Review
Food Science & Technology
Hanieh Amani, Katalin Badak-Kerti, Amin Mousavi Khaneghah
Summary: The smartphone has gained attention in food quality assessment due to its high-resolution cameras and programmability. It shows potential as a nondestructive technique for quality control, but challenges in implementation and industrialization remain.
CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION
(2022)
Article
Agronomy
Li Ma, Yuanhui Hu, Yao Meng, Zhiyi Li, Guifen Chen
Summary: This article proposes an improved residual-network-based model for plant disease recognition, achieving good experimental results. The model maintains a smaller number of parameters and computational requirements while improving the average recognition accuracy. Therefore, this model has significant potential for widespread application.
Review
Agriculture, Multidisciplinary
Harsh Pathak, C. Igathinathane, Z. Zhang, D. Archer, J. Hendrickson
Summary: The use of unmanned aerial vehicles (UAV) and computer vision algorithms in evaluating plant stand count has been reviewed in this study. It is concluded that image acquisition at an appropriate stage and height, along with suitable color space and camera imagery, can improve the accuracy of plant stand count. Other findings include the effectiveness of deep learning models and the application of direct image processing and open-source platforms. This review provides valuable guidance for farmers, producers, and researchers in selecting and employing UAV-based algorithms for plant stand count evaluation.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Computer Science, Artificial Intelligence
Lukasz Karbowiak, Janusz Bobulski
Summary: This article introduces the importance of background segmentation and proposes a method to compare algorithms under severe weather conditions. Through testing in different weather conditions, interesting differences in detail detection and detection noise were observed.
PEERJ COMPUTER SCIENCE
(2022)
Article
Computer Science, Software Engineering
Christoph Praschl, Andreas Pointner, David Baumgartner, Gerald Adam Zwettler
Summary: The paper introduces the open-source Imaging Framework, which addresses the interoperability issue among multiple Java-based frameworks and provides an extendable foundation for handling and combining different image projects.
Article
Computer Science, Software Engineering
Zhicheng Lu, Xiaoming Chen, Vera Yuk Ying Chung, Weidong Cai, Yiran Shen
Summary: This article proposes an event synthesis framework EV-LFV which utilizes one event camera and multiple traditional RGB cameras to generate full multi-subview event-based RGB-LFV. EV-LFV models various features of RGB-LFV through spatial-angular convolution, ConvLSTM, and Transformer for effective synthesis of event streams. Experimental results show that EV-LFV outperforms other methods and effectively alleviates motion blur in reconstructed RGB-LFV.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2023)
Article
Computer Science, Artificial Intelligence
Rui Dou, Jiawen Li, Xujie Wan, Heyou Chang, Hao Zheng, Guangwei Gao
Summary: The article introduces an architecture called the Decoder Structure Guided CNN-Transformer Network (DCTNet) for face image super-resolution. DCTNet utilizes a decoder structure as its backbone, focusing primarily on Global-Local Feature Extraction Units (GLFEU).
IET COMPUTER VISION
(2023)
Article
Computer Science, Artificial Intelligence
Fugang Liu, Songnan Duan, Wang Juan
Summary: In this study, a deep learning-based method for pedestrian trajectory prediction is proposed. The method combines YOLOv7, StrongSORT, and improved LSTM algorithm to solve the problems of target switch and jump, and improves the prediction performance.
IET IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Farzane Maghsoudi Ghombavani, Mohammad Javad Fadaeieslam, Farzin Yaghmaee
Summary: This paper introduces a generative adversarial network model called ARDA-UNIT, which aims to tackle the challenges of image-to-image translation by generating images close to the target domain while preserving important features of the source domain. The model enhances its generating capability and reduces training parameters by applying a recurrent dense self-attention module in the generator latent space. Experimental results demonstrate that the model achieves better qualities by reducing computational loads, transferring structures effectively, and improving evaluation criteria such as FID, KID, and IS.
IET IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Hao Zhang, Yan Piao, Nan Qi
Summary: The authors propose a novel Transformer-based tracker that fuses spatial and temporal features, achieving comprehensive feature extraction and improved information interaction. The tracker achieves state-of-the-art results on multiple benchmark datasets.
IET COMPUTER VISION
(2023)
Article
Computer Science, Artificial Intelligence
Lingbing Meng, Mengya Yuan, Xuehan Shi, Le Zhang, Qingqing Liu, Dai Ping, Jinhua Wu, Fei Cheng
Summary: An end-to-end framework for RGB-D salient object detection (SOD) is proposed, which includes multiple modules for feature enhancement, contextual feature interaction, boundary feature extraction, and boundary attention guidance. The proposed model outperforms state-of-the-art RGB-D SOD models on multiple evaluation metrics.
