Foreword

Computer Science, Computer Engineering, Electrical Engineering, Biomedical Engineering, Mechanical Engineering, and Civil Engineering, together with physics, cognitive psychology, evolutionary biology and mathematics have been involved in computer vision to solve many theoretical and practical problems. For example, understanding of human vision as well as vision of terrestrial animals, underground creatures, underwater animals, and flying animals could be very helpful in developing improved computer vision algorithms for various "smart" autonomous machines such as terrestrial vehicles, mobile robots, underground drones, submersibles, and constellations of small satellites, including nano-, pico-, and femto¬satellites. Another fundamental reason for the quest to develop better computer vision is the expanding interest in cognitive dynamical systems. While adaptive control systems constituted the foundation of the previous revolution in systems, cognitive dynamical systems appear to be the next.

This book is right in the center of such developments. It is motivated by the need to solve many fundamental issues in computer vision, including static and moving object segmentation, scene registration and tracking, as well as object classification. Its approach is also matching the requirements of dynamic systems by including not only the three classic stable states (point, cyclic and toroidal stability), but also the fourth stable state, chaos. Under special conditions and driving functions, chaos can develop in a system to affect its behavior either negatively or positively. The positive impact is explored in the book using aperiodic forcing functions to induce temporal chaotic-like behavior (in image sequences) and spatial chaotic-like behavior (in image textures). This approach is of interest to many researchers today. Since the phase-space trajectories (behavior) of chaotic systems is fractal and multi-fractal, their analysis must also be multi-fractal. The book uses this approach to detect chaotic behavior in a system. This approach could also be used to quantify the behavior in order to improve it. Since a multi-fractal analysis is necessarily based on information-theoretic arguments, it often is superior to energy-based approaches in quantification of complexity of systems and their behavior. An example of the use of a unified approach to multi-fractal analysis of phase-space self-affine objects is presented in Chapter 5.

Since this well-crafted and unique book is supported by many examples, illustrations and images, it is easy to read and understand even though it deals with very difficult scientific and technical matters. It should be of interest to many students, researchers and practitioners not only in the area of computer vision, but also to the broader audience of mechatronics, robotics, secure and resilient systems, as well as adaptive, perceptual and cognitive systems.