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Optic flow provides all the information necessary to guide a walking human or a mobile robot to its target. Over the past 50 years, a body of research on optic flow spanning the disciplines of neurophysiology, psychophysics, experimental psychology, brain imaging and computational modelling has accumulated. Today, when we survey the field, we find independent lines of research have now converged and many arguments have been resolved; simultaneously the underpinning assumptions of flow theory are being questioned and alternative accounts of the visual guidance of locomotion proposed. At this critical juncture, this volume offers a timely review of what has been learnt and pointers to where the field is going.
The four-volume set comprising LNCS volumes 3021/3022/3023/3024 constitutes the refereed proceedings of the 8th European Conference on Computer Vision, ECCV 2004, held in Prague, Czech Republic, in May 2004. The 190 revised papers presented were carefully reviewed and selected from a total of 555 papers submitted. The four books span the entire range of current issues in computer vision. The papers are organized in topical sections on tracking; feature-based object detection and recognition; geometry; texture; learning and recognition; information-based image processing; scale space, flow, and restoration; 2D shape detection and recognition; and 3D shape representation and reconstruction.
Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phase based (or frequency domain) methods; correlation based (or area) methods; feature point (or sparse data) tracking methods; In this chapter we compute the optic flow as a dense optic flow field with a multi scale differential method. The method, originally proposed by Florack and Nielsen [Florack1998a] is known as the Multiscale Optic Flow Constrain Equation (MOFCE). This is a scale space version of the well known computer vision implementation of the optic flow constraint equation, as originally proposed by Horn and Schunck [Horn1981]. This scale space variation, as usual, consists of the introduction of the aperture of the observation in the process. The application to stereo has been described by Maas et al. [Maas 1995a, Maas 1996a]. Of course, difficulties arise when structure emerges or disappears, such as with occlusion, cloud formation etc. Then knowledge is needed about the processes and objects involved. In this chapter we focus on the scale space approach to the local measurement of optic flow, as we may expect the visual front end to do. 17. 2 Motion detection with pairs of receptive fields As a biologically motivated start, we begin with discussing some neurophysiological findings in the visual system with respect to motion detection.
This book constitutes the thoroughly refereed post-conference proceedings of the International Dagstuhl-Seminar on Statistical and Geometrical Approaches to Visual Motion Analysis, held in Dagstuhl Castle, Germany, in July 2008. The workshop focused on critical aspects of motion analysis, including motion segmentation and the modeling of motion patterns. The aim was to gather researchers who are experts in the different motion tasks and in the different techniques used; also involved were experts in the study of human and primate vision. The 15 revised full papers presented were carefully reviewed and selected from or initiated by the lectures given at the workshop. The papers are organized in topical sections on optical flow and extensions, human motion modeling, biological and statistical approaches, alternative approaches to motion analysis.
Abstract Biological vision is a rather fascinating domain of research. Scientists of various origins like biology, medicine, neurophysiology, engineering, math ematics, etc. aim to understand the processes leading to visual perception process and at reproducing such systems. Understanding the environment is most of the time done through visual perception which appears to be one of the most fundamental sensory abilities in humans and therefore a significant amount of research effort has been dedicated towards modelling and repro ducing human visual abilities. Mathematical methods play a central role in this endeavour. Introduction David Marr's theory v^as a pioneering step tov^ards understanding visual percep tion. In his view human vision was based on a complete surface reconstruction of the environment that was then used to address visual subtasks. This approach was proven to be insufficient by neuro-biologists and complementary ideas from statistical pattern recognition and artificial intelligence were introduced to bet ter address the visual perception problem. In this framework visual perception is represented by a set of actions and rules connecting these actions. The emerg ing concept of active vision consists of a selective visual perception paradigm that is basically equivalent to recovering from the environment the minimal piece information required to address a particular task of interest.
We are proud to present the DAGM 2002 proceedings, which are the result of the e?orts of many people. First, there are the many authors, who have submitted so many excellent cont- butions. We received more than 140 papers, of which we could only accept about half in order not to overload the program. Only about one in seven submitted papers could be delivered as an oral presentation, for the same reason. But it needs to be said that almost all submissions were of a really high quality. This strong program could not have been put together without the support of the Program Committee. They took their responsibility most seriously and we are very grateful for their reviewing work, which certainly took more time than anticipated, given the larger than usual number of submissions. Our three invited speakers added a strong multidisciplinary component to the conference. Dr. Antonio Criminisi of Microsoft Research (Redmond, USA) dem- strated how computer vision can literally bring a new dimension to the app- ciation of art. Prof. Philippe Schyns (Dept. of Psychology, Univ. of Glasgow, UK) presented intriguing insights into the human perception of patterns, e.g., the role of scale. Complementary to this presentation, Prof. Manabu Tanifuji of the Brain Science Institute in Japan (Riken) discussed novel neurophysiological ?ndings on how the brain deals with the recognition of objects and their parts.
When we walk, drive a car, or fly an airplane, visual motion is used to control and guide our movement. Optic flow describes the characteristic pattern of visual motion that arises in these situations. This book is the first to take an in-depth look at the neuronal processing strategies that underlie the brain's ability to analyze and use optic flow for the control of self-motion. It does so in a variety of species which use optic flow in different behavioral contexts. The spectrum ranges from flying insects to birds, higher mammals and man. The contributions cover physiological and behavioral studies as well as computational models. Neuronal Processing of Optic Flow provides an authoritative and comprehensive overview of the current state of research on this topic written by a group of authors who have made essential contributions to shaping this field of research over the last ten years. - Provides the first detailed overview of the analysis of complex visual motion patterns in the brain - Includes physiological, behavioral, and computational aspects of optic flow processing - Highlights similarities and differences between different animal species and behavioral tasks - Covers human patients with visual motion deficits - Enhances the reader's understanding with many illustrations
This open access book provides a comprehensive overview of the application of the newest laser and microscope/ophthalmoscope technology in the field of high resolution imaging in microscopy and ophthalmology. Starting by describing High-Resolution 3D Light Microscopy with STED and RESOLFT, the book goes on to cover retinal and anterior segment imaging and image-guided treatment and also discusses the development of adaptive optics in vision science and ophthalmology. Using an interdisciplinary approach, the reader will learn about the latest developments and most up to date technology in the field and how these translate to a medical setting. High Resolution Imaging in Microscopy and Ophthalmology – New Frontiers in Biomedical Optics has been written by leading experts in the field and offers insights on engineering, biology, and medicine, thus being a valuable addition for scientists, engineers, and clinicians with technical and medical interest who would like to understand the equipment, the applications and the medical/biological background. Lastly, this book is dedicated to the memory of Dr. Gerhard Zinser, co-founder of Heidelberg Engineering GmbH, a scientist, a husband, a brother, a colleague, and a friend.