Dynamics Of Visual Motion Processing

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.

The capability to recognize biological motion, i.e. gestures, human actions or face movements is crucial for social interactions, for predators, prey or artifcial systems interacting in a dynamic environment. In this thesis an artifcial feed-forward neural network for biological motion recognition is proposed. Like its natural counterpart, it consists of multiple layers organized in two streams, one for processing static and one for processing dynamic form information. The key component of the proposed system is a novel unsupervised learning algorithn, called VNMF, that is based on sparsity, non-negativity, inhibition and direction selectivity. In the frst layer of the dorsal stream, the VNMF is modifed to solve the optical flow estimation problem. In the subsequent layer the VNMF algorithm extracts prototypical patterns, such as optical flow patterns shaped e.g. as moving heads or lim parts. For the ventral stream the VNMF algorithm learns distict gradient structures, resembling edg...

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.

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.

The brain's ability to detect movement within the retinal image is crucial not only for determining the trajectories of moving objects, but also for identifying and interpreting image motion resulting from eye and head movements. This book summarizes our knowledge of how information about image motion is encoded in the brain. Key Features * Valuable reference source for those involved in the rapidly expanding area of motion perception * Strong emphasis on integration of physiological, computation, and psychophysical approaches * Topics include: * Principles of local motion detection * Inputs to local motion detectors * Integration of motion signals * Higher-order interpretation of motion * Motion detection and eye movements

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.