scholarly journals An Empirical Study on the Artificial Intelligence-Aided Quantitative Design of Art Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wen Zhang ◽  
Sang-Bing Tsai
Keyword(s):  
Artificial Intelligence ◽  
Network Model ◽  
Automatic Generation ◽  
Fine Art ◽  
Image Generation ◽  
Bilateral Filtering ◽  
Color Quantization ◽  
Main Research ◽  
Filtering Algorithm ◽  
Quantitative Design

This paper presents an indepth analysis and research on the quantitative design of fine art images through artificial intelligence algorithms. A CycleGAN-based network model for automatic generation of sketches of fine art images is constructed to extract the edge and contour features of fine art images. The network uses 512 × 1024 high-resolution art images as input and Pitchman as a discriminator. To further enhance the sketch generation effect, a bilateral filtering algorithm is added to the generator model for noise reduction, and then a K -means algorithm is used for color quantization to solve the problem of cluttered lines in the generated sketches. The experimental results show that the network model can effectively realize the automatic generation of art image sketches and can retain the detailed part of the costume information well. A rendering platform is built to realize the application of art image generation algorithms and coloring algorithms. The platform integrates the functions of image preprocessing, sketch generation, and sketch coloring, demonstrates the results of the main research content of this paper, and finally increases the interest of the system through the rendering function of the art image grid, which further improves the practicality of the platform.

Automatic Story Generation

2021 ◽  
Vol 54 (5) ◽  
pp. 1-38
Author(s):  
Arwa I. Alhussain ◽  
Aqil M. Azmi
Keyword(s):  
Artificial Intelligence ◽  
Automatic Generation ◽  
Evaluation Methods ◽  
Extensive Study ◽  
Computational Creativity ◽  
Story Generation ◽  
To Receive ◽  
Shed Light

Computational generation of stories is a subfield of computational creativity where artificial intelligence and psychology intersect to teach computers how to mimic humans’ creativity. It helps generate many stories with minimum effort and customize the stories for the users’ education and entertainment needs. Although the automatic generation of stories started to receive attention many decades ago, advances in this field to date are less than expected and suffer from many limitations. This survey presents an extensive study of research in the area of non-interactive textual story generation, as well as covering resources, corpora, and evaluation methods that have been used in those studies. It also shed light on factors of story interestingness.

scholarly journals Cloud based mobile solution for early detection of Skin Cancer using Artificial Intelligence

2021 ◽  
pp. 312-324
Author(s):  
Pawan Sonawane ◽  
Sahel Shardhul ◽  
Raju Mendhe
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Skin Cancer ◽  
Early Detection ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Mobile Application ◽  
Low Cost ◽  
Skin Tumors

The vast majority of skin cancer deaths are from melanoma, with about 1.04 million cases annually. Early detection of the same can be immensely helpful in order to try to cure it. But most of the diagnosis procedures are either extremely expensive or not available to a vast majority, as these centers are concentrated in urban regions only. Thus, there is a need for an application that can perform a quick, efficient, and low-cost diagnosis. Our solution proposes to build a server less mobile application on the AWS cloud that takes the images of potential skin tumors and classifies it as either Malignant or Benign. The classification would be carried out using a trained Convolution Neural Network model and Transfer learning (Inception v3). Several experiments will be performed based on Morphology and Color of the tumor to identify ideal parameters.

scholarly journals Artificial intelligence and copyright

2021 ◽  
pp. 15-22
Author(s):  
Daryna Prylypko
Keyword(s):  
Artificial Intelligence ◽  
Computer Program ◽  
Key Words ◽  
Automatic Generation ◽  
The Other ◽  
International Level ◽  
Human Agent ◽  
Foreign Countries ◽  
The One ◽  
Justice Practice

