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

Abstract

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.

Space invariant vehicle recognition for toll plaza monitoring and auditing system

“…In case of in-plane rotation invariance, peaks shift horizontally, while in case of scale invariance, peaks shift vertically. It also supports wide field of view, reduced pixel count, and highly focused central area, as in [5,6].…”

“…extracted from toll plaza camera video by implementing polar-exponential sensor geometry as shown in Fig.. 2.1 [5]. The index values of the pixels are stored in lookup tables i.e.…”

“…The Maximum Average Correlation Height (MACH) filter [4] was used for detection of targets in cluttered environment. Peter Bone and et al [5] used MACH filer to recognize the target to orientation invariance and they used log r-θ mapping to create in-plane rotation and scale invariance 978-1-4244-4873-9/09/$25.00 ©2009 IEEE while recognition. They applied the technique on a single target object to demonstrate position, rotation, scale and orientation invariance to object recognition.…”

Influence of perturbations in a hybrid digital-optical correlator

“…Young and his co-workers in 1993 outlined an opticaldigital hybrid correlator system that allows the potential for a multikilohertz reference template search on data acquired at video rates [5][6][7][8]. In this hybrid digital-optical correlation approach, the input data is digitally Fourier transformed and is multiplied with the digitally presynthesized distortion-invariant filters, and the product function is displayed over an electrically addressed spatial light modulator (SLM).…”

“…Pattern recognition using optical techniques, one of the most important and promising applications, has generated considerable amount of interest in the optics community for last few decades [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. As a result, optical correlator systems have now been started appearing in the market place [15].…”