Motion processing is an essential piece of the complex brain machinery that allows us to reconstruct the 3D layout of objects in the environment, to break camouflage, to perform scene segmentation, to estimate the ego movement, and to control our action. Although motion perception and its neural basis have been a topic of intensive research and modeling the last two decades, recent experimental evidences have stressed the dynamical aspects of motion integration and segmentation. This book presents the most recent approaches that have changed our view of biological motion processing. These new experimental evidences call for new models emphasizing the collective dynamics of large population of neurons rather than the properties of separate individual filters. Chapters will stress how the dynamics of motion processing can be used as a general approach to understand the brain dynamics itself.

The contributors to this book focus on such key aspects of motion processing as interaction and integration between locally measured motion units, structure from motion, heading in an optical flow, and second-order motion. They also discuss the interaction of motion processing with other high-level visual functions such as surface representation and attention.

Although it is now possible to integrate many millions of transistors on a single chip, traditional digital circuit technology is now reaching its limits, facing problems of cost and technical efficiency when scaled down to ever-smaller feature sizes. The analysis of biological neural systems, especially for visual processing, has allowed engineers to better understand how complex networks can effectively process large amounts of information, whilst dealing with difficult computational challenges. Analog and parallel processing are key characteristics of biological neural networks. Analog VLSI circuits using the same features can therefore be developed to emulate brain-style processing. Using standard CMOS technology, they can be cheaply manufactured, permitting efficient industrial and consumer applications in robotics and mobile electronics. This book explores the theory, design and implementation of analog VLSI circuits, inspired by visual motion processing in biological neural networks. Using a novel approach pioneered by the author himself, Stocker explains in detail the construction of a series of electronic chips, providing the reader with a valuable practical insight into the technology. Analog VLSI Circuits for the Perception of Visual Motion: analyses the computational problems in visual motion perception; examines the issue of optimization in analog networks through high level processes such as motion segmentation and selective attention; demonstrates network implementation in analog VLSI CMOS technology to provide computationally efficient devices; sets out measurements of final hardware implementation; illustrates the similarities of the presented circuits with the human visual motion perception system; includes an accompanying website with video clips of circuits under real-time visual conditions and additional supplementary material. With a complete review of all existing neuromorphic analog VLSI systems for visual motion sensing, Analog VLSI Circuits for the Perception of Visual Motion is a unique reference for advanced students in electrical engineering, artificial intelligence, robotics and computational neuroscience. It will also be useful for researchers, professionals, and electronics engineers working in the field.

For the successful recognition of objective, `real' motion based on visual cues it is necessary to take self-induced motion signals into account, such as those induced by eye-movements. During a series of fMRI studies we measured responses of visual and parietal regions to motion cues derived from (a) retinal motion, (b) eyemovements (visual pursuit) and (c) objective, (real) motion. We show that the recently described cingulate sulcus visual area (CSv) is not, as implied before, primarily driven by 3D self-motion cues but favoured 2D translational coherent motion over 3D expanding flow fields. Further, we found that V3A is capable of integrating retinal motion with eye-movements, thus allowing V3A to respond to object motion independent of retinal motion. This allowed us to define a new functional localizer for area V3A. Finally, we showed that activity in the foveal representation of the early visual cortex is driven by a combination of retinal input and by error signals as hypothesized by of Rao and Ballard (1999) for predictive coding. Taken together, this work provides evidence that regions V3A and CSv are key regions concerning visual self-motion processing and that early visual regions might be modulated by feedback from higher motion processing regions.

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.

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.

As the state-of-the-art imaging technologies became more and more advanced, yielding scientific data at unprecedented detail and volume, the need to process and interpret all the data has made image processing and computer vision increasingly important. Sources of data that have to be routinely dealt with today's applications include video transmission, wireless communication, automatic fingerprint processing, massive databanks, non-weary and accurate automatic airport screening, robust night vision, just to name a few. Multidisciplinary inputs from other disciplines such as physics, computational neuroscience, cognitive science, mathematics, and biology will have a fundamental impact in the progress of imaging and vision sciences. One of the advantages of the study of biological organisms is to devise very different type of computational paradigms by implementing a neural network with a high degree of local connectivity. This is a comprehensive and rigorous reference in the area of biologically motivated vision sensors. The study of biologically visual systems can be considered as a two way avenue. On the one hand, biological organisms can provide a source of inspiration for new computational efficient and robust vision models and on the other hand machine vision approaches can provide new insights for understanding biological visual systems. Along the different chapters, this book covers a wide range of topics from fundamental to more specialized topics, including visual analysis based on a computational level, hardware implementation, and the design of new more advanced vision sensors. The last two sections of the book provide an overview of a few representative applications and current state of the art of the research in this area. This makes it a valuable book for graduate, Master, PhD students and also researchers in the field.