&lt;title&gt;MACH filter synthesizing for detecting targets in cluttered environment for grayscale optical correlator&lt;/title&gt;

Zhou, Hanying; Chao, Tien‐Hsin

doi:10.1117/12.341322

Cited by 39 publications

(25 citation statements)

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“…Another filter that was considered in this study is the OT-MACH filter [39][40]. From these different comparisons, the POF, AMPOF, and the MACH composite filters were selected for the comparison of correlation performances with our optimized ASPOF filter.…”

Section: Composite Filters Used For Comparison With the Aspofmentioning

confidence: 99%

Spectral optimized asymmetric segmented phase-only correlation filter

2012

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We suggest a new type of optimized composite filter, i.e. the asymmetric segmented phase-only filter (ASPOF), for improving the effectiveness of a VanderLugt correlator (VLC) when used for face identification. Basically, it consists in merging several reference images after application of a specific spectral optimization method.After segmentation of the spectral filter plane to several areas, each area is assigned to a single winner reference according to a new optimized criterion. The point of the paper is to show that this method offers a significant performance improvement on standard composite filters for face identification. We first briefly revisit composite filters (adapted, phase-only, inverse, compromise optimal, segmented, minimum average correlation energy, optimal trade-off maximum average correlation, and amplitude modulated phase-only (AMPOF)) which are tools of choice for face recognition based on correlation techniques and compare their performances with those of the ASPOF. We illustrate some of the drawbacks of current filters for several binary and gray scale images identifications. Next, we describe the optimization steps and introduce the ASPOF that can overcome these technical issues to improve the quality and the reliability of the correlation based decision. We derive performance measures, i.e. peak-to-correlation energy values and receiver operating characteristic curves, to confirm consistency of the results. We numerically find that this filter increases the recognition rate and decrease the false alarm rate. The results show that the discrimination of the ASPOF is comparable to that of the AMPOF, but the ASPOF is more robust than the trade-off maximum average correlation height 3 (OT-MACH) against rotation and various types of noise sources. Our method has several features that make it amenable to experimental implementation using a VLC.4

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Section: Composite Filters Used For Comparison With the Aspofmentioning

confidence: 99%

Spectral optimized asymmetric segmented phase-only correlation filter

2012

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“…In effect, all the training set images, when correlated with an LCF, are set to produce certain prespecified peak values, but there is no information provided for the test image correlation peak height values of the training images. In the unconstrained correlation filter synthesis (Mahalanobis et al, 1994;Mahalanobis & Kumar, 1997;Zhou & Chao, 1999) there are no hard constraints on the correlation peak heights. Thus, the assumption made is that by removing the hard constraints the number of possible solutions the filter can draw on increases by allowing the correlation peak height values to move freely to any value, so improving its performance.…”

Section: Unconstrained-honn Filter For Multiple Objects Recognitionmentioning

confidence: 99%

“…In general, unconstrained linear combinatorial-type filters (Mahalanobis et al, 1994;Mahalanobis & Kumar, 1997;Zhou & Chao, 1999) produce broader correlation peaks but offer better distortion tolerance. However, they are not explicitly optimised to provide good quality discrimination ability between classes.…”

Section: Unconstrained-honn Filter For Multiple Objects Recognitionmentioning

confidence: 99%

Hybrid Optical Neural Network-Type Filters for Multiple Objects Recognition within Cluttered Scenes

Kypraios¹

2011

Object Recognition

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“…The different values of ,  and  control the MACH filter's behaviour to match different application requirements 16 . If = = 0, the resulting filter behaves much like a MVSDF filter 17 with relatively good noise tolerance but broad peaks.…”

Section: Maximum Average Correlation Height (Mach) Filtermentioning

confidence: 99%

Position, rotation, scale, and orientation invariant multiple object recognition from cluttered scenes

2005

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Position-, rotation-, scale-, and orientation-invariant multiple object recognition from cluttered scenes Article (Accepted Version) http://sro.sussex.ac.uk Bone, Peter, Young, Rupert and Chatwin, Chris (2006) Position-, rotation-, scale-, and orientation-invariant multiple object recognition from cluttered scenes. Optical Engineering, 45 (7). ISSN 0091-3286This version is available from Sussex Research Online: http://sro.sussex.ac.uk/28111/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. ABSTRACTA method of tracking objects in video sequences despite any kind of perspective distortion is demonstrated. Moving objects are initially segmented from the scene using a background subtraction method to minimize the search area of the filter. A variation on the Maximum Average Correlation Height (MACH) filter is used to create invariance to orientation while giving high tolerance to background clutter and noise. A log r-θ mapping is employed to give invariance to in-plane rotation and scale by transforming rotation and scale variations of the target object into vertical and horizontal shifts. The MACH filter is trained on the log r-θ map of the target for a range of orientations and applied sequentially over the regions of movement in successive video frames. Areas of movement producing a strong correlation response indicate an in-class target and can then be used to determine the position, in-plane rotation and scale of the target objects in the scene and track it over successive frames.

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<title>MACH filter synthesizing for detecting targets in cluttered environment for grayscale optical correlator</title>

Cited by 39 publications

References 0 publications

Spectral optimized asymmetric segmented phase-only correlation filter

Spectral optimized asymmetric segmented phase-only correlation filter

Hybrid Optical Neural Network-Type Filters for Multiple Objects Recognition within Cluttered Scenes

Position, rotation, scale, and orientation invariant multiple object recognition from cluttered scenes

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