IET COMPUTER VISION
(2023)
Article
Computer Science, Artificial Intelligence
Zhanqiang Huo, Yanan Wang, Yingxu Qiao, Jing Wang, Fen Luo
Summary: Crowd counting is crucial in computer vision, aiming to estimate the number of people in an image. By regressing density maps, researchers have greatly improved the counting accuracy in recent years. However, due to domain shift, models trained on richly labeled datasets (source domain) do not perform well on datasets with limited labels (target domain). To address this issue, a novel dynamic scale aggregation network (DSANet) is proposed to bridge the gap in style and cross-domain head scale variations.
IET COMPUTER VISION
(2023)
Article
Environmental Sciences
William Yamada, Wei Zhao, Matthew Digman
Summary: An automatic method using monovision un-crewed aerial vehicle imagery was developed to obtain geographic coordinates of bales, with YOLOv3 algorithm identified as the best option in terms of accuracy and speed. Lowering image quality resulted in decreased performance.
Article
Computer Science, Artificial Intelligence
Xiaowei Yang, Yong Zhao, Zhiguo Feng, Haiwei Sang, Zhenbo Zhang, Guiying Zhang, Lin He
Summary: In this paper, a lightweight end-to-end network (LWNet) for fast stereo matching is proposed, which achieves competitive accuracy and speed compared to state-of-the-art methods. The network consists of an efficient backbone, 3D U-Net aggregation architecture, and color guidance for disparity computation and refinement.
IET IMAGE PROCESSING
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Sijuan Huang, Zesen Cheng, Lijuan Lai, Wanjia Zheng, Mengxue He, Junyun Li, Tianyu Zeng, Xiaoyan Huang, Xin Yang
Summary: The study aims to create a network utilizing multi-sequence MRI for automatic contouring and compared its performance with manual human contouring. Results showed that the proposed network outperformed baseline models in all metrics. Additionally, it was found that three-sequence fusion (T1-T1DIXONC-T2) was superior to two-sequence fusion (T1-T2 and T1-T1DIXONC).
Article
Computer Science, Artificial Intelligence
Paulo E. Santos, Murilo F. Martins, Valquiria Fenelon, Fabio G. Cozman, HannahM. Dee
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
(2016)
Editorial Material
Computer Science, Artificial Intelligence
Hanno Scharr, Hannah Dee, Andrew P. French, Sotirios A. Tsaftaris
MACHINE VISION AND APPLICATIONS
(2016)
Review
Computer Science, Artificial Intelligence
Jonathan Bell, Hannah M. Dee
IET COMPUTER VISION
(2017)
Article
Computer Science, Artificial Intelligence
Valquiria Fenelon, Paulo E. Santos, Hannah M. Dee, Fabio G. Cozman
APPLIED INTELLIGENCE
(2013)
Article
Computer Science, Artificial Intelligence
Hannah M. Dee, David C. Hogg
ARTIFICIAL INTELLIGENCE
(2009)
Article
Computer Science, Artificial Intelligence
Hannah M. Dee, Anthony G. Cohn, David C. Hogg
COMPUTER VISION AND IMAGE UNDERSTANDING
(2012)
Article
Computer Science, Artificial Intelligence
Ngoc-Son Vu, Hannah M. Dee, Alice Caplier
PATTERN RECOGNITION
(2012)
Article
Dermatology
Alassane Seck, Hannah Dee, William Smith, Bernard Tiddeman
SKIN RESEARCH AND TECHNOLOGY
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
William A. P. Smith, Alassane Seck, Hannah Dee, Bernard Tiddeman, Joshua B. Tenenbaum, Bernhard Egger
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
(2020)
Article
Psychology, Experimental
Charles C. Newey, Owain D. Jones, Hannah M. Dee
SPATIAL COGNITION AND COMPUTATION
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Alassane Seck, Hannah Dee, Bernard Tiddeman
2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
(2014)
Proceedings Paper
Computer Science, Artificial Intelligence
Matthew Pugh, Bernard Tiddeman, Hannah Dee, Philip Hughes
2014 ICPR WORKSHOP ON COMPUTER VISION FOR ANALYSIS OF UNDERWATER IMAGERY (CVAUI 2014)
(2014)
Proceedings Paper
Computer Science, Artificial Intelligence
Alassane Seck, Hannah Dee, Bernard Tiddeman
ADVANCES IN VISUAL COMPUTING, ISVC 2013, PT I
(2013)
Proceedings Paper
Engineering, Electrical & Electronic
Alexander David Brown, Gareth Lloyd Roderick, Hannah M. Dee, Lorna M. Hughes
2013 8TH INTERNATIONAL SYMPOSIUM ON IMAGE AND SIGNAL PROCESSING AND ANALYSIS (ISPA)
(2013)