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    A Collaborative Digital Pathology System for Multi‐Touch Mobile and Desktop Computing Platforms
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Jeong, W.; Schneider, J.; Hansen, A.; Lee, M.; Turney, S. G.; Faulkner‐Jones, B. E.; Hecht, J. L.; Najarian, R.; Yee, E.; Lichtman, J. W.; Pfister, H.; Holly Rushmeier and Oliver Deussen
    Collaborative slide image viewing systems are becoming increasingly important in pathology applications such as telepathology and E‐learning. Despite rapid advances in computing and imaging technology, current digital pathology systems have limited performance with respect to remote viewing of whole slide images on desktop or mobile computing devices. In this paper we present a novel digital pathology client–server system that supports collaborative viewing of multi‐plane whole slide images over standard networks using multi‐touch‐enabled clients. Our system is built upon a standard HTTP web server and a MySQL database to allow multiple clients to exchange image and metadata concurrently. We introduce a domain‐specific image‐stack compression method that leverages real‐time hardware decoding on mobile devices. It adaptively encodes image stacks in a decorrelated colour space to achieve extremely low bitrates (0.8 bpp) with very low loss of image quality. We evaluate the image quality of our compression method and the performance of our system for diagnosis with an in‐depth user study.Collaborative slide image viewing systems are becoming increasingly important in pathology applications such as telepathology and E‐learning. Despite rapid advances in computing and imaging technology, current digital pathology systems have limited performance with respect to remote viewing of whole slide images on desktop or mobile computing devices. In this paper we present a novel digital pathology client‐server systems that supports collaborative viewing of multi‐plane whole slide images over standard networks using multi‐touch enabled clients. Our system is built upon a standard HTTP web server and a MySQL database to allow multiple clients to exchange image and metadata concurrently.
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    Real‐Time Defocus Rendering With Level of Detail and Sub‐Sample Blur
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Jeong, Yuna; Kim, Kangtae; Lee, Sungkil; Holly Rushmeier and Oliver Deussen
    This paper presents a GPU‐based rendering algorithm for real‐time defocus blur effects, which significantly improves the accumulation buffering. The algorithm combines three distinctive techniques: (1) adaptive discrete geometric level of detail (LOD), made popping‐free by blending visibility samples across the two adjacent geometric levels; (2) adaptive visibility/shading sampling via sample reuse; (3) visibility supersampling via height‐field ray casting. All the three techniques are seamlessly integrated to lower the rendering cost of smooth defocus blur with high visibility sampling rates, while maintaining most of the quality of brute‐force accumulation buffering.This paper presents a GPU‐based rendering algorithm for real‐time defocus blur effects, which significantly improves the accumulation buffering. The algorithm combines three distinctive techniques: (1) adaptive discrete geometric level of detail (LOD), made popping‐free by blending visibility samples across the two adjacent geometric levels; (2) adaptive visibility/shading sampling via sample reuse; (3) visibility supersampling via height‐field ray casting. All the three techniques are seamlessly integrated to lower the rendering cost of smooth defocus blur with high visibility sampling rates, while maintaining most of the quality of brute‐force accumulation buffering.
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    InK‐Compact: In‐Kernel Stream Compaction and Its Application to Multi‐Kernel Data Visualization on General‐Purpose GPUs
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Hughes, D. M.; Lim, I. S.; Jones, M. W.; Knoll, A.; Spencer, B.; Holly Rushmeier and Oliver Deussen
    Stream compaction is an important parallel computing primitive that produces a reduced (compacted) output stream consisting of only valid elements from an input stream containing both invalid and valid elements. Computing on this compacted stream rather than the mixed input stream leads to improvements in performance, load balancing and memory footprint. Stream compaction has numerous applications in a wide range of domains: e.g. deferred shading, isosurface extraction and surface voxelization in computer graphics and visualization. We present a novel In‐Kernel stream compaction method, where compaction is completed before leaving an operating kernel. This contrasts with conventional parallel compaction methods that require leaving the kernel and running a prefix sum kernel followed by a scatter kernel. We apply our compaction methods to ray‐tracing‐based visualization of volumetric data. We demonstrate that the proposed In‐Kernel compaction outperforms the standard out‐of‐kernel Thrust parallel‐scan method for performing stream compaction in this real‐world application. For the data visualization, we also propose a novel multi‐kernel ray‐tracing pipeline for increased thread coherency and show that it outperforms a conventional single‐kernel approach.Stream compaction is an important parallel computing primitive that produces a reduced (compacted) output stream consisting of only valid elements from an input stream containing both invalid and valid elements. Computing on this compacted stream rather than the mixed input stream leads to improvements in performance, load balancing, and memory footprint. Stream compaction has numerous applications in a wide range of domains: e.g., deferred shading, isosurface extraction, and surface voxelization in computer graphics and visualization. We present a novel In‐Kernel stream compaction method, where compaction is completed before leaving an operating kernel. This contrasts with conventional parallel compaction methods that require leaving the kernel and running a prefix sum kernel followed by a scatter kernel.