geoTangle : Interactive Design of Geodesic Tangle Patterns on Surfaces

2022 ◽  
Vol 41 (2) ◽  
pp. 1-17
Author(s):  
Giacomo Nazzaro ◽  
Enrico Puppo ◽  
Fabio Pellacini
Keyword(s):  
Real World ◽  
Interactive Design ◽  
Object Surface ◽  
Intrinsic Metric ◽  
Model Complex ◽  
Integral Curves ◽  
Interactive Application ◽  
Complex Patterns ◽  
Digital Shapes

Tangles are complex patterns, which are often used to decorate the surface of real-world artisanal objects. They consist of arrangements of simple shapes organized into nested hierarchies, obtained by recursively splitting regions to add progressively finer details. In this article, we show that 3D digital shapes can be decorated with tangles by working interactively in the intrinsic metric of the surface. Our tangles are generated by the recursive application of only four operators, which are derived from tracing the isolines or the integral curves of geodesics fields generated from selected seeds on the surface. Based on this formulation, we present an interactive application that lets designers model complex recursive patterns directly on the object surface without relying on parametrization. We reach interactive speed on meshes of a few million triangles by relying on an efficient approximate graph-based geodesic solver.

Problem Posing and Solving with Mathematical Modeling

2005 ◽  
Vol 12 (3) ◽  
pp. 156-163 ◽  
Author(s):  
Lyn D. English ◽  
Jillian L. Fox ◽  
James J. Watters
Keyword(s):  
Mathematical Modeling ◽  
Elementary School ◽  
School Children ◽  
Real World ◽  
Elementary School Children ◽  
Problem Posing ◽  
Model Complex ◽  
Complex Relationships

In recent years, we have introduced elementary school children to the powerful world of mathematical modeling. Models are used to interpret real-world situations in a mathematical format. For example, graphs and tables model complex relationships among various phenomena.

scholarly journals Prior-knowledge based Green's kernel for support vector regression

2021 ◽  
Author(s):  
Tahir Farooq
Keyword(s):  
Support Vector Regression ◽  
Prior Knowledge ◽  
Kernel Function ◽  
Real World ◽  
Matched Filter ◽  
Kernel Functions ◽  
Support Vector ◽  
Mathematical Framework ◽  
Knowledge Based ◽  
Model Complex

This thesis presents a novel prior knowledge based Green's kernel for support vector regression (SVR) and provides an empirical investigation of SVM's (support vector machines) ability to model complex real world problems using a real dataset. After reviewing the theoretical background such as theory SVM, the correspondence between kernels functions used in SVM and regularization operators used in regularization networks as well as the use of Green's function of their corresponding regularization operators to construct kernel functions for SVM, a mathematical framework is presented to obtain the domain knowledge about the magnitude of the Fourier transform of the function to be predicted and design a prior knowledge based Green's kernel that exhibits optimal regularization properties by using the concept of matched filters. The matched filter behavior of the proposed kernel function provides the optimal regularization and also makes it suitable for signals corrupted with noise that includes many real world systems. Several experiments, mostly using benchmark datasets ranging from simple regression models to non-linear and high dimensional chaotic time series, have been conducted in order to compare the performance of the proposed technique with the results already published in the literature for other existing support vector kernels over a variety of settings including different noise levels, noise models, loss functions and SVM variations. The proposed kernel function improves the best known results by 18.6% and 24.4% on a benchmark dataset for two different experimental settings.

scholarly journals Pengembangan Sistem Panduan Mobile Dengan Audio dan Teks Berdasarkan Augmented Reality (Android) Untuk Pengunjung di Museum Bali

AITI ◽  
2018 ◽  
Vol 15 (1) ◽  
pp. 27-37
Author(s):  
Feoh Feoh ◽  
Christina Purnama Yanti
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Virtual World ◽  
User Studies ◽  
3D Object ◽  
The Real ◽  
3D Objects ◽  
Interactive Application

