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

外表

書目名稱Computer Vision影響因子(影響力)




書目名稱Computer Vision影響因子(影響力)學科排名




書目名稱Computer Vision網(wǎng)絡(luò)公開度




書目名稱Computer Vision網(wǎng)絡(luò)公開度學科排名




書目名稱Computer Vision被引頻次




書目名稱Computer Vision被引頻次學科排名




書目名稱Computer Vision年度引用




書目名稱Computer Vision年度引用學科排名




書目名稱Computer Vision讀者反饋




書目名稱Computer Vision讀者反饋學科排名

Glaci冰

付出

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.

OVER

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.

偽證

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.

全面

全面

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.

ETCH

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.

太空

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.

dragon

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.