Image, video and 3D data registration: medical, satellite and video processing applications with quality metrics

Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods. This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state–of–the–art registration methodologies used in a variety of targeted applications.

Development of novel antibacterial active, HaCaT biocompatible and biodegradable CA-g-P(3HB)-EC biocomposites with caffeic acid as a functional entity

We have developed novel composites by grafting caffeic acid (CA) onto the P(3HB)-EC based material and laccase from Trametes versicolor was used for grafting purposes. The resulting composites were designated as CA-g-P(3HB)-EC i.e., P(3HB)-EC (control), 5CA-g-P(3HB)-EC, 10CA-g-P(3HB)-EC, 15CA-g-P(3HB)-EC and 20CA-g-P(3HB)-EC. An FT-IR (Fourier-transform infrared spectroscopy) was used to examine the functional and elemental groups of the control and laccase-assisted graft composites. Evidently, 15CA-g-P(3HB)-EC composite exhibited resilient antibacterial activity against Gram-positive and Gram-negative bacterial strains, respectively. Moreover, a significant level of biocompatibility and biodegradability of the CA-g-P(3HB)-EC composites was also achieved with the human keratinocytes-like HaCaT cells and soil burial evaluation, respectively. In conclusion, the newly developed novel composites with multi characteristics could well represent the new wave of biomaterials for medical applications, and more specifically have promising future in the infection free would dressings, burn and/or skin regeneration field due to their sophisticated characteristics.

Secure and Scalable Statistical Computation of Questionnaire Data in R

Collecting data via a questionnaire and analyzing them while preserving respondents’ privacy may increase the number of respondents and the truthfulness of their responses. It may also reduce the systematic differences between respondents and non-respondents. In this paper, we propose a privacy-preserving method for collecting and analyzing survey responses using secure multi-party computation (SMC). The method is secure under the semi-honest adversarial model. The proposed method computes a wide variety of statistics. Total and stratified statistical counts are computed using the secure protocols developed in this paper. Then, additional statistics, such as a contingency table, a chi-square test, an odds ratio, and logistic regression, are computed within the R statistical environment using the statistical counts as building blocks. The method was evaluated on a questionnaire dataset of 3,158 respondents sampled for a medical study and simulated questionnaire datasets of up to 50,000 respondents. The computation time for the statistical analyses linearly scales as the number of respondents increases. The results show that the method is efficient and scalable for practical use. It can also be used for other applications in which categorical data are collected.