The Bali museum is one of the museums in which objects of artistic, cultural and historical of Balinese against the invaders are kept. However, there are still few visitors coming to the museum. One way to increase a number of visitors is by using a technology in the form of interactive application for them. This research made visitors of Bali museum as subjects /user studies, while the designed application was aimed at knowing the users’ responses toward it. Previously, a study of mobile-based AR had been conducted to see the needs of the users. A guide in the form of text and sound were also added. Augmented Reality is a technology that combines virtual world and the real world simultaneously. AR technology requires a camera to capture the picture of marker whichwill display the 3D object from one of the Bali museum’s collections. The 3D object was created with software marker of 3D objects. In this study, both software blender and software unity were used to develop 3D object and the application of AR. The application runs on in 5 inch – screen Android devices and provide information in the form of Indonesian text and audio bilingual, both in Indonesian and English.

scholarly journals A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

2021 ◽  
Author(s):  
Vignesh Sampath ◽  
Iñaki Maurtua ◽  
Juan José Aguilar Martín ◽  
Aitor Gutierrez
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Medical Image Analysis ◽  
Image Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Metallic Surface ◽  
Synthetic Image ◽  
Application Development ◽  
Model Complex

Abstract Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

scholarly journals A survey on generative adversarial networks for imbalance problems in computer vision tasks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vignesh Sampath ◽  
Iñaki Maurtua ◽  
Juan José Aguilar Martín ◽  
Aitor Gutierrez
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Medical Image Analysis ◽  
Image Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Metallic Surface ◽  
Synthetic Image ◽  
Application Development ◽  
Model Complex

AbstractAny computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

Parasites and Pattern Formation

1999 ◽  
Vol 54 (2) ◽  
pp. 146-152 ◽  
Author(s):  
J. Ackermann ◽  
T. Kirner
Keyword(s):  
Pattern Formation ◽  
Flow Reactor ◽  
Spiral Waves ◽  
Cooperative Interaction ◽  
Biological Information ◽  
Parameter Region ◽  
Parasitic Species ◽  
Model Complex ◽  
Complex Patterns ◽  
The Stability

Abstract Biological information is coded in replicating molecules. To maintain a given amount of in-formation a cooperative interaction between these molecules is essential. The main problem for the stability of a system of prebiotic replicators are emerging parasites. Stabilization against such parasites is possible if space is introduced in the model. Complex patterns like spiral waves and self-replicating spot patterns have been shown to stabilize such systems. Stability of replicating systems, however, occurs only in parameter regions were such complex patterns occur. We show that parasites are able to push such systems into a parameter region were life is possible. To demonstrate this influence of parasites on such systems, we introduce a parasitic species in the Gray-Scott model. The growing concentration of parasites will kill the system, and the cooperative Gray-Scott system will be diluted out in a well mixed flow reactor. While considering space, in the model stabilizing pattern formation in a narrow parameter region is possible. We demonstrate that the concentration of the parasitic species is able to push the system into a region were stabilizing patterns emerge.

scholarly journals A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

2020 ◽  
Author(s):  
Vignesh Sampath ◽  
Iñaki Maurtua ◽  
Juan José Aguilar Martín ◽  
Aitor Gutierrez
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Medical Image Analysis ◽  
Image Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Metallic Surface ◽  
Synthetic Image ◽  
Adversarial Networks ◽  
Model Complex

Abstract Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

Design of Virtual Store Roaming System Based on Virtools

2012 ◽  
Vol 190-191 ◽  
pp. 273-276
Author(s):  
Guo Qiang Shen ◽  
Jing Jing Xu
Keyword(s):  
Real World ◽  
Collision Detection ◽  
Interactive Design ◽  
Two Dimensional ◽  
Virtual Scene ◽  
The Real ◽  
Virtual Store

According to the phenomenon that most website display products through the form of two-dimensional and can not resolve interactive issue between users and virtual scene. This paper design a virtual roaming system which uses 3ds max to perform modeling for all the scenes in the store and make dynamic interactive design for model on Virtools platform, and study two types of collision detection. In this roaming system, users can walk freely in the store and operate interested in commodities independently like entering the real world.

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