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Ray Tracing on a Stream Processor
, 2004
"... Ray tracing is an image synthesis technique which simulates the interaction of light with surfaces. Most highquality, photorealistic renderings are generated by global illumination techniques built on top of ray tracing. Realtime ray tracing has been a goal of the graphics community for many years ..."
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Ray tracing is an image synthesis technique which simulates the interaction of light with surfaces. Most highquality, photorealistic renderings are generated by global illumination techniques built on top of ray tracing. Realtime ray tracing has been a goal of the graphics community for many years. Unfortunately, ray tracing is a very expensive operation. VLSI technology has just reached the point where the computational capability of a single chip is su#cient for realtime ray tracing. Supercomputers and clusters of PCs have only recently been able to demonstrate interactive ray tracing and global illumination applications. In this
On optimizing nearest neighbor queries in highdimensional data spaces
 In Proceedings of 8th International Conference on Database Theory (ICDT
, 2001
"... Abstract. Nearestneighbor queries in highdimensional space are of high importance in various applications, especially in contentbased indexing of multimedia data. For an optimization of the query processing, accurate models for estimating the query processing costs are needed. In this paper, we p ..."
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Abstract. Nearestneighbor queries in highdimensional space are of high importance in various applications, especially in contentbased indexing of multimedia data. For an optimization of the query processing, accurate models for estimating the query processing costs are needed. In this paper, we propose a new cost model for nearest neighbor queries in highdimensional space, which we apply to enhance the performance of highdimensional index structures. The model is based on new insights into effects occurring in highdimensional space and provides a closed formula for the processing costs of nearest neighbor queries depending on the dimensionality, the block size and the database size. From the wide range of possible applications of our model, we select two interesting samples: First, we use the model to prove the known linear complexity of the nearest neighbor search problem in highdimensional space, and second, we provide a technique for optimizing the block size. For data of medium dimensionality, the optimized block size allows significant speedups of the query processing time when compared to traditional block sizes and to the linear scan. 1.
On the Efficiency of Nearest Neighbor Searching with Data Clustered in Lower Dimensions
, 2001
"... In nearest neighbor searching we are given a set of n data points in real ddimensional space, R d , and the problem is to preprocess these points into a data structure, so that given a query point, the nearest data point to the query point can be reported eciently. Because data sets can be quit ..."
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Cited by 6 (0 self)
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In nearest neighbor searching we are given a set of n data points in real ddimensional space, R d , and the problem is to preprocess these points into a data structure, so that given a query point, the nearest data point to the query point can be reported eciently. Because data sets can be quite large, we are interested in data structures that use optimal O(dn) storage. Given the limitation of linear storage, the best known data structures suer from expectedcase query times that grow exponentially in d. However, it is widely regarded in practice that data sets in high dimensional spaces tend to consist of clusters residing in much lower dimensional subspaces. This raises the question of whether data structures for nearest neighbor searching adapt to the presence of lower dimensional clustering, and further how performance varies when the clusters are aligned with the coordinate axes. We analyze the popular kdtree data structure in the form of two variants based on a modication of the splitting method, which produces cells satisfy the basic packing properties needed for eciency without producing empty cells. We show that when data points are uniformly distributed on a k dimensional hyperplane for k d, then expected number of leaves visited in such a kdtree grows exponentially in k, but not in d. We show that the growth rate is even smaller still if the hyperplane is aligned with the coordinate axes. We present empirical studies to support our theoretical results. Keywords: Nearest neighbor searching, kdtrees, splitting methods, expectedcase analysis, clustering. 1
Chromatic Nearest Neighbor Searching: A Query Sensitive Approach
, 1996
"... The nearest neighbor problem is that of preprocessing a set P of n data points in R d so that, given any query point q, the closest point in P to q can be determined efficiently. In the chromatic nearest neighbor problem, each point of P is assigned a color, and the problem is to determine the col ..."
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Cited by 4 (2 self)
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The nearest neighbor problem is that of preprocessing a set P of n data points in R d so that, given any query point q, the closest point in P to q can be determined efficiently. In the chromatic nearest neighbor problem, each point of P is assigned a color, and the problem is to determine the color of the nearest point to the query point. More generally, given k 1, the problem is to determine the color occurring most frequently among the k nearest neighbors. The chromatic version of the nearest neighbor problem is used in many applications in pattern recognition and learning. In this paper we present a simple algorithm for solving the chromatic k nearest neighbor problem. We provide a query sensitive analysis, which shows that if the color classes form spatially well separated clusters (as often happens in practice), then queries can be answered quite efficiently. We also allow the user to specify an error bound ffl 0, and consider the same problem in the context of approximate ne...
