Skin-Anatomy Based Face Texture Image Synthesis by Skin Feature Distribution Analyzing Method

2010 ◽  
Author(s):  
Rui Guo ◽  
Hiroki Takahashi
Keyword(s):  
Image Synthesis ◽  
Texture Image

An Improved Texture Image Synthesis Algorithm

2016 ◽  
Vol 53 (12) ◽  
pp. 121001 ◽  
Author(s):  
谭永前 Tan Yongqian ◽  
曾凡菊 Zeng Fanju ◽  
岳莉 Yue Li ◽  
吴位巍 Wu Weiwei
Keyword(s):  
Image Synthesis ◽  
Texture Image ◽  
Synthesis Algorithm

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

Optical and Radar Data Analysis for Land Use Land Cover Mapping in Peru

2019 ◽  
Vol 3 (1) ◽  
pp. 14-27
Author(s):  
Barry Haack ◽  
Ron Mahabir
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Window Size ◽  
Radar Data ◽  
Optical Data ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Texture Image ◽  
Site Class ◽  
Spectral Signatures

This analysis determined the best individual band and combinations of various numbers of bands for land use land cover mapping for three sites in Peru. The data included Landsat Thematic Mapper (TM) optical data, PALSAR L-band dual-polarized radar, and derived radar texture images. Spectral signatures were first obtained for each site class and separability between classes determined using divergence measures. Results show that the best single band for analysis was a TM band, which was different for each site. For two of the three sites, the second best band was a radar texture image from a large window size. For all sites the best three bands included two TM bands and a radar texture image. The original PALSAR bands were of limited value. Finally upon further analysis it was determined that no more than six bands were needed for viable classification at each study site.

scholarly journals Graph-Based Construction of 3D Korean Giwa House Models

Buildings ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 68
Author(s):  
Mankyu Sung
Keyword(s):  
Data Structure ◽  
Computer Graphics ◽  
Real Time ◽  
Texture Image ◽  
Time Rate ◽  
Commercial Software ◽  
Spline Curve ◽  
High Level ◽  
Complicated Part ◽  
Different Parts

This paper proposes a graph-based algorithm for constructing 3D Korean traditional houses automatically using a computer graphics technique. In particular, we target designing the most popular traditional house type, a giwa house, whose roof is covered with a set of Korean traditional roof tiles called giwa. In our approach, we divided the whole design processes into two different parts. At a high level, we propose a special data structure called ‘modeling graphs’. A modeling graph consists of a set of nodes and edges. A node represents a particular component of the house and an edge represents the connection between two components with all associated parameters, including an offset vector between components. Users can easily add/ delete nodes and make them connect by an edge through a few mouse clicks. Once a modeling graph is built, then it is interpreted and rendered on a component-by-component basis by traversing nodes in a procedural way. At a low level, we came up with all the required parameters for constructing the components. Among all the components, the most beautiful but complicated part is the gently curved roof structures. In order to represent the sophisticated roof style, we introduce a spline curve-based modeling technique that is able to create curvy silhouettes of three different roof styles. In this process, rather than just applying a simple texture image onto the roof, which is widely used in commercial software, we actually laid out 3D giwa tiles on the roof seamlessly, which generated more realistic looks. Through many experiments, we verified that the proposed algorithm can model and render the giwa house at a real time rate.

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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