Dual‐view 3‐D display with multiple resolutions

2021 ◽  
Author(s):  
Guo‐Jiao Lv ◽  
Bai‐Chuan Zhao ◽  
Fei Wu
Keyword(s):  
Multiple Resolutions

Measuring evolutionary cancer dynamics from genome sequencing, one patient at a time

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Giulio Caravagna
Keyword(s):  
Genome Sequencing ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
Evolutionary Forces ◽  
Sequencing Technologies ◽  
Cancer Genome Sequencing ◽  
Multiple Resolutions ◽  
Multiple Patients ◽  
Single Tumour ◽  
Generation Sequencing

AbstractCancers progress through the accumulation of somatic mutations which accrue during tumour evolution, allowing some cells to proliferate in an uncontrolled fashion. This growth process is intimately related to latent evolutionary forces moulding the genetic and epigenetic composition of tumour subpopulations. Understanding cancer requires therefore the understanding of these selective pressures. The adoption of widespread next-generation sequencing technologies opens up for the possibility of measuring molecular profiles of cancers at multiple resolutions, across one or multiple patients. In this review we discuss how cancer genome sequencing data from a single tumour can be used to understand these evolutionary forces, overviewing mathematical models and inferential methods adopted in field of Cancer Evolution.

Multi-Resolution-Based Contour Corner Extraction Algorithm for Computer Vision-Based Measurement

2018 ◽  
Author(s):  
Jianhua Li ◽  
Lin Liao
Keyword(s):  
Computer Vision ◽  
Experimental Results ◽  
Original Algorithm ◽  
Industry Standard ◽  
Extraction Algorithm ◽  
Corner Points ◽  
Multiple Resolutions ◽  
Key Steps

Corner-based registration of the industry standard contour and the actual product contour is one of the key steps in industrial computer vision-based measurement. However, existing corner extraction algorithms do not achieve satisfactory results in the extraction of the standard contour and the deformed contour of the actual product. This paper proposes a multi-resolution-based contour corner extraction algorithm for computer vision-based measurement. The algorithm first obtains different corners in multiple resolutions, then sums up the weighted corner values, and finally chooses the corner points with the appropriate corner values as the final contour corners. The experimental results show that the proposed algorithm, based on multi-resolution, outperforms the original algorithm in the aspect of the corner matching situation and helps in subsequent product measurements.

scholarly journals MR-InpaintNet: Toward Deep Multi-Resolution Learning for Progressive Image Inpainting

2021 ◽  
Author(s):  
Huan Zhang ◽  
Zhao Zhang ◽  
Haijun Zhang ◽  
Yi Yang ◽  
Shuicheng Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Semantic Information ◽  
Feature Fusion ◽  
Image Inpainting ◽  
Feature Learning ◽  
Low Resolution ◽  
Resolution Image ◽  
Texture Information ◽  
Multiple Resolutions

<div>Deep learning based image inpainting methods have improved the performance greatly due to powerful representation ability of deep learning. However, current deep inpainting methods still tend to produce unreasonable structure and blurry texture, implying that image inpainting is still a challenging topic due to the ill-posed property of the task. To address these issues, we propose a novel deep multi-resolution learning-based progressive image inpainting method, termed MR-InpaintNet, which takes the damaged images of different resolutions as input and then fuses the multi-resolution features for repairing the damaged images. The idea is motivated by the fact that images of different resolutions can provide different levels of feature information. Specifically, the low-resolution image provides strong semantic information and the high-resolution image offers detailed texture information. The middle-resolution image can be used to reduce the gap between low-resolution and high-resolution images, which can further refine the inpainting result. To fuse and improve the multi-resolution features, a novel multi-resolution feature learning (MRFL) process is designed, which is consisted of a multi-resolution feature fusion (MRFF) module, an adaptive feature enhancement (AFE) module and a memory enhanced mechanism (MEM) module for information preservation. Then, the refined multi-resolution features contain both rich semantic information and detailed texture information from multiple resolutions. We further handle the refined multiresolution features by the decoder to obtain the recovered image. Extensive experiments on the Paris Street View, Places2 and CelebA-HQ datasets demonstrate that our proposed MRInpaintNet can effectively recover the textures and structures, and performs favorably against state-of-the-art methods.</div>

