Quad-Tree-Based Image Shape Coding with Block Compensation

Author(s):  
Lih-Yang Wang ◽  
Ching-Hui Lai ◽  
Kuan-Ren Pan
Keyword(s):  
2008 ◽  
Vol 18 (6) ◽  
pp. 845-850 ◽  
Author(s):  
Zhenliang Shen ◽  
M.R. Frater ◽  
J.F. Arnold
Keyword(s):  

2020 ◽  
Vol 27 (1) ◽  
pp. 29-38
Author(s):  
Teng Zhang ◽  
Junsheng Ren ◽  
Lu Liu

AbstractA three-dimensional (3D) time-domain method is developed to predict ship motions in waves. To evaluate the Froude-Krylov (F-K) forces and hydrostatic forces under the instantaneous incident wave profile, an adaptive mesh technique based on a quad-tree subdivision is adopted to generate instantaneous wet meshes for ship. For quadrilateral panels under both mean free surface and instantaneous incident wave profiles, Froude-Krylov forces and hydrostatic forces are computed by analytical exact pressure integration expressions, allowing for considerably coarse meshes without loss of accuracy. And for quadrilateral panels interacting with the wave profile, F-K and hydrostatic forces are evaluated following a quad-tree subdivision. The transient free surface Green function (TFSGF) is essential to evaluate radiation and diffraction forces based on linear theory. To reduce the numerical error due to unclear partition, a precise integration method is applied to solve the TFSGF in the partition computation time domain. Computations are carried out for a Wigley hull form and S175 container ship, and the results show good agreement with both experimental results and published results.


2014 ◽  
Vol 1 (3) ◽  
pp. 8-17
Author(s):  
Shefali Sharma ◽  
◽  
Ashutosh Kumar Singh ◽  
Rajiv Saxena ◽  
◽  
...  

Author(s):  
Grace L. Samson ◽  
Joan Lu

AbstractWe present a new detection method for color-based object detection, which can improve the performance of learning procedures in terms of speed, accuracy, and efficiency, using spatial inference, and algorithm. We applied the model to human skin detection from an image; however, the method can also work for other machine learning tasks involving image pixels. We propose (1) an improved RGB/HSL human skin color threshold to tackle darker human skin color detection problem. (2), we also present a new rule-based fast algorithm (packed k-dimensional tree --- PKT) that depends on an improved spatial structure for human skin/face detection from colored 2D images. We also implemented a novel packed quad-tree (PQT) to speed up the quad-tree performance in terms of indexing. We compared the proposed system to traditional pixel-by-pixel (PBP)/pixel-wise (PW) operation, and quadtree based procedures. The results show that our proposed spatial structure performs better (with a very low false hit rate, very high precision, and accuracy rate) than most state-of-the-art models.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alena Zhdanava ◽  
Surinderpal Kaur ◽  
Kumaran Rajandran

Abstract Ecolinguistics studies the interactions between language and ecology. It investigates whether the stories created by language are destructive or beneficial to all the constituents of the environment. In search of positive stories for our environment, this article focuses on vegan campaigns which generally bring awareness about veganism that, in turn, advocates protection of nonhuman animals and abstention from their exploitation. Nonhuman animals are part of the ecosystem and the way they are portrayed in language may determine the relationship between human and nonhuman animals. As vegan campaigns refer to nonhuman animals as sentient living beings, it is important to analyze whether the language and image of these campaigns articulate their purposes and create beneficial stories for nonhuman species. This article explores the stories regarding nonhuman animals in 27 posters of the vegan campaign “Go Vegan World” and examines how these stories are shaped and whether they are aligned with vegan values. The study is approached from an ecolinguistic perspective with a focus on multimodality where the language was analyzed through van Leeuwen’s Social Actor and Social Action theory, and the image was analyzed with Kress and van Leeuwen’s Grammar of Visual Design. Further, the analysis involves the ecosophy defined as a personal ecological philosophy of relationships between human and nonhuman animals, plants, and the physical environment. The findings suggest that the campaign language and image shape three stories: salience where nonhuman animals are individuals with their own feelings and lives; conviction that nonhuman animals matter as much as humans; ideology where biocentrism is promoted. By comparing these stories with the article’s ecosophy, an ecolinguistic analysis showed that they are largely beneficial in representing nonhuman animals as sentient living beings who are equal to humans.


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