Titlebook: Computer Vision - ECCV 2014 Workshops; Zurich, Switzerland, Lourdes Agapito,Michael M. Bronstein,Carsten Rothe Conference proceedings 2015

AROMA

The Organization, Concreteness, Complexitynition of coins that leverages this new coin image set. As the use of succinct spatial-appearance relationships is critical for accurate coin recognition, we suggest two competing methods, adapted for the coin domain, to accomplish this task.

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RENIN

Hemiplegia

https://doi.org/10.1057/9781137379610ed in visual odometry system. Our approach gives lowest relative pose error amongst any other approaches tested on public benchmark dataset. A set of 3D reconstruction results on publicly available RGB-D videos are presented.

MUT

Conference proceedings 20153th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 203 workshop papers were carefully reviewed and selected for inclusion in the proceedings. They were presented at workshops with the following themes: where computer vision meets art; computer

灌溉

0302-9743 s that took place in conjunction with the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 203 workshop papers were carefully reviewed and selected for inclusion in the proceedings. They were presented at workshops with the following themes:

机械

The Power of Abstract Images in Advertisingsification tasks. In this work, we examine the perceptiveness of these features in identifying artistic styles in paintings, and suggest a compact binary representation of the paintings. Combined with the PiCoDes descriptors, these features show excellent classification results on a large scale collection of paintings.

intangibility

Ebct207

Classification of Artistic Styles Using Binarized Features Derived from a Deep Neural Networksification tasks. In this work, we examine the perceptiveness of these features in identifying artistic styles in paintings, and suggest a compact binary representation of the paintings. Combined with the PiCoDes descriptors, these features show excellent classification results on a large scale collection of paintings.

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