Visualization for the Physical Sciences
 EUROGRAPHICS
"... Close collaboration with other scientific fields is seen as an important goal for the visualization community by leading researchers in visualization. Yet, engaging in a scientific collaboration can be challenging. Physical sciences, with its array of research directions, provide many exciting chall ..."
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Close collaboration with other scientific fields is seen as an important goal for the visualization community by leading researchers in visualization. Yet, engaging in a scientific collaboration can be challenging. Physical sciences, with its array of research directions, provide many exciting challenges for a visualization scientist which in turn create ample possibilities for collaboration. We present the first survey of its kind that provides a comprehensive view on existing work on visualization for the physical sciences. We introduce a novel classification scheme based on application area, data dimensionality and main challenge addressed and apply this classification scheme to each contribution from the literature. Our classification highlights mature areas in visualization for the physical sciences and suggests directions for future work. Our survey serves as a useful starting point for those interested in visualization for the physical sciences, namely astronomy, chemistry, earth sciences and physics.
Detection and Visualization of Defects in 3D Unstructured Models of Nematic Liquid Crystals
"... Abstract—A method for the semiautomatic detection and visualization of defects in models of nematic liquid crystals (NLCs) is introduced; this method is suitable for unstructured models, a previously unsolved problem. The detected defects—also known as disclinations—are regions were the alignment o ..."
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Abstract—A method for the semiautomatic detection and visualization of defects in models of nematic liquid crystals (NLCs) is introduced; this method is suitable for unstructured models, a previously unsolved problem. The detected defects—also known as disclinations—are regions were the alignment of the liquid crystal rapidly changes over space; these defects play a large role in the physical behavior of the NLC substrate. Defect detection is based upon a measure of total angular change of crystal orientation (the director) over a node neighborhood via the use of a nearest neighbor path. Visualizations based upon the detection algorithm clearly identifies complete defect regions as opposed to incomplete visual descriptions provided by cuttingplane and isosurface approaches. The introduced techniques are currently in use by scientists studying the dynamics of defect change. Index Terms—scientific visualization, disclination, nematic liquid crystal, defects, unstructured grid, feature extraction 1
Performance Analysis of Nearest Neighbor Algorithms
 in: 8th International Fall Workshop of Vision, Modeling, and Visalization
, 2003
"... There are many nearest neighbor algorithms tailormade for ICP, but most of them require special input data like range images or triangle meshes. We focus on efficient nearest neighbor algorithms that do not impose this limitation, and thus can also be used with 3D point sets generated by structure ..."
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There are many nearest neighbor algorithms tailormade for ICP, but most of them require special input data like range images or triangle meshes. We focus on efficient nearest neighbor algorithms that do not impose this limitation, and thus can also be used with 3D point sets generated by structurefrommotion techniques. We shortly present the evaluated algorithms and introduce the modifications we made to improve their efficiency. In particular, several enhancements to the wellknown kD tree algorithm are described. The first part of our performance analysis consists of experiments on synthetic point sets, whereas the second part features experiments with the ICP algorithm on real point sets. Both parts are completed by a thorough evaluation of the obtained results.
Computer Vision and Image Understanding 116 (2012) 991–998 Contents lists available at SciVerse ScienceDirect Computer Vision and Image Understanding
"... journal homepage: www.elsevier.com/locate/cviu Probabilistic cost model for nearest neighbor search in image retrieval q ..."
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journal homepage: www.elsevier.com/locate/cviu Probabilistic cost model for nearest neighbor search in image retrieval q
Optimal Load Factor for Approximate Nearest Neighbor Search under Exact Euclidean Locality Sensitive Hashing
"... Locality Sensitive Hashing (LSH) is an indexbased data structure that allows spatial item retrieval over a large dataset. The performance measure, ρ, has significant effect on the computational complexity and memory space requirement to create and store items in this data structure respectively. Th ..."
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Locality Sensitive Hashing (LSH) is an indexbased data structure that allows spatial item retrieval over a large dataset. The performance measure, ρ, has significant effect on the computational complexity and memory space requirement to create and store items in this data structure respectively. The minimization of ρ at a specific approximation factor c, is dependent on the load factor, α. Over the years,