A spatial domain optimal trade-off Maximum Average Correlation Height (OT-MACH) filter has been previously
developed and shown to have advantages over frequency domain implementations in that it can be made locally adaptive
to spatial variations in the input image background clutter and normalised for local intensity changes. In this paper we
compare the performance of the spatial domain (SPOT-MACH) filter to the widely applied data driven technique known
as the Scale Invariant Feature Transform (SIFT). The SPOT-MACH filter is shown to provide more robust recognition
performance than the SIFT technique for demanding images such as scenes in which there are large illumination
gradients. The SIFT method depends on reliable local edge-based feature detection over large regions of the image plane
which is compromised in some of the demanding images we examined for this work. The disadvantage of the SPOTMACH
filter is its numerically intensive nature since it is template based and is implemented in the spatial domain.
An improvement to the wavelet-modified Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter
with the use of the Rayleigh distribution filter is proposed. The Rayleigh distribution filter is applied to the OT-MACH
filter to provide a sharper low frequency cut-off than the Laplacian of Gaussian based wavelet filter that has been
previously reported to enhance OT-MACH filter performance. Filters are trained using a 3D CAD model and tested on
the corresponding real target object in high clutter environments acquired from a Forward Looking Infra Red (FLIR)
sensor. Comparative evaluation of the performance of the original, wavelet and Rayleigh modified OT-MACH filter is
reported for the recognition of the target objects present within the thermal infra-red image data set.
A speed enhanced space variant correlation filer which has been designed to be invariant to change in orientation and
scale of the target object but also to be spatially variant, i.e. the filter function becoming dependant on local clutter
conditions within the image. The speed enhancement of the filter is due to the use of optimization techniques employing
low-pass filtering to restrict kernel movement to be within regions of interest. The detection and subsequent
identification capability of the two-stage process has been evaluated in highly cluttered backgrounds using both visible
and thermal imagery acquired from civil and defense domains along with associated training data sets for target detection
and classification. In this paper a series of tests have been conducted in multiple scenarios relating to situations that pose
a security threat. Performance matrices comprised of peak-to-correlation energy (PCE) and peak-to-side lobe ratio (PSR)
measurements of the correlation output have been calculated to allow the definition of a recognition criterion. The
hardware implementation of the system has been discussed in terms of Field Programmable Gate Array (FPGA) chipsets
with implementation bottle necks and their solution being considered.
A wavelet-modified frequency domain Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter has
been trained using 3D CAD models and tested on real target images acquired from a Forward Looking Infra Red (FLIR)
sensor. The OT-MACH filter can be used to detect and discriminate predefined targets from a cluttered background. The
FLIR sensor extends the filter's ability by increasing the range of detection by exploiting the heat signature differences
between the target and the background. A Difference of Gaussians (DoG) based wavelet filter has been use to improve
the OT-MACH filter discrimination ability and distortion tolerance. Choosing the right standard deviation values of the
two Gaussians comprising the filter is critical. In this paper we present a new technique for auto adjustment of the DoG
filter parameters driven by the expected target size. Tests were carried on images acquired by the Apache AH-64
helicopter mounted FLIR sensor, results showing an overall improvement in the recognition of target objects present
within the IR images.
A frequency domain implementation of the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter
has been optimized to classify target vehicles acquired from a Forward Looking Infra Red (FLIR) sensor. The clutter
noise does not have a white spectrum and models employing the power spectral density of the background clutter require
a predefined threshold. A method of automatically adjusting the noise model in the filter by using the input image
statistical information has been introduced. Parameter surfaces for the remaining OT-MACH variables are calculated in
order to determine optimal operating conditions for the view independent recognition of vehicles in highly cluttered
FLIR imagery.
A space domain implementation of the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter can
not only be designed to be invariant to change in orientation of the target object but also to be spatially variant, i.e. the
filter function becoming dependant on local clutter conditions within the image. Sequential location of the kernel in all
regions of the image does, however, require excessive computational resources. An optimization technique is discussed
in this paper which employs low-pass filtering to highlight the potential region of interests in the image and then restricts
the movement of the kernel to these regions to allow target identification. The detection and subsequent identification
capability of the two-stage process has been evaluated in highly cluttered backgrounds using both visible and thermal
imagery and associated training data sets. A performance matrix comprised of peak-to-correlation energy (PCE) and
peak-to-side lobe ratio (PSR) measurements of the correlation output has been calculated to allow the definition of a
recognition criterion. A feasible hardware implementation for potential use in a security application using the proposed
two-stage process is also described in the paper.
We propose a space variant Maximum Average Correlation Height (MACH) filter which can be locally modified
depending upon its position in the input frame. This can be used to detect targets in an environment from varying ranges
and in unpredictable weather conditions using thermal images. It enables adaptation of the filter dependant on
background heat signature variances and also enables the normalization of the filter energy levels. The kernel can be
normalized to remove a non-uniform brightness distribution if this occurs in different regions of the image. The main
constraint in this implementation is the dependence on computational ability of the system. This can be minimized with
the recent advances in optical correlators using scanning holographic memory, as proposed by Birch et al. [1]
In this paper we describe the discrimination abilities of the MACH filter against background heat signature variances and
tolerance to changes in scale and calculate the improvement in detection capabilities with the introduction of a nonlinearity.
We propose a security detection system which exhibits a joint process where human and an automated pattern
recognition system contribute to the overall solution for the detection of pre-defined targets.
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