By George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Fatih Porikli, Sandra Skaff, Alireza Entezari, Jianyuan Min, Daisuke Iwai, Amela Sadagic, Carlos Scheidegger, Tobias Isenberg

The quantity set LNCS 10072 and LNCS 10073 constitutes the refereed complaints of the twelfth foreign Symposium on visible Computing, ISVC 2016, held in Las Vegas, NV, united states in December 2016.

The 102 revised complete papers and 34 poster papers offered during this e-book have been conscientiously reviewed and chosen from 220 submissions. The papers are geared up in topical sections: half I (LNCS 10072) includes computational bioimaging; special effects; movement and monitoring; segmentation; development reputation; visualization; 3D mapping; modeling and floor reconstruction; advancing autonomy for aerial robotics; clinical imaging; digital fact; laptop imaginative and prescient as a carrier; visible belief and robot structures; and biometrics. half II (LNCS 9475): purposes; visible surveillance; special effects; and digital reality.

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Additional resources for Advances in Visual Computing: 12th International Symposium, ISVC 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part II

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Zili Yi, Yang Li, and Minglun Gong Extraction of Vascular Intensity Directional Derivative on Computed Tomography Angiography . . . . . . . . . . . . . . . . . . Elijah Agbayani, Baixue Jia, Graham Woolf, David Liebeskind, and Fabien Scalzo 485 497 Contents – Part I Capturing Photorealistic and Printable 3D Models Using Low-Cost Hardware . . . . . . . . . . . . . . . . . . . . . . . . Christoph Heindl, Sharath Chandra Akkaladevi, and Harald Bauer Improved Stereo Vision of Indoor Dense Suspended Scatterers Scenes from De-scattering Images .

Liangjiang Yu and Guoliang Fan 309 Pollen Grain Recognition Using Deep Learning . . . . . . . . . . . Amar Daood, Eraldo Ribeiro, and Mark Bush Classifying Pollen Using Robust Sequence Alignment of Sparse Z-Stack Volumes . . . . . . . . . . . . . . . . . . . . . . . . Amar Daood, Eraldo Ribeiro, and Mark Bush Complementary Keypoint Descriptors . . . . . . . . . . . . . . Clark F.

In the training phase, the proposed approach describes each patch of food image, generates two overcomplete dictionaries from the local descriptors of the patch, A Sparse Representation Based Classification Algorithm 5 Fig. 1. The overview of the proposed algorithm forms feature vectors through sparse coding the local descriptors with the learned dictionaries, and finally trains SVM with the feature vectors and the corresponding label of each food category. In the testing phase, we construct the features of the input food image with the dictionaries that is generated in the training phase, and identify its category with the trained SVM.

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