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3D computer vision: efficient methods and applications
Hoogs, and J. Snape, S. Guy, J. Long, X. Wang, G. Hua, M. Xu, D. Song, and A. Dawkins and A. Yongmian Zhang, Yifan Zhang, E. Porikli, F. Bremond, S. Dockstader, J. Sie finden die entsprechenden Informationen in der Detailansicht des jeweiligen Titels. Umfang: S. Erschienen am Given one or typically more images of a scene, or a video, scene reconstruction aims at computing a 3D model of the scene.
In the simplest case the model can be a set of 3D points. More sophisticated methods produce a complete 3D surface model. The advent of 3D imaging not requiring motion or scanning, and related processing algorithms is enabling rapid advances in this field. Grid-based 3D sensing can be used to acquire 3D images from multiple angles. Algorithms are now available to stitch multiple 3D images together into point clouds and 3D models.
The aim of image restoration is the removal of noise sensor noise, motion blur, etc. The simplest possible approach for noise removal is various types of filters such as low-pass filters or median filters. More sophisticated methods assume a model of how the local image structures look, to distinguish them from noise. By first analysing the image data in terms of the local image structures, such as lines or edges, and then controlling the filtering based on local information from the analysis step, a better level of noise removal is usually obtained compared to the simpler approaches.
The organization of a computer vision system is highly application-dependent. Some systems are stand-alone applications that solve a specific measurement or detection problem, while others constitute a sub-system of a larger design which, for example, also contains sub-systems for control of mechanical actuators, planning, information databases, man-machine interfaces, etc.
The specific implementation of a computer vision system also depends on whether its functionality is pre-specified or if some part of it can be learned or modified during operation. Many functions are unique to the application. There are, however, typical functions that are found in many computer vision systems. Image-understanding systems IUS include three levels of abstraction as follows: low level includes image primitives such as edges, texture elements, or regions; intermediate level includes boundaries, surfaces and volumes; and high level includes objects, scenes, or events.
Many of these requirements are really topics for further research. The representational requirements in the designing of IUS for these levels are: representation of prototypical concepts, concept organization, spatial knowledge, temporal knowledge, scaling, and description by comparison and differentiation. While inference refers to the process of deriving new, not explicitly represented facts from currently known facts, control refers to the process that selects which of the many inference, search, and matching techniques should be applied at a particular stage of processing.
Inference and control requirements for IUS are: search and hypothesis activation, matching and hypothesis testing, generation and use of expectations, change and focus of attention, certainty and strength of belief, inference and goal satisfaction. There are many kinds of computer vision systems; however, all of them contain these basic elements: a power source, at least one image acquisition device camera, ccd, etc. In addition, a practical vision system contains software, as well as a display in order to monitor the system. Vision systems for inner spaces, as most industrial ones, contain an illumination system and may be placed in a controlled environment.
Furthermore, a completed system includes many accessories such as camera supports, cables and connectors. Most computer vision systems use visible-light cameras passively viewing a scene at frame rates of at most 60 frames per second usually far slower. A few computer vision systems use image-acquisition hardware with active illumination or something other than visible light or both, such as structured-light 3D scanners , thermographic cameras , hyperspectral imagers , radar imaging , lidar scanners, magnetic resonance images , side-scan sonar , synthetic aperture sonar , etc.
Such hardware captures "images" that are then processed often using the same computer vision algorithms used to process visible-light images. While traditional broadcast and consumer video systems operate at a rate of 30 frames per second, advances in digital signal processing and consumer graphics hardware has made high-speed image acquisition, processing, and display possible for real-time systems on the order of hundreds to thousands of frames per second. For applications in robotics, fast, real-time video systems are critically important and often can simplify the processing needed for certain algorithms.
When combined with a high-speed projector, fast image acquisition allows 3D measurement and feature tracking to be realised. Egocentric vision systems are composed of a wearable camera that automatically take pictures from a first-person perspective. As of , vision processing units are emerging as a new class of processor, to complement CPUs and graphics processing units GPUs in this role. From Wikipedia, the free encyclopedia. Play media. Ballard; Christopher M. Brown Computer Vision. Prentice Hall. Vandoni, Carlo, E ed. Geneva: CERN. Image Processing, Analysis, and Machine Vision.
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