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dc.contributor.authorWang, C.M.en_US
dc.contributor.authorWang, P.C.en_US
dc.description.abstractSampling is important for many applications in research areas such as graphics. vision, and image processing. In this paper. we present a novel stratified sampling algorithm (SSA) for the coiled tubing surface with a given probability density function. The algorithm is developed from the inverse function of the integration for the areas of the coiled tubing surface. We exploit a Hierarchical Allocation Strategy (HAS) to preserve sample stratification when generating any desirable sample numbers. This permits us to reduce variances when applying our algorithm to Monte Carlo Direct Lighting for realistic image generation. We accelerate the sampling process using a segmentation technique in the integration domain. Our algorithm thus runs 324 orders of magnitude faster when using faster SSA algorithm where the order of the magnitude is proportional to the sample numbers. Finally. we employ a parabolic interpolation technique to decrease the average errors occurred for using the segmentation technique. This permits us to produce nearly constant average errors, independent of the sample numbers. The proposed algorithm is novel, efficient in computing and feasible for realistic image generation using Monte Carlo method.en_US
dc.relationIeice Transactions on Information and Systemsen_US
dc.relation.ispartofseriesIeice Transactions on Information and Systems, Volume E87D, Issue 6, Page(s) 1545-1553.en_US
dc.subjectstratified sampling algorithmsen_US
dc.subjectcoiled tubing surfaceen_US
dc.subjecthierarchical allocation strategyen_US
dc.subjectMonte Carlo methoden_US
dc.subjectcanal surfaceen_US
dc.titleA novel approach to sampling the coiled tubing surface with an application for Monte Carlo direct lightingen_US
dc.typeJournal Articlezh_TW
item.openairetypeJournal Article-
item.fulltextno fulltext-
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