STREAM: Sensor Topology Retrieval at Multiple Resolutions

2004 ◽  
Vol 26 (2-4) ◽  
pp. 285-320 ◽  
Author(s):  
Budhaditya Deb ◽  
Sudeept Bhatnagar ◽  
Badri Nath
Keyword(s):  
Multiple Resolutions

scholarly journals Stereological assessment of mouse lung parenchyma via nondestructive, multiscale micro-CT imaging validated by light microscopic histology

2013 ◽  
Vol 114 (6) ◽  
pp. 716-724 ◽  
Author(s):  
Dragoş M. Vasilescu ◽  
Christine Klinge ◽  
Lars Knudsen ◽  
Leilei Yin ◽  
Ge Wang ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Lung Parenchyma ◽  
Quantitative Information ◽  
Mouse Lung ◽  
Imaging Method ◽  
Micro Computed Tomography ◽  
Lung Structure ◽  
Internal Structures ◽  
Multiple Resolutions ◽  
Conventional Histology

Quantitative assessment of the lung microstructure using standard stereological methods such as volume fractions of tissue, alveolar surface area, or number of alveoli, are essential for understanding the state of normal and diseased lung. These measures are traditionally obtained from histological sections of the lung tissue, a process that ultimately destroys the three-dimensional (3-D) anatomy of the tissue. In comparison, a novel X-ray-based imaging method that allows nondestructive sectioning and imaging of fixed lungs at multiple resolutions can overcome this limitation. Scanning of the whole lung at high resolution and subsequent regional sampling at ultrahigh resolution without physically dissecting the organ allows the application of design-based stereology for assessment of the whole lung structure. Here we validate multiple stereological estimates performed on micro–computed tomography (μCT) images by comparing them with those obtained via conventional histology on the same mouse lungs. We explore and discuss the potentials and limitations of the two approaches. Histological examination offers higher resolution and the qualitative differentiation of tissues by staining, but ultimately loses 3-D tissue relationships, whereas μCT allows for the integration of morphometric data with the spatial complexity of lung structure. However, μCT has limited resolution satisfactory for the sterological estimates presented in this study but not for differentiation of tissues. We conclude that introducing stereological methods in μCT studies adds value by providing quantitative information on internal structures while not curtailing more complex approaches to the study of lung architecture in the context of physiological or pathological studies.

Cascade Registration of Micro CT Volumes Taken in Multiple Resolutions

2016 ◽  
pp. 269-280
Author(s):  
Kai Nagara ◽  
Hirohisa Oda ◽  
Shota Nakamura ◽  
Masahiro Oda ◽  
Hirotoshi Homma ◽  
...  
Keyword(s):  
Micro Ct ◽  
Multiple Resolutions

Geo-feature modeling uncertainties in discrete global grids: a case study of downtown Calgary, Canada

GEOMATICA ◽  
2020 ◽  
pp. 1-21
Author(s):  
Mingke Li ◽  
Emmanuel Stefanakis
Keyword(s):  
Urban Environments ◽  
Feature Modeling ◽  
Research Directions ◽  
Grid Systems ◽  
Topological Relationships ◽  
Modeling Uncertainties ◽  
Multiple Resolutions ◽  
Grid Orientation ◽  
Open Geospatial Consortium

The Open Geospatial Consortium has officially adopted discrete global grid systems (DGGS) as a new option for Earth reference standards. Many state-of-the-art DGGS implementations have been developed, revealing the potential for DGGS applications. Before the wide application of DGGS in solving real-world problems, however, the potential uncertainties of modeling on DGGS should be investigated and documented. This study focused on the uncertainties of geo-feature modeling on DGGS, quantitatively measured the point position displacement and line and polygon features’ geometry distortion, and evaluated the validity of topological relationships. Specifically, traffic cameras (points), main streets (lines), and land-cover classes (polygons) of downtown Calgary (AB, Canada) were modeled in various DGGS configurations at multiple resolutions. Results showed that the point displacement and polygon distortion generally reduced when being modeled at a higher resolution. The tessellations with the monotonical convergence characteristic are recommended if cell indices are expected to represent levels of model precision. Line features’ fidelity was affected by grid tessellations, resolution levels, grid orientation relative to the Earth, and the rotated line directions. The degree of the line distortion was not straightforward to forecast. Maintaining the topological validity between spatial objects with various granularities was challenging and needed further algorithm development for DGGS implementations. The study outcomes can serve as useful guidelines in the selection among grid types, refinement ratios, and resolution levels when applying DGGS implementations to urban environments. This paper also pinpoints several research directions that can benefit the quantization and analysis of vector features on DGGS.

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