scholarly journals Local patch analysis for testing statistical isotropy of the Planck convergence map

2021 ◽  
Vol 2021 (08) ◽  
pp. 006
Author(s):  
Priya Goyal ◽  
Pravabati Chingangbam
Keyword(s):  
Local Patch ◽  
Statistical Isotropy

scholarly journals Plant Leaf Recognition through Local Discriminative Tangent Space Alignment

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Chuanlei Zhang ◽  
Shanwen Zhang ◽  
Weidong Fang
Keyword(s):  
Dimensionality Reduction ◽  
Manifold Learning ◽  
Tangent Space ◽  
Reduction Method ◽  
Plant Leaf ◽  
Local Patch ◽  
Dimensionality Reduction Method

Manifold learning based dimensionality reduction algorithms have been payed much attention in plant leaf recognition as the algorithms can select a subset of effective and efficient discriminative features in the leaf images. In this paper, a dimensionality reduction method based on local discriminative tangent space alignment (LDTSA) is introduced for plant leaf recognition based on leaf images. The proposed method can embrace part optimization and whole alignment and encapsulate the geometric and discriminative information into a local patch. The experiments on two plant leaf databases, ICL and Swedish plant leaf datasets, demonstrate the effectiveness and feasibility of the proposed method.

Local Patch Similarity Ranked Voxelwise STAPLE on Magnetic Resonance Image Hippocampus Segmentation

2013 ◽  
Vol 333-335 ◽  
pp. 1065-1070
Author(s):  
Yuan Li ◽  
Fu Cang Jia ◽  
Xiao Dong Zhang ◽  
Cheng Huang ◽  
Huo Ling Luo
Keyword(s):  
Magnetic Resonance ◽  
Image Features ◽  
Morphometric Features ◽  
Segmentation Methods ◽  
Local Patch ◽  
Ranking Strategy ◽  
And Performance ◽  
Spatially Variant ◽  
Hippocampus Segmentation ◽  
Atlas Segmentation

The segmentation and labeling of sub-cortical structures of interest are important tasks for the assessment of morphometric features in quantitative magnetic resonance (MR) image analysis. Recently, multi-atlas segmentation methods with statistical fusion strategy have demonstrated high accuracy in hippocampus segmentation. While, most of the segmentations rarely consider spatially variant model and reserve all segmentations. In this study, we propose a novel local patch-based and ranking strategy for voxelwise atlas selection to extend the original Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. The local ranking strategy is based on the metric of normalized cross correlation (NCC). Unlike its predecessors, this method estimates the fusion of each voxel patch-by-patch and makes use of gray image features as a prior. Validation results on 33 pairs of hippocampus MR images show good performance on the segmentation of hippocampus.

scholarly journals Testing isotropy of cosmos with WMAP and PLANCK data

2015 ◽  
Vol 30 (27) ◽  
pp. 1550131 ◽  
Author(s):  
Pranati K. Rath ◽  
Pramoda Kumar Samal
Keyword(s):  
Planck Data ◽  
Tensor Method ◽  
Entropy Measures ◽  
High Degree ◽  
Statistical Isotropy

In recent years, there have been a large number of studies which support violation of statistical isotropy. Meanwhile, there are some studies which also found inconsistency. We use the power tensor method defined earlier in the literature to study the new CMBR data. The orientation of these three orthogonal vectors, as well as the power associated with each vector, contains information about possible violation of statistical isotropy. This information is encoded in two entropy measures, the power-entropy and alignment-entropy. We apply this method to WMAP 9-year and PLANCK data. Here, we also revisit the statistics to test high-[Formula: see text] anomaly reported in our earlier paper and find that the high degree of isotropy seen in earlier WMAP 5-year data is absent in the revised WMAP 9-year data.

scholarly journals Learning data-driven reduced elastic and inelastic models of spot-welded patches

