Advances in Visual Computing: 10th International Symposium, by George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin,

By George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Ryan McMahan, Jason Jerald, Hui Zhang, Steven Drucker, Kambhamettu Chandra, El Choubassi Maha, Zhigang Deng, Mark Carlson

The 2 quantity set LNCS 8887 and 8888 constitutes the refereed court cases of the tenth foreign Symposium on visible Computing, ISVC 2014, held in Las Vegas, NV, united states. The seventy four revised complete papers and fifty five poster papers provided including 39 designated music papers have been conscientiously reviewed and chosen from greater than 280 submissions. The papers are geared up in topical sections: half I (LNCS 8887) includes computational bioimaging, special effects; movement, monitoring, characteristic extraction and matching, segmentation, visualization, mapping, modeling and floor reconstruction, unmanned self sustaining structures, clinical imaging, monitoring for human job tracking, clever transportation platforms, visible notion and robot platforms. half II (LNCS 8888) contains issues resembling computational bioimaging , acceptance, desktop imaginative and prescient, functions, face processing and popularity, digital fact, and the poster periods.

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Extra resources for Advances in Visual Computing: 10th International Symposium, ISVC 2014, Las Vegas, NV, USA, December 8-10, 2014, Proceedings, Part II (Lecture Notes in Computer Science)

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In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 317–324. ACM Press/Addison-Wesley Publishing Co. (1999) Multimodal Non-Rigid Registration Methods Based on Demons Models and Local Uncertainty Quantification Used in 3D Brain Images Isnardo Reducindo1, , Aldo R. Mej´ıa-Rodr´ıguez1, Edgar Arce-Santana1, , Daniel U. Campos-Delgado1 , Elisa Scalco2 , Giovanni M. mx 2 3 Abstract. In this work, we propose a novel fully automated method to solve the 3D multimodal non-rigid image registration problem.

Yoav Liberman and Adi Perry 128 Optical Flow Computation in the Presence of Spatially-Varying Motion Blur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad Hossein Daraei 140 A Fast TGV-l1 RGB-D Flow Estimation . . . . . . . . . . . . . . Junha Roh, Hwasup Lim, and Sang Chul Ahn Statistical Estimation of Fluid Flow: An Image Restoration Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Jiaxi He, Cishen Zhang, and Ifat-Al Baqee 87 Real-Time Depth-Image-Based Rendering for 3DTV Using OpenCL . . Roberto Gerson de Albuquerque Azevedo, Fernando Ism´erio, Alberto Barbosa Raposo, and Luiz Fernando Gomes Soares 97 XXXIV Table of Contents – Part I Motion and Tracking Direct Estimation of Dense Scene Flow and Depth from a Monocular Sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yosra Mathlouthi, Amar Mitiche, and Ismail Ben Ayed 107 3D Deformable Spatial Pyramid for Dense 3D Motion Flow of Deformable Object .

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