This book presents original research works by researchers, engineers and practitioners in the field of artificial intelligence and cognitive computing. The book is divided into two parts, the first of which focuses on artificial intelligence (AI), knowledge representation, planning, learning, scheduling, perception-reactive AI systems, evolutionary computing and other topics related to intelligent systems and computational intelligence. In turn, the second part focuses on cognitive computing, cognitive science and cognitive informatics. It also discusses applications of cognitive computing in medical informatics, structural health monitoring, computational intelligence, intelligent control systems, bio-informatics, smart manufacturing, smart grids, image/video processing, video analytics, medical image and signal processing, and knowledge engineering, as well as related applications.

This book describes the design, development, and testing of a novel digital watermarking technique for color images using Magic Square and Ridgelet transforms. The novel feature of the method is that it generates and uses multiple copies of the digital watermark. The book describes how the method was tested for embedding digital watermarks into color cover images, resulting in very high PSNR value and yielding comparable results with existing watermarking techniques.To reach this new method, eight different techniques are designed, developed and tested. First, the authors test two digital watermarking techniques based on encryption: Image Watermark Using Complete Complementary Code Technique (CCCT) and Image Watermarking Using CCC-Fast Walsh Hadamard Transform Technique (CCC-FWHTT). Next, four digital watermarking techniques based on curvelet transforms are discussed: Image Watermarking Using Curvelet Transform (WCT), Watermark Wavelets in Curvelets of Cover Image (WWCT), Resized Watermark into Curvelets of Cover Image (RWCT), and Resized Watermark Wavelets into Curvelets of Cover Image (RWWCT). Then, two final techniques are presented: Image Watermarking Based on Magic Square (MST) and Image watermarking based on Magic Square and Ridgelet Transform (MSRTT). Future research directions are explored in the final chapter.Designed for professionals and researchers in computer graphics and imaging, Digital Watermarking Techniques in Curvelet and Ridgelet Domain is also a useful tool for advanced-level students.

This work presents a review of different indexing techniques designed to enhance the speed and efficiency of searches over large biometric databases. The coverage includes an extended Delaunay triangulation-based approach for fingerprint biometrics, involving a classification based on the type of minutiae at the vertices of each triangle. This classification is demonstrated to provide improved partitioning of the database, leading to a significant decrease in the number of potential matches during identification. This discussion is then followed by a description of a second indexing technique, which sorts biometric images based on match scores calculated against a set of pre-selected sample images, resulting in a rapid search regardless of the size of the database. The text also examines a novel clustering-based approach to indexing with decision-level fusion, using an adaptive clustering algorithm to compute a set of clusters represented by a 'leader' image, and then determining the index code from the set of leaders. This is shown to improve identification performance while using minimal resources.