Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications

Applying biometrics to daily scenarios involves demanding requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are far from meeting the previous stated requirements. In fact, achieving a combination of both necessities is one of the most difficult problems at present in biometrics. Therefore, this paper presents a segmentation algorithm able to provide suitable solutions in terms of precision for hand biometric recognition, considering a wide range of backgrounds like carpets, glass, grass, mud, pavement, plastic, tiles or wood. Results highlight that segmentation accuracy is carried out with high rates of precision (F-measure 88%)), presenting competitive time results when compared to state-of-the-art segmentation algorithms time performance

Some examples of the simultaneous use of closed-form and numerical solutions in shell structural analysis

The computational advantages of the use of different approaches -numerical and analytical ones- to the analysis of different parts of the same shell structure are discussed. Examples of large size problems that can be reduced to those more suitable to be handled by a personal related axisyrometric finite elements, local unaxisymmetric shells, geometric quasi-regular shells, infinite elements and homogenization techniques are described