach in many situations (e.g. blue sky); however, this is likely to produce a high probability of false alarm for dynamic backgrounds and a low probability of detection for static targets.…”

Section: Introductionmentioning

confidence: 99%

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

Kennedy

2014

J. Electron. Imaging

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A 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 on 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.

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Detection algorithms for image sequence analysis

Cited by 8 publications

References 5 publications

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Size-distribution estimation in process fluids by ultrasound for particle sizes in the wavelength range

Parallel software implementation of recursive multidimensional digital filters for point-target detection in cluttered infrared scenes

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

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