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1.发明申请SYSTEM AND METHODS FOR COMPUTING FORWARD AND INVERSE DISCRETE PERIODIC RADON TRANSFORM 审中-公开用于计算前向和反向离散周期性RADON变换的系统和方法公开(公告)号：US20160314603A1
公开(公告)日：2016-10-27
申请号：US15103977
申请日：2014-12-15
Applicant: STC.UNM
Inventor： Cesar Carranza , Marios Stephanou Pattichis , Daniel Llamocca Obregon
CPC classification number: G06T11/006 , G06F17/14 , G06F17/141 , G06K9/00973 , G06T2211/428
Abstract: A fast and a scalable approach for computing the forward and inverse DPRT that uses: (i) a parallel array of fixed-point adder trees to compute the additions, (ii) circular shift registers to remove the need for accessing external memory components, (iii) an image block-based approach to DPRT computation that can fit the proposed architecture to available resources, and (iv) fast transpositions that are computed in one or a few clock cycles that do not depend on the size of the input image.
Abstract translation: 用于计算正向和反向DPRT的快速和可扩展的方法，其使用：（i）用于计算加法的定点加法器树的并行阵列，（ii）循环移位寄存器以消除访问外部存储器组件的需要， iii）基于图像块的DPRT计算方法，其可以将所提出的架构适合于可用资源，以及（iv）在不依赖于输入图像的大小的一个或几个时钟周期内计算的快速转置。
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公开(公告)号：US10810696B2
公开(公告)日：2020-10-20
申请号：US15780052
申请日：2016-12-16
Applicant: STC.UNM
Inventor： Marios Stephanou Pattichis , Cesar Carranza , Daniel Llamocca Obregon
Abstract: Fast and scalable architectures and methods adaptable to available resources, that (1) compute 2-D convolutions using 1-D convolutions, (2) provide fast transposition and accumulation of results for computing fast cross-correlations or 2-D convolutions, and (3) provide parallel computations using pipelined 1-D convolvers. Additionally, fast and scalable architectures and methods that compute 2-D linear convolutions using Discrete Periodic Radon Transforms (DPRTs) including the use of scalable DPRT, Fast DPRT, and fast 1-D convolutions.
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公开(公告)号：US10943322B2
公开(公告)日：2021-03-09
申请号：US15780076
申请日：2016-12-16
Applicant: STC.UNM
Inventor： Marios Stephanou Pattichis , Cesar Carranza , Daniel Llamocca Obregon
Abstract: Fast and a scalable algorithms and methods adaptable to available resources for computing (1) the DPRT on multicore CPUs by distributing the computation of the DPRT primary directions among the different cores, and (2) the DPRT on GPUs using parallel, distributed, and synchronized ray computations among the GPU cores with “ray” referring to one of the sums required for computing the DPRT or its inverse along a prime direction.
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公开(公告)号：US10049469B2
公开(公告)日：2018-08-14
申请号：US15103977
申请日：2014-12-15
Applicant: STC.UNM
Inventor： Cesar Carranza , Marios Stephanou Pattichis , Daniel Llamocca Obregon
Abstract: A fast and a scalable approach for computing the forward and inverse DPRT that uses: (i) a parallel array of fixed-point adder trees to compute the additions, (ii) circular shift registers to remove the need for accessing external memory components, (iii) an image block-based approach to DPRT computation that can fit the proposed architecture to available resources, and (iv) fast transpositions that are computed in one or a few clock cycles that do not depend on the size of the input image.
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