Moments as projections of an image’s intensity onto a proper polynomial basis can be applied to many different aspects of image processing. These include invariant pattern recognition, image normalization, image registration, focus/ defocus measurement, and watermarking. This book presents a survey of both recent and traditional image analysis and pattern recognition methods, based on image moments, and offers new concepts of invariants to linear filtering and implicit invariants. In addition to the theory, attention is paid to efficient algorithms for moment computation in a discrete domain, and to computational aspects of orthogonal moments. The authors also illustrate the theory through practical examples, demonstrating moment invariants in real applications across computer vision, remote sensing and medical imaging. Key features: Presents a systematic review of the basic definitions and properties of moments covering geometric moments and complex moments. Considers invariants to traditional transforms – translation, rotation, scaling, and affine transform - from a new point of view, which offers new possibilities of designing optimal sets of invariants. Reviews and extends a recent field of invariants with respect to convolution/blurring. Introduces implicit moment invariants as a tool for recognizing elastically deformed objects. Compares various classes of orthogonal moments (Legendre, Zernike, Fourier-Mellin, Chebyshev, among others) and demonstrates their application to image reconstruction from moments. Offers comprehensive advice on the construction of various invariants illustrated with practical examples. Includes an accompanying website providing efficient numerical algorithms for moment computation and for constructing invariants of various kinds, with about 250 slides suitable for a graduate university course. Moments and Moment Invariants in Pattern Recognition is ideal for researchers and engineers involved in pattern recognition in medical imaging, remote sensing, robotics and computer vision. Post graduate students in image processing and pattern recognition will also find the book of interest.

The New York Times bestselling authors of Switch and Made to Stick explore why certain brief experiences can jolt us and elevate us and change us—and how we can learn to create such extraordinary moments in our life and work. While human lives are endlessly variable, our most memorable positive moments are dominated by four elements: elevation, insight, pride, and connection. If we embrace these elements, we can conjure more moments that matter. What if a teacher could design a lesson that he knew his students would remember twenty years later? What if a manager knew how to create an experience that would delight customers? What if you had a better sense of how to create memories that matter for your children? This book delves into some fascinating mysteries of experience: Why we tend to remember the best or worst moment of an experience, as well as the last moment, and forget the rest. Why “we feel most comfortable when things are certain, but we feel most alive when they’re not.” And why our most cherished memories are clustered into a brief period during our youth. Readers discover how brief experiences can change lives, such as the experiment in which two strangers meet in a room, and forty-five minutes later, they leave as best friends. (What happens in that time?) Or the tale of the world’s youngest female billionaire, who credits her resilience to something her father asked the family at the dinner table. (What was that simple question?) Many of the defining moments in our lives are the result of accident or luck—but why would we leave our most meaningful, memorable moments to chance when we can create them? The Power of Moments shows us how to be the author of richer experiences.

In How to Read a Moment, Mathias Nilges shows that time is inseparable from the stories we tell about it, demonstrating that the contemporary American novel offers new ways to make sense of the temporality that governs our present. “Time is a thing that grows scarcer every day,” observes one of Don DeLillo’s characters. “The future is gone,” The Baffler argues. “Where’s my hoverboard!?” a meme demands. Contemporary capitalism, a system that insists that everything happen at once, creates problems for social thought and narrative alike. After all, how does one tell the time of instantaneity? In this moment of on-demand service and instant trading, it has become difficult to imagine the future. The novel emerged as the art form of a rapidly changing modern world, a way of telling time in its progress. Nilges argues that this historical mission is renewed today through works that understand contemporaneity as a form of time shaping that props up our material world and cultural imagination. But the contemporary American novel does not simply associate our present with a crisis of futurity. Through analyses of works by authors such as DeLillo, Jennifer Egan, Charles Yu, and Colson Whitehead, Nilges illustrates that the novel presents ways to make sense of the temporality that controls our purportedly fully contemporary world. In so doing, the novel recovers a sense of possibility and hope, forwarding a dazzling argument for its own importance today.

In Disrupt Yourself, innovator and digital media expert Jay Samit reveals how to achieve your goals and permanently alter the status quo through the art of self-disruption. In today's ever-changing and often-volatile business landscape, adaptability and creativity are more crucial than ever. Samit describes how specific strategies that help companies flourish - challenging assumptions, pinpointing one's unique value, and identifying weaknesses in the structure of current industries - can be applied at an individual level. Incorporating stories from his own experience and anecdotes from other innovators and disruptive businesses - including Richard Branson, Steve Jobs, YouTube, the BBC, Virgin Media and many more - Samit shows how personal transformation can reap entrepreneurial and professional rewards. Disrupt Yourself offers clear and empowering advice for anyone looking to break through a creative barrier; anyone with a big idea but no idea how to apply it; and for anyone worried about being made irrelevant in an era of technological transformation. This engaging, perspective-shifting book demystifies the mechanics of disruption for individuals and businesses alike.

Presents recent significant and rapid development in the field of 2D and 3D image analysis 2D and 3D Image Analysis by Moments, is a unique compendium of moment-based image analysis which includes traditional methods and also reflects the latest development of the field. The book presents a survey of 2D and 3D moment invariants with respect to similarity and affine spatial transformations and to image blurring and smoothing by various filters. The book comprehensively describes the mathematical background and theorems about the invariants but a large part is also devoted to practical usage of moments. Applications from various fields of computer vision, remote sensing, medical imaging, image retrieval, watermarking, and forensic analysis are demonstrated. Attention is also paid to efficient algorithms of moment computation. Key features: Presents a systematic overview of moment-based features used in 2D and 3D image analysis. Demonstrates invariant properties of moments with respect to various spatial and intensity transformations. Reviews and compares several orthogonal polynomials and respective moments. Describes efficient numerical algorithms for moment computation. It is a "classroom ready" textbook with a self-contained introduction to classifier design. The accompanying website contains around 300 lecture slides, Matlab codes, complete lists of the invariants, test images, and other supplementary material. 2D and 3D Image Analysis by Moments, is ideal for mathematicians, computer scientists, engineers, software developers, and Ph.D students involved in image analysis and recognition. Due to the addition of two introductory chapters on classifier design, the book may also serve as a self-contained textbook for graduate university courses on object recognition.