標(biāo)題: Titlebook: Hypercube Algorithms; with Applications to Sanjay Ranka,Sartaj Sahni Textbook 1990 Springer-Verlag New York Inc. 1990 algorithm.algorithms. [打印本頁] 作者: tricuspid-valve 時(shí)間: 2025-3-21 18:26
書目名稱Hypercube Algorithms影響因子(影響力)
書目名稱Hypercube Algorithms影響因子(影響力)學(xué)科排名
書目名稱Hypercube Algorithms網(wǎng)絡(luò)公開度
書目名稱Hypercube Algorithms網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Hypercube Algorithms被引頻次
書目名稱Hypercube Algorithms被引頻次學(xué)科排名
書目名稱Hypercube Algorithms年度引用
書目名稱Hypercube Algorithms年度引用學(xué)科排名
書目名稱Hypercube Algorithms讀者反饋
書目名稱Hypercube Algorithms讀者反饋學(xué)科排名
作者: Ancestor 時(shí)間: 2025-3-21 20:46 作者: Orgasm 時(shí)間: 2025-3-22 02:47 作者: Culpable 時(shí)間: 2025-3-22 06:52
Textbook 1990sum, shift, data circulation, data accumulation, sorting, random access reads and writes and data permutation. The fundamental algorithms are then used to obtain efficient hypercube algorithms for matrix multiplication, image processing problems such as convolution, template matching, hough transfor作者: 吸氣 時(shí)間: 2025-3-22 10:12 作者: 盲信者 時(shí)間: 2025-3-22 14:40
Sanjay Ranka,Sartaj Sahnilly due to tumor resection and tissue displacement. Classical image registration techniques oftentimes fail in vicinity of the tumor, where the enclosing structures are massively altered from one scan to another. Still, locations nearby the tumor or the resection cavity are the most relevant for eva作者: 浪費(fèi)時(shí)間 時(shí)間: 2025-3-22 19:27
Sanjay Ranka,Sartaj Sahnir, as a result of the staining process, the tissue slides are being deformed and registration is required before further processing. The importance of this problem led to organizing an open challenge named Automatic Non-rigid Histological Image Registration Challenge (ANHIR), organized jointly with 作者: abject 時(shí)間: 2025-3-23 01:03 作者: Foreknowledge 時(shí)間: 2025-3-23 01:26 作者: AVOW 時(shí)間: 2025-3-23 08:43 作者: 使痛苦 時(shí)間: 2025-3-23 13:03
Sanjay Ranka,Sartaj Sahniudes. The recent Learn2Reg medical registration benchmark has demonstrated that single-scale U-Net architectures, such as VoxelMorph that directly employ a spatial transformer loss, often do not generalise well beyond the cranial vault and fall short of state-of-the-art performance for abdominal or 作者: 江湖騙子 時(shí)間: 2025-3-23 16:59 作者: Commemorate 時(shí)間: 2025-3-23 20:38
Sanjay Ranka,Sartaj Sahniudes. The recent Learn2Reg medical registration benchmark has demonstrated that single-scale U-Net architectures, such as VoxelMorph that directly employ a spatial transformer loss, often do not generalise well beyond the cranial vault and fall short of state-of-the-art performance for abdominal or 作者: Vaginismus 時(shí)間: 2025-3-24 01:47 作者: 沒有希望 時(shí)間: 2025-3-24 05:56
y when predicting domain-shifted input data. Multi-atlas segmentation utilizes multiple available sample annotations which are deformed and propagated to the target domain via multimodal image registration and fused to a consensus label afterwards but subsequent network training with the registered 作者: Choreography 時(shí)間: 2025-3-24 07:00
Fundamental Operations,of the remaining processors in the hypercube. This may be accomplished using the binary tree transmitting scheme of Figure 2.1. This figure is for the case of a dimension 3 hypercube. The data to be broadcast is initially only in processor 0 (root of the broadcast tree). It is transmitted along bit 作者: BOLT 時(shí)間: 2025-3-24 13:48
Template Matching,is an .×. matrix . 2. where.. 2. is called the two dimensional convolution of . and .. Template matching, i.e., computing . 2., is a fundamental operation in computer vision and image processing. It is often used for edge and object detection; filtering; and image registration (Rosenfeld and Kak 198作者: Intentional 時(shí)間: 2025-3-24 15:23 作者: ACRID 時(shí)間: 2025-3-24 22:41
