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Fourier Vision provides a new treatment of figure-ground segmentation in scenes comprising transparent, translucent, or opaque objects. Exploiting the relative motion between figure and ground, this technique deals explicitly with the separation of additive signals and makes no assumptions about the spatial or spectral content of the images, with segmentation being carried out phasor by phasor in the Fourier domain. It works with several camera configurations, such as camera motion and short-baseline binocular stereo, and performs best on images with small velocities/displacements, typically one to ten pixels per frame. The book also addresses the use of Fourier techniques to estimate stereo disparity and optical flow. Numerous examples are provided throughout. Fourier Vision will be of value to researchers in image processing & computer vision and, especially, to those who have to deal with superimposed transparent or translucent objects. Researchers in application areas such as medical imaging and acoustic signal processing will also find this of interest.
Fourier Vision provides a new treatment of figure-ground segmentation in scenes comprising transparent, translucent, or opaque objects. Exploiting the relative motion between figure and ground, this technique deals explicitly with the separation of additive signals and makes no assumptions about the spatial or spectral content of the images, with segmentation being carried out phasor by phasor in the Fourier domain. It works with several camera configurations, such as camera motion and short-baseline binocular stereo, and performs best on images with small velocities/displacements, typically one to ten pixels per frame. The book also addresses the use of Fourier techniques to estimate stereo disparity and optical flow. Numerous examples are provided throughout. Fourier Vision will be of value to researchers in image processing & computer vision and, especially, to those who have to deal with superimposed transparent or translucent objects. Researchers in application areas such as medical imaging and acoustic signal processing will also find this of interest.
Fourier-Domain optical coherence tomography (OCT) is the latest technology available to provide high-speed, high-resolution imaging of the cornea, anterior chamber angle, macula, and optic nerve head. It is uniquely suited for ophthalmologists that treat diseases from the front to the back of the eye. Inside Imaging the Eye from Front to Back with RTVue Fourier-Domain Optical Coherence Tomography, Drs. David Huang, Jay S. Duker, James G. Fujimoto, Bruno Lumbroso, Joel S. Schuman, and Robert N. Weinreb cover up-to-date OCT technology and diagnostic software of the RTVue. It is the first book that covers clinical applications from the front to the back of the eye, as opposed to concentrating either on posterior segment imaging or anterior segment imaging. Inside you'll find: * Explanation of Fourier-Domain OCT technology * Teaching of scan pattern selection * Step-by-step instruction for scan acquisition * Teaching of the interpretations of OCT images and measurements by many case examples * Interpretation of en face images Some chapters covered: * Anterior segment * Cornea * Posterior segment * Retina * Age-related macular degeneration * Diabetic retinopathy * Glaucoma Imaging the Eye from Front to Back with RTVue Fourier-Domain Optical Coherence Tomography is the must-have book for general ophthalmologists, glaucoma specialists, retina specialists, cornea specialists, and refractive surgeons, as well as biomedical engineers.
The purpose of robot vision is to enable robots to perceive the external world in order to perform a large range of tasks such as navigation, visual servoing for object tracking and manipulation, object recognition and categorization, surveillance, and higher-level decision-making. Among different perceptual modalities, vision is arguably the most important one. It is therefore an essential building block of a cognitive robot. This book presents a snapshot of the wide variety of work in robot vision that is currently going on in different parts of the world.
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
This volume presents an integrated view of how we perceive the spatial relations in our visual world, covering anatomical, physiological, psychophysical, and perceptual aspects. The authors discuss the visual system primarily in terms of spatial frequency analysis using a linear systems approach. They review evidence supporting a local, patch-by-patch spatial frequency filtering of visual information rather than the global Fourier analysis other researchers have proposed. A separate chapter addresses the special issues surrounding color vision, and a brief, nonmathematical introduction to linear systems analysis is included for the uninitiated reader.
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.
Recent vision research has led to the emergence of new techniques that offer exciting potential for a more complete assessment of vision in clinical, industrial, and military settings. Emergent Techniques for Assessment of Visual Performance examines four areas of vision testing that offer potential for improved assessment of visual capability including: contrast sensitivity function, dark-focus of accommodation, dynamic visual acuity and dynamic depth tracking, and ambient and focal vision. In contrast to studies of accepted practices, this report focuses on emerging techniques that could help determine whether people have the vision necessary to do their jobs. In addition to examining some of these emerging techniques, the report identifies their usefulness in predicting performance on other visual and visual-motor tasks, and makes recommendations for future research. Emergent Techniques for Assessment of Visual Performance provides summary recommendations for research that will have significant value and policy implications for the next 5 to 10 years. The content and conclusions of this report can serve as a useful resource for those responsible for screening industrial and military visual function.
Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the exemplar code of the algorithms." Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended. - Named a 2012 Notable Computer Book for Computing Methodologies by Computing Reviews - Essential reading for engineers and students working in this cutting-edge field - Ideal module text and background reference for courses in image processing and computer vision - The only currently available text to concentrate on feature extraction with working implementation and worked through derivation
This book constitutes the refereed proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2008, held in Juan-les-Pins, France, in October 2008. The 33 revised full papers and 69 posters presented were carefully reviewed and selected from 179 submissions. The papers are organized in topical sections on image and video coding; systems and applications; video processing; filtering and restoration; segmentation and feature extraction; tracking, scene understanding and computer vision; medical imaging; and biometrics and surveillance.