Vision In Motion

Originated from a small workshop on the question of how image motion is processed under natural conditions that was held in 1997 at the Institute of Advanced Studies of the Australian National University in Canberra."--Pref.

Reveals Rollin Becker's deep insight into cranial osteopathy: inspiring generations of practitioners; teaching about the nature of health and healing; and how we can apply this understanding through osteopathic manipulation.

"It is an extension of my previous book, "The new vision" ... "Vision in motion" concentrates on the work of the Institute of Design, Chicago, and presents a broader, more general view of the interrelatedness of art and life."--Foreword

This book on autonomous road-following vehicles brings together twenty years of innovation in the field. The book uniquely details an approach to real-time machine vision for the understanding of dynamic scenes, viewed from a moving platform that begins with spatio-temporal representations of motion for hypothesized objects whose parameters are adjusted by well-known prediction error feedback and recursive estimation techniques.

Mind-body wellness and fitness expert combines mantra, self-reflection, and movement into an accessible 14-day routine for manifesting your best self. Holistic wellness and fitness expert Erin Stutland harnesses all the body's mental, physical, and spiritual energy in her tri-fold approach to creating change. When you move your body while repeating mantras--speaking your desires aloud--manifesting is no longer a purely intellectual exercise or an occasional craft project. Instead, you are expressing your passion through your voice and your body, putting every ounce of your energy in service of what you want. Each chapter breaks down one mantra to use to focus on a key step to achieving your best self, including unearthing your desires, releasing resistance, and taking inspired action. Alongside each mantra, Stutland provides stories from her own life and those of her clients, a meditation or visualization, a journaling exercise, and an easy movement to accompany the mantra to help enhance its resonant power. And to put it all together, you are provided with a 14-day plan so you can design the life you want, infusing the power of movement, mantra, and self-reflection.

Motion perception lies at the heart of the scientific study of vision. The motion aftereffect (MAE) is the appearance of directional movement in a stationary object or scene after the viewer has been exposed to viusal motion in the opposite direction. For example, after one has looked at a waterfall for a period of time, the scene beside the waterfall may appear to move upward when one's gaze is transfered to it. Although the phenomenon seems simple, research has revealed copmlexities in the underlying mechanisms, and offered general lessons about how the brain processes visual information. In the 1990s alone, more than 200 papers have been published on MAE, largely inspired by improved techniques for examining brain electrophysiology and by emerging new theories of motion perception.

Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.