Key words: copyright, work, artificial intelligence, computer program In the article, the problemsof legislation of Ukraine regarding the issues of copyright on works created due to artificialintelligence were analyzed. Particularly, who is the owner of copyright ofworks created due to artificial intelligence. On the one hand, it could be a developer ofa computer program, from the other hand, it could be a client or an employer. Because,it could happen that there is a situation when robots created something newand original, e.g., how it happened with the project “New Rembrandt”. In this case,computers created a unique portrait of Rembrandt. And here is a question, where isin this portrait original and intellectual works of developers of these computers andprograms. In the contrast, this portrait could be created without people who developedspecial machines, programs, and computers. The article’s author proposes to addinto Ukrainian legislation with following norm: the owner of the copyright createddue to artificial intelligence should be a natural person who uses artificial intelligencefor these purposes within the official relationship or on the basis of a contract. In caseof automatic generation of such work by artificial intelligence, the owner of copyrightshould be the developer.Also, another question arises, particularly, who will be responsible for the damagecaused by the artificial intelligence. As an example, of the solution for this issue Resolution2015/2103 (INL) was given, where is mentioned that human agent could be responsiblefor the caused damage. Because, it is not always a developer is responsiblefor the damage.Also, the legislation and justice practice of foreign countries was explored. Theways of overcoming mentioned problems in legislation of Ukraine were proposed.Such as changing our legislation and giving the exact explanation in who is the ownerof copyright on works created due to artificial intelligence and in which cases this personcould become an owner of the copyright. However, probably, these issues shouldbe resolved at international level regarding globalization.

Improved Adaptive Bilateral Filtering Algorithm

2020 ◽  
Vol 57 (4) ◽  
pp. 041003
Author(s):  
白晓东 Bai Xiaodong ◽  
舒勤 Shu Qin ◽  
杜小燕 Du Xiaoyan ◽  
黄燕琴 Huang Yanqin
Keyword(s):  
Bilateral Filtering ◽  
Filtering Algorithm

A fast bilateral filtering algorithm based on rising cosine function

2019 ◽  
Vol 31 (9) ◽  
pp. 5097-5108
Author(s):  
Jie Guo ◽  
Changhai Chen ◽  
Shoubing Xiang ◽  
Yang Ou ◽  
Bailin Li
Keyword(s):  
Cosine Function ◽  
Bilateral Filtering ◽  
Filtering Algorithm

scholarly journals A Refined Bilateral Filtering Algorithm Based on Adaptively-Trimmed-Statistics for Speckle Reduction in SAR Imagery

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 103443-103455 ◽  
Author(s):  
Jiaqiu Ai ◽  
Ruiming Liu ◽  
Bo Tang ◽  
Lu Jia ◽  
Jinling Zhao ◽  
...  
Keyword(s):  
Speckle Reduction ◽  
Bilateral Filtering ◽  
Filtering Algorithm ◽  
Sar Imagery

scholarly journals A Focus on Soft Actuation

Actuators ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 74 ◽  
Author(s):  
Miriyev
Keyword(s):  
Artificial Intelligence ◽  
Functional Materials ◽  
Soft Material ◽  
Soft Robotics ◽  
Functional Material ◽  
Research Focus ◽  
Main Research ◽  
Soft Actuator ◽  
Grand Challenges ◽  
Actuator Design

The present editorial paper analyzes the hundred recent research works on soft actuation to understand the current main research focus in the light of the grand challenges in the field. Two characteristic paper types were obtained: one focuses on soft actuator design, manufacturing and demonstration, while another includes in addition the development of functional materials. Although vast majority of the works showcased soft actuation, evaluation of its robustness by multi-cyclic actuation was reported in less than 50% of the works, while only 10% described successful actuation for more than 1000 cycles. It is suggested that broadening the research focus to include investigation of mechanisms underlying the degradation of soft functional material performance in real cyclic actuation conditions, along with application of artificial intelligence methods for prediction of muscle behavior, may allow overcoming the reliability issues and developing robust soft-material actuators. The outcomes of the present work might be applicable to the entire soft robotics domain.

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