Advances in Visual Computing: 12th International Symposium, by George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, PDF

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 offered during this e-book have been rigorously 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 attractiveness; visualization; 3D mapping; modeling and floor reconstruction; advancing autonomy for aerial robotics; scientific imaging; digital truth; laptop imaginative and prescient as a carrier; visible notion and robot structures; and biometrics. half II (LNCS 9475): purposes; visible surveillance; special effects; and digital reality.

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Extra info for Advances in Visual Computing: 12th International Symposium, ISVC 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part I

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Xin Zhou, Guangming Shi, and Wei Zhou 75 Video Anomaly Detection Based on Adaptive Multiple Auto-Encoders . . . Tianlong Bao, Chunhui Ding, Saleem Karmoshi, and Ming Zhu Comprehensive Parameter Sweep for Learning-Based Detector on Traffic Lights . . . . . . . . . . . . . . . . . . . . . . . . . Morten B. Jensen, Mark P. Philipsen, Thomas B. Moeslund, and Mohan Trivedi 83 92 XXXII Contents – Part II An Efficient Pedestrian Detector Based on Saliency and HOG Features Modeling .

1). 6 A. Tang and F. Scalzo Fig. 1. Extraction of local input features from 2D DSA and 3D MRA to train the model. Local descriptors are local point cloud histograms. Pairs of point descriptors are sampled randomly from labeled matching and nonmatching transformations. Image Representation. The 2D/3D images are represented as point clouds in their respective spaces. An erosion filter is applied to remove outliers. In this paper, we refer to the 2D point cloud as the Target image, and the 3D point cloud as the Source image.

Haixiang Yang, Dong Zhang, Dah-Jye Lee, and Minjie Huang Guided Text Spotting for Assistive Blind Navigation in Unfamiliar Indoor Environments . . . . . . . . . . . . . . . . . . . . . . . Xuejian Rong, Bing Li, J. Pablo Muñoz, Jizhong Xiao, Aries Arditi, and Yingli Tian 3 11 Automatic Oil Reserve Analysis Through the Shadows of Exterior Floating Crest Oil Tanks in Highlight Optical Satellite Images . . . . . . . . . Qingquan Wang, Jinfang Zhang, and Xiaohui Hu 23 Performance Evaluation of Video Summaries Using Efficient Image Euclidean Distance .

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