By Aravinthan Parthibarajan, Gopalakrishnan Narayanamurthy, Arun srinivas Parthibarajan (auth.), Natarajan Meghanathan, Brajesh Kumar Kaushik, Dhinaharan Nagamalai (eds.)
This quantity constitutes the 3rd of 3 components of the refereed court cases of the 1st foreign convention on desktop technology and knowledge know-how, CCSIT 2010, held in Bangalore, India, in January 2011. The forty six revised complete papers awarded during this quantity have been conscientiously reviewed and chosen. The papers are geared up in topical sections on tender computing, similar to AI, Neural Networks, Fuzzy structures, etc.; allotted and parallel structures and algorithms; protection and data coverage; advert hoc and ubiquitous computing; instant advert hoc networks and sensor networks.
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Additional resources for Advanced Computing: First International Conference on Computer Science and Information Technology, CCSIT 2011, Bangalore, India, January 2-4, 2011. Proceedings, Part III
Off-line Geometric parameters for automatic signature verification using fixed point arithematic. IEEE Transections on Pattern Analysis and Machine Intelligence 27(6), 993–997 (2005) 12. : Signature Verification using multiple neural classifiers. Pattern Recognition 30(1), 1–7 (1997) 13. : Off-line signature verification and recognition by support vector machine 14. : Representations and metrics for off-line handwriting segmentation 15. : An artificial neural network based segmentation algorithm for off-line handwriting recognition 16.
Face recognition with radial basis function (RBF) neural networks. IEEE Trans. Neural Networks 13(3), 697–710 12. : High speed face recognition based on discrete cosine transform and RBF neural network. IEEE Trans. On Neural Network 16(3), 679–691 (2005) 13. : Image redundancy reduction for neural network classification using discrete cosine transform. In: Proc. Int. Conf. on Neural Network, Italy, vol. 3, pp. 149–154 (2000) 14. : Face Recognition System using Artificial Neural Networks Approach.
Flow Chart of Proposed Methodology The flow chart for the proposed methodology is shown in Fig. 3. e. 2200 Signature Samples) and digitized using scanner, 300 dpi resolution. The first step for pre-processing is Binarization. e. e. in 0 or 1) format. In this paper global threshold is used for this purpose. Noise is filtered out using median filter. Thinning is done after noise filtering. Morphological operation is applied (in MATLAB) to performs the desired thinning. Rotation of signature patterns by a non predictive angle is one of the major difficulties.