Clustering,5). . partitions a set of feature vectors into groups. It is a valuable tool in exploratory pattern analysis and helps making hypotheses about the structure of data. It is important in syntactic pattern recognition, image segmentation and registration. There are many methods for clustering feature v作者: Mendicant 時(shí)間: 2025-3-25 02:41 作者: Vital-Signs 時(shí)間: 2025-3-25 06:59 作者: 享樂主義者 時(shí)間: 2025-3-25 09:47 作者: 委屈 時(shí)間: 2025-3-25 14:31
Sanjay Ranka,Sartaj Sahnihod that works well for images with different resolutions, aspect ratios, without the necessity to perform image padding, while maintaining a low number of network parameters and fast forward pass time. The proposed method is orders of magnitude faster than the classical approach based on the iterat作者: 染色體 時(shí)間: 2025-3-25 19:38
Sanjay Ranka,Sartaj Sahnition problem is evaluated. The results show that the computation time of B-spline interpolation is decreased by the proposed algorithm from a factor 4.1 for a 2D image using 1st order interpolation to a factor of 19.9 for 4D using 3rd order interpolation.作者: 波動(dòng) 時(shí)間: 2025-3-25 21:17 作者: 構(gòu)成 時(shí)間: 2025-3-26 00:35
Sanjay Ranka,Sartaj Sahnid work, including FlowNet or PDD-Net, our approach does not require a fully discretised architecture with correlation layer. Our ablation study demonstrates the importance of keypoints in both self-supervised and unsupervised (using only a MIND metric) settings. On a multi-centric inspiration-exhale作者: 連鎖,連串 時(shí)間: 2025-3-26 07:19
Sanjay Ranka,Sartaj Sahninsi when using a straightforward weighting scheme. Comparing our results to the STAPLE method, we find that our consensi are not only a better approximation of the oracle-label regarding Dice score but also improve subsequent network training results.作者: 修改 時(shí)間: 2025-3-26 09:30
Sanjay Ranka,Sartaj Sahnid work, including FlowNet or PDD-Net, our approach does not require a fully discretised architecture with correlation layer. Our ablation study demonstrates the importance of keypoints in both self-supervised and unsupervised (using only a MIND metric) settings. On a multi-centric inspiration-exhale作者: 氣候 時(shí)間: 2025-3-26 14:03
Sanjay Ranka,Sartaj Sahninsi when using a straightforward weighting scheme. Comparing our results to the STAPLE method, we find that our consensi are not only a better approximation of the oracle-label regarding Dice score but also improve subsequent network training results.作者: 休閑 時(shí)間: 2025-3-26 17:31
nsi when using a straightforward weighting scheme. Comparing our results to the STAPLE method, we find that our consensi are not only a better approximation of the oracle-label regarding Dice score but also improve subsequent network training results.作者: 未完成 時(shí)間: 2025-3-26 23:33 作者: MERIT 時(shí)間: 2025-3-27 03:04 作者: 悠然 時(shí)間: 2025-3-27 05:38
Textbook 1990rter course on hypercube algorithms. For students with no prior exposure to parallel algorithms, it is recommended that one week will be spent on the material in chapter 1, about six weeks on chapter 2 and one week on chapter 3. The remainder of the term can be spent covering topics from the rest of作者: 木訥 時(shí)間: 2025-3-27 10:58 作者: PACT 時(shí)間: 2025-3-27 15:48 作者: 容易懂得 時(shí)間: 2025-3-27 17:49
Hypercube Algorithms978-1-4613-9692-5Series ISSN 1431-0848 作者: 絕種 時(shí)間: 2025-3-28 00:14
https://doi.org/10.1007/978-1-4613-9692-5algorithm; algorithms; clustering; image processing; pattern; pattern recognition; programming; template; al作者: Lobotomy 時(shí)間: 2025-3-28 05:01 作者: 約會(huì) 時(shí)間: 2025-3-28 08:58
Template Matching,is an .×. matrix . 2. where.. 2. is called the two dimensional convolution of . and .. Template matching, i.e., computing . 2., is a fundamental operation in computer vision and image processing. It is often used for edge and object detection; filtering; and image registration (Rosenfeld and Kak 1982, and Ballard and Brown 1985).作者: Spongy-Bone 時(shí)間: 2025-3-28 12:59 作者: 規(guī)范就好 時(shí)間: 2025-3-28 17:53
Introduction,Parallel computers may be classified by taking into account their memory organization, processor organization, and the number of instruction streams supported.作者: regale 時(shí)間: 2025-3-28 21:03 作者: 中止 時(shí)間: 2025-3-29 02:22
