Akber Gardezi

An optical space domain volume holographic correlator

We propose a novel space domain volume holographic correlator system. One of the limitations of conventional correlators is the bandwidth limits imposed by updating the filter and the readout speed of the CCD. The volume holographic correlator overcomes these by storing a large number of filters that can be interrogated simultaneously. By using angle multiplexing, the match can be read out onto a high speed linear array of sensors. A scanning window can be used to implement shift invariance, thus, making the system operate like a space domain correlator. The space domain correlation method offers an advantage over the frequency domain correlator in that the correlation filter no longer has shift invariance imposed on it since the kernel can be modified depending on its position. This maybe used for normalising the kernel or imposing some non-linearity in an attempt to improve performance. However, one of the key advantages of the frequency domain method is lost using this technique, namely the speed of the computation. A large kernel space-domain correlation, performed on a computer, will be very slow compared to what is achievable using a 4f optical correlator. We propose a method of implementing this using the scanning holographic memory based correlator.

A space variant Maximum Average Correlation Height (MACH) filter for object recognition in real time thermal images for security applications

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.

Parameter optimization of the optimal trade-off maximum average correlation height filter (OT-MACH) for FLIR imaging in high clutter environments

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.

Implementation of the Maximum Average Correlation Height (MACH) filter in the spatial domain for object recognition from clutter backgrounds

A moving space domain window is used to implement a Maximum Average Correlation Height (MACH) filter which can be locally modified depending upon its position in the input frame. This enables adaptation of the filter dependant on locally variant background clutter conditions and also enables the normalization of the filter energy levels at each step. Thus the spatial domain implementation of the MACH filter offers an advantage over its frequency domain implementation as shift invariance is not imposed upon it. The only drawback of the spatial domain implementation of the MACH filter is the amount of computational resource required for a fast implementation. Recently an optical correlator using a scanning holographic memory has been proposed by Birch et al [1] for the real-time implementation of space variant filters of this type. In this paper we describe the discrimination abilities against background clutter and tolerance to in-plane rotation, out of plane rotation and changes in scale of a MACH correlation filter implemented in the spatial domain.

Enhancement of the speed of space-variant correlation filter implementations by using low-pass pre-filtering for kernel placement and applications to real-time security monitoring

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.

Automatic parameter adjustment of difference of Gaussian (DoG) filter to improve OT-MACH filter performance for target recognition applications

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 filters 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.

Vehicle monitoring under Vehicular Ad-Hoc Networks (VANET) parameters employing illumination invariant correlation filters for the Pakistan motorway police

A spatial domain optimal trade-off Maximum Average Correlation Height (SPOT-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. The main concern for using the SPOT-MACH is its computationally intensive nature. However in the past enhancements techniques were proposed for the SPOT-MACH to make its execution time comparable to its frequency domain counterpart. In this paper a novel approach is discussed which uses VANET parameters coupled with the SPOT-MACH in order to minimise the extensive processing of the large video dataset acquired from the Pakistan motorways surveillance system. The use of VANET parameters gives us an estimation criterion of the flow of traffic on the Pakistan motorway network and acts as a precursor to the training algorithm. The use of VANET in this scenario would contribute heavily towards the computational complexity minimization of the proposed monitoring system.

Comparison of spatial domain optimal trade-off maximum average correlation height (OT-MACH) filter with scale invariant feature transform (SIFT) using images with poor contrast and large illumination gradient

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.

Human detection using OT-MACH filter in cluttered FLIR imagery

An improvement to the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter with the addition of a Rayleigh distribution filter has been used to detect humans in FLIR imagery scenes. The Rayleigh distribution filter is applied to the OT-MACH filter to provide a sharper low frequency cut-off which improves the OT-MACH filter performance in terms of target discrimination. The OT-MACH filter has been trained using a Computer Aided Design (CAD) model and tested on the corresponding real target object in high clutter environments acquired from a Forward Looking Infra Red (FLIR) sensor. Evaluation of the performance of the Rayleigh modified OT-MACH filter is reported for the recognition of humans present within the thermal infra-red image data set.

Improving OT-MACH filter performance for target recognition applications with the use of a Rayleigh distribution filter

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.