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1 September 2009 Binary image registration using cellular simultaneous recurrent networks
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Cellular simultaneous recurrent networks (CSRN)s have been traditionally exploited to solve digital control and conventional maze traversing problems. In a previous work [1], we investigate the use of CSRNs for image registration under affine transformations for binary images. In Ref. [1], we attempt to register simulated binary images with in-plane rotations between ±20° using two different CSRN implementations such as with 1) a general multi-layered perceptron (GMLP) architecture; and (2) a modified MLP architecture with multi-layered feedback. Our results in Ref. 1 show that both architectures achieve moderate local registration, with our modified MLP architecture producing a best result of around 64% for cost function accuracy and 98% for image registration accuracy. In this current work, for the first time in literature, we investigate gray scale image registration using CSRNs. We exploit both types of CSRNs for registration of realistic images and perform complete evaluation of both binary and gray-scale image registration. Simulation results with both CSRN architectures show an average cost function accuracy of 40.5% and an average image accuracy of 33.2%, with a best result of 46.2% and 40.3%, respectively. Image results clearly show that the CSRN shows promise for use in registration of gray-scale images.
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Keith Anderson, Khan Iftekharuddin, and Paul Kim "Binary image registration using cellular simultaneous recurrent networks", Proc. SPIE 7442, Optics and Photonics for Information Processing III, 74420C (1 September 2009);

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