2021 ◽  
Vol 22 ◽  
pp. 32
Author(s):  
Agathe Reille ◽  
Victor Champaney ◽  
Fatima Daim ◽  
Yves Tourbier ◽  
Nicolas Hascoet ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Structural Response ◽  
Data Driven ◽  
Fine Scale ◽  
Time Step ◽  
Large Structures ◽  
Computational Performance ◽  
Local Patch ◽  
The Rich ◽  
Learning Data

Solving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized behaviors need for extremely fine descriptions, and this has an associated impact in the number of degrees of freedom from one side, and the decrease of the time step employed in usual explicit time integrations, whose stability scales with the size of the smallest element involved in the mesh. In the present work we propose a data-driven technique for learning the rich behavior of a local patch and integrate it into a standard coarser description at the structure level. Thus, localized behaviors impact the global structural response without needing an explicit description of that fine scale behaviors.

scholarly journals Knowledge, attitude and practice of Lebanese community pharmacists with regard to self-management of low back pain

2020 ◽  
Vol 19 (4) ◽  
pp. 873-878
Author(s):  
Mariam K. Dabbous ◽  
Sara M. Moustafa ◽  
Fouad R. Sakr ◽  
Marwan G. Akel ◽  
Jihan H. Safwan ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Community Pharmacists ◽  
Cross Sectional Study ◽  
Oral Dosage ◽  
Secondary Outcome ◽  
Low Back ◽  
Cross Sectional ◽  
Local Patch

Purpose: To determine the knowledge, attitude and reported practice of Lebanese community pharmacists who advise persons who present with low back pain.Methods: This was a multi-center cross-sectional study conducted in over 300 community pharmacies across Lebanon from December 2017 to May 2018. Pharmacists working at a community pharmacy were considered eligible, and those who volunteered to participate completed the questionnaire. The questionnaire was designed for self-completion by the pharmacist and included demographic questions about the respondent, questions that assessed knowledge and attitude toward low back pain, and questions about treatment to reflect and characterize the nature of practice. The primary outcome was to determine the knowledge, attitude and reported practice of the Lebanese pharmacists advising people who presented with low back pain. The secondary outcome was to assess factors that affect knowledge, attitude, and practice.Results: The response of 320 community pharmacists was analysed. The proportion of pharmacists with good knowledge about low back pain (51. 7 %) was slightly higher than those with poor knowledge (48. 3 %). Oral therapy was the most prescribed dosage form for back pain compared to local patch and cream. Among oral dosage forms, non-steroidal anti-inflammatory drugs were the most prescribed medications (42 %). Of the patients’ referral to the physician if necessary, 73.1 % of the referrals were by pharmacists.Conclusion: Community pharmacists in Lebanon demonstrate an acceptable level of knowledge of back pain, yet major gaps still exist, particularly in terms of the quality of advice. Hence, more education is needed to provide better quality of advice. Keywords: Attitude, Knowledge, Low back pain, Reported practice, Quality of advice

scholarly journals Single Image Dehazing Using Sparse Contextual Representation

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1266
Author(s):  
Jing Qin ◽  
Liang Chen ◽  
Jian Xu ◽  
Wenqi Ren
Keyword(s):  
Sparse Representation ◽  
State Of The Art ◽  
Input Image ◽  
Single Image ◽  
Image Dehazing ◽  
Dark Channel ◽  
Local Patch ◽  
Novel Method ◽  
Contextual Representation ◽  
Single Image Dehazing

In this paper, we propose a novel method to remove haze from a single hazy input image based on the sparse representation. In our method, the sparse representation is proposed to be used as a contextual regularization tool, which can reduce the block artifacts and halos produced by only using dark channel prior without soft matting as the transmission is not always constant in a local patch. A novel way to use dictionary is proposed to smooth an image and generate the sharp dehazed result. Experimental results demonstrate that our proposed method performs favorably against the state-of-the-art dehazing methods and produces high-quality dehazed and vivid color results.

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