Read e-book online Modelling and Implementation of Complex Systems: Proceedings PDF

By Salim Chikhi, Abdelmalek Amine, Allaoua Chaoui, Mohamed Khireddine Kholladi, Djamel Eddine Saidouni

This quantity is a complete number of prolonged contributions from the fourth foreign symposium on Modeling and imposing advanced structures (MICS’2106) geared up into 4 major themes: Networking and Cloud Computing, software program Engineering and Formal equipment, clever and data structures, and Algorithms and Complexity. This publication offers contemporary advances regarding idea and functions of networking and dispensed computing, together with: cloud computing, software program engineering, formal equipment, info extraction, optimization algorithms, clever structures, and multi-agent systems.

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Read or Download Modelling and Implementation of Complex Systems: Proceedings of the 4th International Symposium, MISC 2016, Constantine, Algeria, May 7-8, 2016, Constantine, Algeria PDF

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Additional info for Modelling and Implementation of Complex Systems: Proceedings of the 4th International Symposium, MISC 2016, Constantine, Algeria, May 7-8, 2016, Constantine, Algeria

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In low resolution images features such as principal lines, wrinkles and textures can be extracted with less than 100 dpi (dots per inch) [3]. However, features like minutiae points, ridges and singular points can be obtained from high resolution images with at least 400 dpi [3]. In general, high resolution images are suitable for forensic applications such as criminal detection, while low resolution images essential for civil and commercial applications such as access control. Initially palmprint research focused on high-resolution images but now almost all research is on low resolution images for civil and commercial applications [4–6].

This degree of similarity is called local quality score LQS. To compute the LQS, we propose to calculate the similarity between the gray level histogram hT of the test block and the gray level histogram of the reference block hR using the following formula: 255 LQS = dðhT , hR Þ = ∑ min½hT ðiÞ, hR ðiފ ð2Þ i=0 where hT(i) and hR(i) are the values of the test and reference block histograms at the gray level i. 255 The LQS takes its value in the interval [0, L] where L = ∑ hR ðiÞ, 0 means a poor i=0 quality block, while L means good quality block.

The biometric image acquired from this step may undergo a number of distortions such as poor illumination on a face, cuts on a fingerprint and reflections in an iris. This may significantly decrease the performances of the biometric systems. In order to face up to this problem, researchers have recently defined the concept of the biometric image quality measurement. It is useful in different settings (enrolment, identification and verification) as well as in different processing phases (pre-processing, matching and decision) of a biometric system [8].

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