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Two Papers Accepted by Computer Graphics International 2024

(June 2nd, 2024)

Article Links/PDFs (Open Access)

  • Fast image recoloring for red–green anomalous trichromacy with contrast enhancement and naturalness preservation
    Haiqiang Zhou, Wangkang Huang, Zhenyang Zhu, Xiaodiao Chen, Kentaro Go, Xiaoyang Mao
    (Link|PDF)
  • Personalized hairstyle and hair color editing based on multi-feature fusion
    Jiayi Xu, Chenming Zhang, Weikang Zhu, Hongbin Zhang, Li Li, Xiaoyang Mao
    (Link|PDF)

Conference Homepage

Research demo given to visiting group from Soochow University

(2024.01.17)
GE Chao: FoV inspection using VR
JU Yixuan: Face editing using deep learning
Song Ziwei: Anomaly detection for onion using deep learning
ISHIKAWA Naohiko: Image editing for perceptual size restoration
TAMURA Shun: AR and deep learning for grape thinning support
YU Jun Wei: Color communication support for color vision deficiency compensation

Manuscript Titled “3D Grape Bunch Model Reconstruction from 2D Images” has been Accepted for Publication in Computers and Electronics in Agriculture.

Our research team is pleased to announce that our manuscript titled “3D Grape Bunch Model Reconstruction from 2D Images” has been accepted for publication in the journal Computers and Electronics in Agriculture.

This paper, authored by Yan San Woo, Zhuguang Li, Shun Tamura, Prawit Buayai, Hiromitsu Nishizaki, Koji Makino, Latifah Munirah Kamarudin, and Xiaoyang Mao, introduces a pioneering technology for reconstructing a 3D model of a grape bunch, with each berry uniquely identified, using 2D images captured in real grape field conditions. The development of this technology is set to reshape the grape cultivation landscape. By providing an accurate 3D model of grape bunches, it offers an innovative approach to automate berry counting and identification, enhancing the efficiency of the thinning process. This technology is particularly significant in the agriculture industry, where precision and automation are becoming increasingly vital.

This accomplishment underscores our commitment to advancing agricultural technology and contributing valuable insights to the industry. We extend our gratitude to our co-authors, contributors, and readers for their constant support.

The full paper can be accessed here.

“Lightweight System for Berry Number Prediction” Accepted for Publication in Computers and Electronics in Agriculture

Our research team is pleased to announce that our manuscript titled “End-to-End Lightweight Berry Number Prediction for Supporting Table Grape Cultivation,” has been accepted for publication in the journal Computers and Electronics in Agriculture.
This paper, authored by Yan San Woo, Prawit Buayai, Hiromitsu Nishizaki, Koji Makino, Latifah Munirah Kamarudin, and Xiaoyang Mao, introduces a novel lightweight system developed to automate the berry thinning process in table grape cultivation. The system incorporates advanced object detection techniques and is optimized for edge computing devices, which allows it to achieve high accuracy while maintaining a small computational footprint. This makes it particularly suitable for practical applications, even in locations with limited internet connectivity.
It is our hope that the results and insights provided in this paper will be a valuable contribution to the ongoing research and discussions in the field of agriculture technology. We extend our gratitude to our co-authors, contributors, and readers for their constant support.
The full paper can be accessed here.