Dimitrios Hatzinakos – författare

SmartData empowers personal data by wrapping it in a cloak of intelligence such that it now becomes the individual’s virtual proxy in cyberspace. No longer will personal data be shared or stored in the cloud as merely data, encrypted or otherwise; it will now be stored and shared as a constituent of the binary string specifying the entire SmartData agent. This agent proactively builds-in privacy, security and user preferences, right from the outset, not as an afterthought.SmartData: Privacy Meets Evolutionary Robotics includes the advances made in the technology of simulating virtual worlds, together with the ideas emerging from fields of evolutionary robotics and embodied cognition within a framework of dynamical systems as an approach toward this ultimate goal. The book brings together top researchers in the field and addresses current personal data privacy challenges in the online-world.

SmartData empowers personal data by wrapping it in a cloak of intelligence such that it now becomes the individual’s virtual proxy in cyberspace. No longer will personal data be shared or stored in the cloud as merely data, encrypted or otherwise; it will now be stored and shared as a constituent of the binary string specifying the entire SmartData agent. This agent proactively builds-in privacy, security and user preferences, right from the outset, not as an afterthought.SmartData: Privacy Meets Evolutionary Robotics includes the advances made in the technology of simulating virtual worlds, together with the ideas emerging from fields of evolutionary robotics and embodied cognition within a framework of dynamical systems as an approach toward this ultimate goal. The book brings together top researchers in the field and addresses current personal data privacy challenges in the online-world.

SmartData empowers personal data by wrapping it in a cloak of intelligence such that it now becomes the individual’s virtual proxy in cyberspace. No longer will personal data be shared or stored in the cloud as merely data, encrypted or otherwise; it will now be stored and shared as a constituent of the binary string specifying the entire SmartData agent. This agent proactively builds-in privacy, security and user preferences, right from the outset, not as an afterthought.SmartData: Privacy Meets Evolutionary Robotics includes the advances made in the technology of simulating virtual worlds, together with the ideas emerging from fields of evolutionary robotics and embodied cognition within a framework of dynamical systems as an approach toward this ultimate goal. The book brings together top researchers in the field and addresses current personal data privacy challenges in the online-world.

Instead of focusing on the mathematical properties of moments, this book is a compendium of research that demonstrates the effectiveness of orthogonal moment-based features in face recognition, expression recognition, fingerprint recognition and iris recognition. The usefulness of moments and their invariants in pattern recognition is well known. What is less well known is how orthogonal moments may be applied to specific problems in human-centric visual pattern recognition. Unlike previous books, this work highlights the fundamental issues involved in moment-based pattern recognition, from the selection of discriminative features in a high-dimensional setting, to addressing the question of how to classify a large number of patterns based on small training samples. In addition to offering new concepts that illustrate the use of statistical methods in addressing some of these issues, the book presents recent results and provides guidance on implementing the methods. Accordingly, it will be of interest to researchers and graduate students working in the broad areas of computer vision and visual pattern recognition.

Instead of focusing on the mathematical properties of moments, this book is a compendium of research that demonstrates the effectiveness of orthogonal moment-based features in face recognition, expression recognition, fingerprint recognition and iris recognition. The usefulness of moments and their invariants in pattern recognition is well known. What is less well known is how orthogonal moments may be applied to specific problems in human-centric visual pattern recognition. Unlike previous books, this work highlights the fundamental issues involved in moment-based pattern recognition, from the selection of discriminative features in a high-dimensional setting, to addressing the question of how to classify a large number of patterns based on small training samples. In addition to offering new concepts that illustrate the use of statistical methods in addressing some of these issues, the book presents recent results and provides guidance on implementing the methods. Accordingly, it will be of interest to researchers and graduate students working in the broad areas of computer vision and visual pattern recognition.