Simulating 3D Cloud Shape Based on Computer Vision and Particle System

2012 ◽  
Vol 182-183 ◽  
pp. 819-822 ◽  
Author(s):  
Lin Gong
Keyword(s):  
Computer Vision ◽  
Computer Graphics ◽  
Natural Environment ◽  
Particle System ◽  
3D Structure ◽  
Realistic Simulation ◽  
Structure Information ◽  
Efficient Approach ◽  
3D Space ◽  
Computer Vision Technology

Clouds are an important part of natural environment. The realistic simulation of cloud is a challenging topic in computer graphics. This paper proposes a simple, efficient approach based on computer vision and particle system to model various 3D clouds. This method use computer vision technology to extract 3D structure information of clouds from images, then using particles technology to fill the 3D space and render the cloud. This method is suitable to model all kinds of clouds, such as stratus, cumulus, cirrus etc. It is an improvement over earlier systems that modeled only one type of cloud.

scholarly journals A Review on Computer Vision Technology for Monitoring Poultry Farm – Application, Hardware, and Software

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Nur Syazarin Natasha Abd Aziz ◽  
Salwani Mohd Daud ◽  
Rudzidatul Akmam Dziyauddin ◽  
Mohamad Zulkefli Adam ◽  
Azizul Azizan
Keyword(s):  
Computer Vision ◽  
Poultry Farm ◽  
Computer Vision Technology

scholarly journals TomoSAR Mapping of 3D Forest Structure: Contributions of L-Band Configurations

2021 ◽  
Vol 13 (12) ◽  
pp. 2255
Author(s):  
Matteo Pardini ◽  
Victor Cazcarra-Bes ◽  
Konstantinos Papathanassiou
Keyword(s):  
Information Content ◽  
Forest Structure ◽  
Structural Information ◽  
3D Structure ◽  
Physical Structure ◽  
Structure Mapping ◽  
Structure Information ◽  
Forest Sites ◽  
L Band ◽  
Structure Indices

Synthetic Aperture Radar (SAR) measurements are unique for mapping forest 3D structure and its changes in time. Tomographic SAR (TomoSAR) configurations exploit this potential by reconstructing the 3D radar reflectivity. The frequency of the SAR measurements is one of the main parameters determining the information content of the reconstructed reflectivity in terms of penetration and sensitivity to the individual vegetation elements. This paper attempts to review and characterize the structural information content of L-band TomoSAR reflectivity reconstructions, and their potential to forest structure mapping. First, the challenges in the accurate TomoSAR reflectivity reconstruction of volume scatterers (which are expected to dominate at L-band) and to extract physical structure information from the reconstructed reflectivity is addressed. Then, the L-band penetration capability is directly evaluated by means of the estimation performance of the sub-canopy ground topography. The information content of the reconstructed reflectivity is then evaluated in terms of complementary structure indices. Finally, the dependency of the TomoSAR reconstruction and of its structural information to both the TomoSAR acquisition geometry and the temporal change of the reflectivity that may occur in the time between the TomoSAR measurements in repeat-pass or bistatic configurations is evaluated. The analysis is supported by experimental results obtained by processing airborne acquisitions performed over temperate forest sites close to the city of Traunstein in the south of Germany.

scholarly journals Survey of rice seed quality analysis for varietal purity estimation by using image processing techniques

2018 ◽  
Vol 7 (1.7) ◽  
pp. 34
Author(s):  
S. Durai ◽  
C. Mahesh ◽  
T. Sujithra ◽  
A. Suresh
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
South India ◽  
Seed Quality ◽  
Quality Analysis ◽  
Rice Seed ◽  
Two Factors ◽  
Computer Vision Technology ◽  
Processing Techniques ◽  
Varietal Purity

 In south India rice is the major food source and in agriculture, rice production covers more than 70 percentages of entire forming. But in recent the production only from south India not enough to satisfy the need of all, such a huge demand is there. The better production comes from the selection of good seeds. Up to now formers depend on two factors for selecting better seeds, One is the brand which is approved by some quality standards and second one is analyzed manually by experienced people. Both are risky one, we are not pretty much sure the accuracy of analyze. The second one is seeing and feeling. The inspection is not consistent also very time consuming. In the other way we can use computer vision technology to analyze the quality of the seeds. In recent years many of the big industries they are using computer vision technology with Digital Image Processing for many of the applications. In this Paper we are going to discuss the different seed quality analyzing methods and accuracy of result also. Moreover there are different factors and features are there for it, here we are going to study about varietal purity estimation by different methods.

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