Machine Vision, Pattern Recognition

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

[+] Author Affiliations
Peter Bone, Rupert Young, Chris Chatwin

University of Sussex, Department of Engineering and Design, Laser and Photonic Systems Research Group, Brighton BN1 9QT, United Kingdom

Opt. Eng. 45(7), 077203 (July 20, 2006). doi:10.1117/1.2227362
History: Received September 08, 2005; Accepted December 19, 2005; Published July 20, 2006
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A method of detecting target objects in still images despite any kind of geometrical distortion is demonstrated. Two existing techniques are combined, each one capable of creating invariance to various types of distortion of the target object. A maximum average correlation height (MACH) filter is used to create invariance to orientation and gives good tolerance to background clutter and noise. A logr-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 logr-θ map of the target for a range of orientations and applied sequentially over regions of interest in the input image. Areas producing a strong correlation response can then be used to determine the position, in-plane rotation, and scale of the target objects in the scene.

Figures in this Article
© 2006 Society of Photo-Optical Instrumentation Engineers

Citation

Peter Bone ; Rupert Young and Chris Chatwin
"Position-, rotation-, scale-, and orientation-invariant multiple object recognition from cluttered scenes", Opt. Eng. 45(7), 077203 (July 20, 2006). ; http://dx.doi.org/10.1117/1.2227362


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