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Titlebook: Computer Vision; Detection, Recogniti Roberto Cipolla,Sebastiano Battiato,Giovanni Maria Book 20101st edition Springer-Verlag Berlin Heidel

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发表于 2025-3-21 16:40:00 | 显示全部楼层 |阅读模式
书目名称Computer Vision
副标题Detection, Recogniti
编辑Roberto Cipolla,Sebastiano Battiato,Giovanni Maria
视频video
概述Recent Results in Detection, Recognition and Reconstruction in Computer Vision.Writte by experts in this field
丛书名称Studies in Computational Intelligence
图书封面Titlebook: Computer Vision; Detection, Recogniti Roberto Cipolla,Sebastiano Battiato,Giovanni Maria Book 20101st edition Springer-Verlag Berlin Heidel
描述Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout.The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvssThis edited volume contains a selection of articles covering some of the talks and tutorials held during the first two editions of the school on topics such as Recognition, Registration and Reconstruction. The chapters provide an in-depth overview of these chall
出版日期Book 20101st edition
关键词Computational Intelligence; Computer Vision; Detection; Ground Truth; Layout; Recognition; Reconstruction;
版次1
doihttps://doi.org/10.1007/978-3-642-12848-6
isbn_softcover978-3-662-50556-4
isbn_ebook978-3-642-12848-6Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2010
The information of publication is updating

书目名称Computer Vision影响因子(影响力)




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书目名称Computer Vision读者反馈学科排名




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Discriminative Graphical Models for Context-Based Classification,tandard binary CRFs to handle problems with multiclass labels or hierarchical context are also discussed. Finally, application of CRFs on contextual object detection, scene segmentation and texture recognition tasks is demonstrated.
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Towards a New Regional Policy in Spain, track-before-detect approach that consistently detects and recognizes multiple simultaneous objects in a common view, based on motion models. This approach estimates the temporal evolution of objects from noisy data, given their motion model, without an explicit object detection stage.
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The Need for an Anthropology of Wealthimation from images, and surface extraction from photo-consistency. In this Chapter we will put more emphasis on the latter two: namely how to extract geometric information from a set of photographs without explicit camera visibility, and how to combine different geometry estimates in an optimal way.
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https://doi.org/10.1007/978-3-031-34370-4with the aim to provide an answer to such questions. Does machine vision still have anything to learn from human vision? I identify a number of basic principles of biological vision that are likely to be of interest to the machine vision community.
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https://doi.org/10.1007/978-3-031-34370-4 a novel mathematical characterisation of the co-variance properties of the features which accounts for deviation from the usual idealised image affine (de)formation model. We propose novel metrics to evaluate the features and we show how these can be used to automatically design improved features.
发表于 2025-3-23 03:17:53 | 显示全部楼层
Is Human Vision Any Good?,with the aim to provide an answer to such questions. Does machine vision still have anything to learn from human vision? I identify a number of basic principles of biological vision that are likely to be of interest to the machine vision community.
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Knowing a Good Feature When You See It: Ground Truth and Methodology to Evaluate Local Features for a novel mathematical characterisation of the co-variance properties of the features which accounts for deviation from the usual idealised image affine (de)formation model. We propose novel metrics to evaluate the features and we show how these can be used to automatically design improved features.
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