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All Journal Journal of Information Systems and Informatics The Indonesian Journal of Computer Science
Tu, Chunling
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Advanced 3D Artistic Image Generation with VAE-SDFCycleGAN Esan, Dorcas Oladayo; Owolawi, Pius Adewale; Tu, Chunling
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.900

Abstract

Generation of a 3-dimensional (3D)-based artistic image from a 2-dimensional (2D) image using a generative adversarial network (GAN) framework is challenging. Most existing artistic GAN-based frameworks lack robust algorithms lack suitable 3D data representations that can fit into GAN to produce high-quality 3D artistic images. To produce 3D artistic images from 2D image that considerably improves scalability and visual quality, this research integrates innovative variational autoencoder signed distance function, cycle generative adversarial network (VAE-SDFCycleGAN). The proposed method feeds a single 2D image into the network to produce a mesh-based 3D shape. The network encodes a 2D image of the 3D object into latent representations, and implicit surface representations of 3D images corresponding to those of 2D images are subsequently generated. VAE extracts feature from the two-dimensional input image and reconstructs a voxel-type grid using a signed distance function. Cycle GAN produces improved and high-quality 3D artistic images from 2D images. The publicly available COCO dataset was used to evaluate the proposed advanced 3D-VAE-SDFCycleGAN. The model produced a peak signal noise ratio (PSNR) of 31.35, mean square error (MSE) of 65.32, and structural similarity index measure (SSIM) of 0.772 which indicates the improved quality of the generated images. The results are compared with other traditional GAN methods and the results obtained show that the proposed method outperforms the others in terms of quantitative and qualitative evaluation metrics.
3D Image Generation Using Generative Adversarial Network for Virtual Art Gallery Esan, Dorcas; Owolawi, Pius Adewale; Tu, Chunling
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4505

Abstract

Gallery art websites are often used to display artistic work for user accessibility. The conventional web-based gallery is built on the hyper-text Markup Language (HTML) which is limited in terms of content dynamism, interactivity, and scalability. GANs provide advanced capabilities with continuously evolving art experiences, making them a powerful tool for modern digital art galleries. Artistic websites have significantly contributed to the exhibition of artistic images since they can be accessed anywhere. However, the artistic web suffers from being too passive and lacks in-depth interactivity to keep people meaningfully engaged with an exhibition virtually. This paper explores the exhibition of 3D images within a virtual art gallery using an intelligent artistic web-based applications framework that integrates Variational Autoencoder and 3-D Signed Distance Function Cycle GAN (VAE-3DSDFCycleGAN) and quantitative questionnaire methods. The virtual gallery utilises GAN architectures to produce diverse and original 3D artworks, addressing traditional art galleries' spatial, viewing dimensions, image quality, and accessibility limitations. The questionnaire was used to evaluate the user’s satisfaction. The experiment was done on the Coco African Mask dataset to generate 3-D images, yielding a high result and satisfaction in terms of the ease of use and viewing of the artistic image contents.
A Real-time Internet of Things-Based Wireless Livestock Tracking System for Theft Prevention Sandlana, Muzi; Mathonsi, Topside E.; Deon du Plessis; Tu, Chunling
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i3.4910

Abstract

Livestock theft is a significant threat to the agricultural industry, necessitating innovative preventive strategies. This study proposes a Wireless Livestock Tracking System (WLTS) that uses real-time Internet of Things (IoT) technologies to prevent livestock theft. The WLTS integrates GPS sensors with Long Range Radio (LoRa) wireless communication modules, overcoming the limitations of Wi-Fi and Bluetooth-based systems. It uses a single LoRa network receiver to facilitate real-time communication between farmers and their livestock. Simulation results show the WLTS effectively mitigates livestock theft, enabling farmers to quickly identify and recover stolen animals. Geofencing alerts enhance the system's sensitivity to potential theft scenarios. The WLTS has a user-friendly interface, allowing farmers to remotely monitor their livestock. Data analytics capabilities enable predictive analysis of probable theft trends based on historical data. The findings pave the way for practical implementation, revolutionizing livestock protection and safeguarding farmers' livelihoods worldwide.
Co-Authors Deon du Plessis Esan, Dorcas Esan, Dorcas Oladayo Mathonsi, Topside E. Owolawi, Pius Adewale Sandlana, Muzi