no code implementations • 18 Apr 2024 • Gautham Vinod, Jiangpeng He, Zeman Shao, Fengqing Zhu
Image-based methods to analyze food images have alleviated the user burden and biases associated with traditional methods.
no code implementations • 30 Jan 2024 • Chen Bai, Zeman Shao, Guoxiang Zhang, Di Liang, Jie Yang, Zhuorui Zhang, Yujian Guo, Chengzhang Zhong, Yiqiao Qiu, Zhendong Wang, Yichen Guan, Xiaoyin Zheng, Tao Wang, Cheng Lu
Our proposed general framework encompasses three key processes: 1) integrating a realistic object into a given scene video with proper placement to ensure geometric realism; 2) estimating the sky and environmental lighting distribution and simulating realistic shadows to enhance the light realism; 3) employing a style transfer network that refines the final video output to maximize photorealism.
no code implementations • 3 Aug 2023 • Zeman Shao, Gautham Vinod, Jiangpeng He, Fengqing Zhu
Dietary assessment is a key contributor to monitoring health status.
no code implementations • 25 Aug 2022 • Gautham Vinod, Zeman Shao, Fengqing Zhu
In this paper, we propose an "Energy Density Map" which is a pixel-to-pixel mapping from the RGB image to the energy density of the food.
no code implementations • 5 Jun 2022 • Zeman Shao, Jiangpeng He, Ya-Yuan Yu, Luotao Lin, Alexandra Cowan, Heather Eicher-Miller, Fengqing Zhu
Food classification is critical to the analysis of nutrients comprising foods reported in dietary assessment.
no code implementations • 5 Oct 2021 • Zeman Shao, Yue Han, Jiangpeng He, Runyu Mao, Janine Wright, Deborah Kerr, Carol Boushey, Fengqing Zhu
Accurate assessment of dietary intake requires improved tools to overcome limitations of current methods including user burden and measurement error.
no code implementations • 6 Sep 2021 • Runyu Mao, Jiangpeng He, Luotao Lin, Zeman Shao, Heather A. Eicher-Miller, Fengqing Zhu
Image-based dietary assessment refers to the process of determining what someone eats and how much energy and nutrients are consumed from visual data.
no code implementations • 12 Mar 2021 • Zeman Shao, Shaobo Fang, Runyu Mao, Jiangpeng He, Janine Wright, Deborah Kerr, Carol Jo Boushey, Fengqing Zhu
We aim to estimate food portion size, a property that is strongly related to the presence of food object in 3D space, from single monocular images under real life setting.
no code implementations • 1 Feb 2021 • Jiangpeng He, Runyu Mao, Zeman Shao, Janine L. Wright, Deborah A. Kerr, Carol J. Boushey, Fengqing Zhu
Our end-to-end framework is evaluated on a real life food image dataset collected from a nutrition feeding study.
no code implementations • 6 Dec 2020 • Runyu Mao, Jiangpeng He, Zeman Shao, Sri Kalyan Yarlagadda, Fengqing Zhu
Experimental results demonstrate that our system can significantly improve both classification and recognition performance on 4 publicly available datasets and the new VFN dataset.
no code implementations • 27 Apr 2020 • Jiangpeng He, Zeman Shao, Janine Wright, Deborah Kerr, Carol Boushey, Fengqing Zhu
Deep learning based methods have achieved impressive results in many applications for image-based diet assessment such as food classification and food portion size estimation.
no code implementations • CVPR 2020 • Jiangpeng He, Runyu Mao, Zeman Shao, Fengqing Zhu
Modern deep learning approaches have achieved great success in many vision applications by training a model using all available task-specific data.
no code implementations • 11 Oct 2019 • Zeman Shao, Runyu Mao, Fengqing Zhu
The web crawler is used to download large sets of online food images based on the given food labels.
no code implementations • 27 Feb 2018 • Shaobo Fang, Zeman Shao, Runyu Mao, Chichen Fu, Deborah A. Kerr, Carol J. Boushey, Edward J. Delp, Fengqing Zhu
We can then estimate food energy based on the energy distribution.