Training-based Super Resolution Enhancement using CUDA. D99922013 張書豪 R99944018 張嫚家 R99944035 楊逸民. Outline. Introduction Training-based super resolution method Improvement and application Task and Goal Schedule. Introduction. Super resolution can enhance the resolution of imagesBy baba
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Coons Patches and Gregory Patches. Dr. Scott Schaefer. Patches With Arbitrary Boundaries. Given any 4 curves, f ( s ,0), f ( s ,1), f (0, t ), f (1, t ) that meet continuously at the corners, construct a smooth surface interpolating these curves. Patches With Arbitrary Boundaries.
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Super-Patches. Image Tessellation with Arbitrarily Shaped Patches. Eddie K. H. Ng , University of Toronto. Joint work with Brendan Frey. Motivations. Patch-based models have been successfully applied to many different areas of computer vision.
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Parametric Patches. Tensor product or rectangular patches are of the form: P (u,w) = u,w [0,1]. The number of control points is (m+1)(n+1) Triangular patches have triangular domain. They are of the form: P (r,s,t) = r,s,t 0 It has (n+1)(n+2)/2 control points.
Sweet Patches. By: Natasha, Eslie , Jess, and Ashlynn. The reason for diabetes research. Diabetes causes many problems, ranging from amputations, to deaths, to serious illnesses, and even blindness. Statistics say: 25.8 million children and adults in the U.S. have diabetes.
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LR Parser. Giuseppe Morelli. La maggior parte dei parser Bottom-Up è costituita dai cosiddetti parser LR(k) dove: L indica il verso dell’analisi della stringa di input (letf-to-right) R indica che per la costruzione dell’albero di parse si usa una derivazione destra inversa