scholarly journals Semantic Image Synthesis With Spatially-Adaptive Normalization

2019 ◽  
Author(s):  
Taesung Park ◽  
Ming-Yu Liu ◽  
Ting-Chun Wang ◽  
Jun-Yan Zhu
Keyword(s):  
Image Synthesis ◽  
Spatially Adaptive

Automated 3-D cell population analysis in thick tissue sections using laser-scanning confocal-microscopy data

1993 ◽  
Vol 51 ◽  
pp. 562-563
Author(s):  
Hakan Ancin
Keyword(s):  
Laser Scanning ◽  
Population Analysis ◽  
Three Dimensional ◽  
Large Data ◽  
Laser Scanning Confocal Microscopy ◽  
Tissue Slices ◽  
Scanning Confocal Microscopy ◽  
Tissue Sections ◽  
Spatially Adaptive ◽  
Microscopy Data

This paper presents methods for performing detailed quantitative automated three dimensional (3-D) analysis of cell populations in thick tissue sections while preserving the relative 3-D locations of cells. Specifically, the method disambiguates overlapping clusters of cells, and accurately measures the volume, 3-D location, and shape parameters for each cell. Finally, the entire population of cells is analyzed to detect patterns and groupings with respect to various combinations of cell properties. All of the above is accomplished with zero subjective bias.In this method, a laser-scanning confocal light microscope (LSCM) is used to collect optical sections through the entire thickness (100 - 500μm) of fluorescently-labelled tissue slices. The acquired stack of optical slices is first subjected to axial deblurring using the expectation maximization (EM) algorithm. The resulting isotropic 3-D image is segmented using a spatially-adaptive Poisson based image segmentation algorithm with region-dependent smoothing parameters. Extracting the voxels that were labelled as "foreground" into an active voxel data structure results in a large data reduction.

Image Synthesis by combining the Wavelets and Inverse Approaches

1999 ◽  
Vol 19 (Supplement1) ◽  
pp. 87-90
Author(s):  
D. SEKIJIMA ◽  
S. HAYANO ◽  
Y. SAITO ◽  
T.L. KUNII
Keyword(s):  
Image Synthesis

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

scholarly journals Resolved spectral variations of the centimetre-wavelength continuum from the ρ Oph W photo-dissociation-region

2021 ◽  
Author(s):  
Simon Casassus ◽  
Matías Vidal ◽  
Carla Arce-Tord ◽  
Clive Dickinson ◽  
Glenn J White ◽  
...  
Keyword(s):  
Molecular Cloud ◽  
Dust Emission ◽  
Image Synthesis ◽  
Microwave Emission ◽  
Radio Continuum ◽  
Continuum Emission ◽  
Parametric Image ◽  
Physical Conditions ◽  
Morphological Differences ◽  
Dust Grain

Abstract Cm-wavelength radio continuum emission in excess of free-free, synchrotron and Rayleigh-Jeans dust emission (excess microwave emission, EME), and often called ‘anomalous microwave emission’, is bright in molecular cloud regions exposed to UV radiation, i.e. in photo-dissociation regions (PDRs). The EME correlates with IR dust emission on degree angular scales. Resolved observations of well-studied PDRs are needed to compare the spectral variations of the cm-continuum with tracers of physical conditions and of the dust grain population. The EME is particularly bright in the regions of the ρ Ophiuchi molecular cloud (ρ Oph) that surround the earliest type star in the complex, HD 147889, where the peak signal stems from the filament known as the ρ Oph-W PDR. Here we report on ATCA observations of ρ Oph-W that resolve the width of the filament. We recover extended emission using a variant of non-parametric image synthesis performed in the sky plane. The multi-frequency 17 GHz to 39 GHz mosaics reveal spectral variations in the cm-wavelength continuum. At ∼30 arcsec resolutions, the 17-20 GHz intensities follow tightly the mid-IR, Icm∝I(8 μm), despite the breakdown of this correlation on larger scales. However, while the 33-39 GHz filament is parallel to IRAC 8 μm, it is offset by 15–20 arcsec towards the UV source. Such morphological differences in frequency reflect spectral variations, which we quantify spectroscopically as a sharp and steepening high-frequency cutoff, interpreted in terms of the spinning dust emission mechanism as a minimum grain size acutoff ∼ 6 ± 1 Å that increases deeper into the PDR.

scholarly journals Visual Thinking of Neural Networks: Interactive Text to Image Synthesis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hyunhee Lee ◽  
Gyeongmin Kim ◽  
Yuna Hur ◽  
Heuiseok Lim
Keyword(s):  
Neural Networks ◽  
Image Synthesis ◽  
Visual Thinking ◽  
Interactive Text
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