Titlebook: Advances in Visual Computing; 13th International S George Bebis,Richard Boyle,Jonathan Ventura Conference proceedings 2018 Springer Nature

Wilder

书目名称Advances in Visual Computing影响因子(影响力)




书目名称Advances in Visual Computing影响因子(影响力)学科排名




书目名称Advances in Visual Computing网络公开度




书目名称Advances in Visual Computing网络公开度学科排名




书目名称Advances in Visual Computing被引频次




书目名称Advances in Visual Computing被引频次学科排名




书目名称Advances in Visual Computing年度引用




书目名称Advances in Visual Computing年度引用学科排名




书目名称Advances in Visual Computing读者反馈




书目名称Advances in Visual Computing读者反馈学科排名

happiness

DOTE

作茧自缚

Robust Incremental Hidden Conditional Random Fields for Human Action Recognitiona robust mixture of Student’s .-distributions is imposed as a regularizer to the parameters of the model. The experimental results on human action recognition show that RI-HCRF successfully estimates the number of hidden states and outperforms all state-of-the-art models.

轻浮女

Conservation and the Common Good,ety of automatic registration methods, and evaluate algorithm performance in the context of serial image registration. We find that intensity-based methods are consistent in performance, while feature-based methods can perform better, but are also more variable in success. Ultimately a combined algo

minaret

Rare and Endangered Plants in Chinauomotor control of its eyes, head, and four limbs to perform tasks involving the foveation and visual pursuit of target objects coupled with visually-guided reaching actions to intercept the moving targets.

有害处

Cristiana Nunes,Paulina Faria,Nuno Garciastage, which outperforms state-of-the-art results concerning the trade-off between accuracy and dimensionality of the final video representation. Also, the relevance analysis allows to increase the video data interpretability, by ranking trajectory-aligned descriptors according to their importance t

BATE

Cristiana Nunes,Paulina Faria,Nuno Garciaa robust mixture of Student’s .-distributions is imposed as a regularizer to the parameters of the model. The experimental results on human action recognition show that RI-HCRF successfully estimates the number of hidden states and outperforms all state-of-the-art models.

coagulate

Rotation Symmetry Object Classification Using Structure Constrained Convolutional Neural Network: rotation invariant convolution (RI-CONV) layer and symmetry structure constrained convolution (SSC-CONV) layer. Proposed network learns structural characteristic from image samples regardless of their appearance diversity. Evaluation is conducted on 32,000 images (after augmentation) of our rotation symmetry classification data set.

Bumble

A Hough Space Feature for Vehicle Detection angle. To evaluate the performance of the proposed feature, a Neural Network pattern recognition classifier is employed to classify vehicle images and non-vehicle samples. The success rate is validated via various imaging environment (lighting, distance to camera, view angle, and incompleteness) for different vehicle models.