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 court cases of the twelfth overseas 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 provided during this e-book have been conscientiously reviewed and chosen from 220 submissions. The papers are prepared in topical sections: half I (LNCS 10072) contains 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; laptop 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.

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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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Martin Cerman, Gayane Shalunts, and Daniel Albertini 247 Towards Landmine Detection Using Ubiquitous Satellite Imaging . . . . . Sahar Elkazaz, Mohamed E. Hussein, Ahmed El-Mahdy, and Hiroshi Ishikawa 257 Robustness of Rotation Invariant Descriptors for Texture Classification . . . Raissa Tavares Vieira, Tamiris Trevisan Negri, and Adilson Gonzaga 268 Feature Evaluation for Handwritten Character Recognition with Regressive and Generative Hidden Markov Models . . . . . . . .

Ahmad P. Tafti, Ahmadreza Baghaie, Mehdi Assefi, Hamid R. Arabnia, Zeyun Yu, and Peggy Peissig Animal Identification in Low Quality Camera-Trap Images Using Very Deep Convolutional Neural Networks and Confidence Thresholds. . . . . Alexander Gomez, German Diez, Augusto Salazar, and Angelica Diaz A Gaussian Mixture Model Feature for Wildlife Detection . . . . . . . Shengzhi Du, Chunling Du, Rishaad Abdoola, and Barend Jacobus van Wyk 735 747 757 Biometrics Age Classification from Facial Images: Is Frontalization Necessary?

1 Training Phase In order to eliminate the interference of the background, we segment the target food region from the background for each image. We apply background detecting algorithm to extract the salient region from each raw image [9]. (1) Generating the Local Descriptors To provide an efficient description of the food image, we select some key points in the segmented food region of the image. And then generate two local descriptors for each patch around the selected key points. We apply the SIFT algorithm to extract key points of interest [10].

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