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
ISBN-10: 3319508318
ISBN-13: 9783319508313
ISBN-10: 3319508326
ISBN-13: 9783319508320
The quantity set LNCS 10072 and LNCS 10073 constitutes the refereed court cases 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 awarded during this publication have been conscientiously reviewed and chosen from 220 submissions. The papers are equipped in topical sections: half I (LNCS 10072) includes computational bioimaging; special effects; movement and monitoring; segmentation; development acceptance; visualization; 3D mapping; modeling and floor reconstruction; advancing autonomy for aerial robotics; scientific imaging; digital fact; desktop imaginative and prescient as a carrier; visible conception and robot platforms; and biometrics. half II (LNCS 9475): functions; visible surveillance; special effects; and digital reality.
Read Online or Download Advances in Visual Computing: 12th International Symposium, ISVC 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part II PDF
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Extra resources for Advances in Visual Computing: 12th International Symposium, ISVC 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part II
Sample text
Edward Kim and Christopher Moritz 681 Narrative Approach to Assess Fear of Heights in Virtual Environments . . . Angelo D. Moro, Christian Quintero, and Wilson J. Sarmiento 691 Immersive Industrial Process Environment from a P&ID Diagram . . . . . Víctor H. Andaluz, Washington X. Quevedo, Fernando A. Chicaiza, Catherine Gálvez, Gabriel Corrales, Jorge S. Sánchez, Edwin P. Pruna, Oscar Arteaga, Fabián A. Álvarez, and Galo Ávila 701 Automatic Environment Map Construction for Mixed Reality Robotic Applications .
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. Olson, Sam A. Hoover, Jordan L.
A five-fold cross verification was performed to evaluate the accuracy and stability of the proposed method. For each test, four groups of each category were chosen as the training samples and one group was used as the testing samples. We repeated the same process, each time with a different group of samples as the testing data, to calculate the average successful rate of classification. Fig. 2. Sample images of the developed visual dataset. The dataset contains more than 5000 food images organized into 18 classes 8 H.
Advances in Visual Computing: 12th International Symposium, ISVC 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part II 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
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