Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa by Zac Yung-Chun Liu, Andrew J. Chamberlin, Krti Tallam, Isabel J. Jones, Lance L. Lamore, John Bauer, Mariano Bresciani, Caitlin M. Wolfe, Renato Casagrandi, Lorenzo Mari, Marino Gatto, Abdou Ka Diongue, Lamine Toure, Jason R. Rohr, Gilles Riveau, Nicolas Jouanard, Chelsea L. Wood, Susanne H. Sokolow, Lisa Mandle, Gretchen Daily, Eric F. Lambin, Giulio A. De Leo

“…In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computer vision technique called semantic segmentation. …”
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Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives by Giuseppe Quero, Pietro Mascagni, Fiona R. Kolbinger, Claudio Fiorillo, Davide De Sio, Fabio Longo, Carlo Alberto Schena, Vito Laterza, Fausto Rosa, Roberta Menghi, Valerio Papa, Vincenzo Tondolo, Caterina Cina, Marius Distler, Juergen Weitz, Stefanie Speidel, Nicolas Padoy, Sergio Alfieri

“…Artificial intelligence (AI) and computer vision (CV) are beginning to impact medicine. …”
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Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model by Lele Wang, Lele Wang, Yingjie Zhao, Yingjie Zhao, Zhangjun Xiong, Zhangjun Xiong, Shizhou Wang, Shizhou Wang, Yuanhong Li, Yuanhong Li, Yubin Lan, Yubin Lan, Yubin Lan, Yubin Lan, Yubin Lan

“…Factors such as complex growth environment, dense distribution, and random occlusion by leaves, branches, and other litchi fruits easily cause the predicted output based on computer vision deviate from the actual value. This study proposed a fast and precise litchi fruit detection method and application software based on an improved You Only Look Once version 5 (YOLOv5) model, which can be used for the detection and yield estimation of litchi in orchards. …”
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Enhanced open biomass burning detection: The BranTNet approach using UAV aerial imagery and deep learning for environmental protection and health preservation by Hongyu Wang, Zhaomin Yao, Tian Li, Zhan Ying, Xiaodan Wu, Shanhu Hao, Miao Liu, Zhiguo Wang, Tianci Gu

“…To address this limitation and monitor the human living environment more flexibly and accurately, we propose a new method to identify straw fires in UAV Aerial Image Using CNN Branch Reinforce Transformer which named BranTNet, enabling early detection and rapid response to crop straw fires. By integrating computer vision technology and deep learning algorithms, straw fires in UAV-acquired aerial survey images can be detected and categorized. …”
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Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets by Stephanie A. Harmon, Thomas H. Sanford, Sheng Xu, Evrim B. Turkbey, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Amel Amalou, Maxime Blain, Michael Kassin, Dilara Long, Nicole Varble, Stephanie M. Walker, Ulas Bagci, Anna Maria Ierardi, Elvira Stellato, Guido Giovanni Plensich, Giuseppe Franceschelli, Cristiano Girlando, Giovanni Irmici, Dominic Labella, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Ronald M. Summers, Peter L. Choyke, Daguang Xu, Mona Flores, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Francesca Patella, Maurizio Cariati, Gianpaolo Carrafiello, Peng An, Bradford J. Wood, Baris Turkbey

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Machine vision-based automatic lamb identification and drinking activity in a commercial farm by A. Alon, I. Shimshoni, A. Godo, R. Berenstein, J. Lepar, N. Bergman, I. Halachmi

“…Thus, identifying lambs in a commercial pen using a relatively inexpensive and easily installed system consisting of a RGB camera and a computer vision-based algorithm has potential for farm management.…”
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Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review by Vera C. Kaelin, Vera C. Kaelin, Mina Valizadeh, Mina Valizadeh, Zurisadai Salgado, Julia G. Sim, Dana Anaby, Dana Anaby, Andrew D. Boyd, Andrew D. Boyd, Andrew D. Boyd, Natalie Parde, Natalie Parde, Mary A. Khetani, Mary A. Khetani, Mary A. Khetani, Mary A. Khetani

“…Included assessment approaches mainly captured participation through annotated observations (n = 20; 95%), were administered in person (n = 17; 81%), and applied machine learning (n = 20; 95%) and computer vision (n = 13; 62%). None integrated the child or youth perspective and only one included the caregiver perspective. …”
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DIGITAL RECONSTRUCTION OF THE CHURCH OF SAN ILDEFONSO AT ZAMORA (SPAIN) USING ORTHOWARE by A. Pérez, R. Cachero, S. Navarro, F. Jordá, D. López, J.L. Lerma, A. Martos

“…Orthoware makes use of automatic image analysis and computer vision techniques to improve this processing. …”
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Deep learning based markerless motion tracking as a clinical tool for movement disorders: Utility, feasibility and early experience by Rex N. Tien, Anand Tekriwal, Anand Tekriwal, Anand Tekriwal, Anand Tekriwal, Dylan J. Calame, Dylan J. Calame, Dylan J. Calame, Jonathan P. Platt, Sunderland Baker, Lauren C. Seeberger, Drew S. Kern, Abigail L. Person, Steven G. Ojemann, John A. Thompson, John A. Thompson, John A. Thompson, Daniel R. Kramer

“…However, recent advances in deep learning based computer vision algorithms have made accurate, robust markerless motion tracking viable in any setting where digital video can be captured. …”
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NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform by Konstantinos Mitsopoulos, Vasiliki Fiska, Konstantinos Tagaras, Athanasios Papias, Panagiotis Antoniou, Konstantinos Nizamis, Konstantinos Kasimis, Paschalina-Danai Sarra, Diamanto Mylopoulou, Theodore Savvidis, Apostolos Praftsiotis, Athanasios Arvanitidis, George Lyssas, Konstantinos Chasapis, Alexandros Moraitopoulos, Alexander Astaras, Panagiotis D. Bamidis, Alkinoos Athanasiou

“…BMI subsystems validation was performed using exercises through a Stereoscopic camera Computer Vision approach for the jacket and through multiple grip activities for the glove. …”
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An artificial intelligence-enabled smartphone app for real-time pressure injury assessment by Chun Hon Lau, Chun Hon Lau, Ken Hung-On Yu, Ken Hung-On Yu, Tsz Fung Yip, Tsz Fung Yip, Luke Yik Fung Luk, Luke Yik Fung Luk, Abraham Ka Chung Wai, Tin-Yan Sit, Janet Yuen-Ha Wong, Janet Yuen-Ha Wong, Joshua Wing Kei Ho, Joshua Wing Kei Ho

“…Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. …”
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Using feature maps to unpack the CNN ‘Black box’ theory with two medical datasets of different modality by Sami Azam, Sidratul Montaha, Kayes Uddin Fahim, A.K.M. Rakibul Haque Rafid, Md. Saddam Hossain Mukta, Mirjam Jonkman

“…Convolutional neural networks (CNNs) have been established for a comprehensive range of computer vision problems across several benchmarks. Visualization and analysis of feature maps generated by convolutional layers can be an effective approach to explore the hidden and complex characteristic of a CNN model. …”
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AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study by Jason Oke, Mark Harrison, David J Lowe, Alex Novak, Tilak Das, Ruchir Shah, Mariusz Tadeusz Grzeda, Sarim Ather, Kavitha Vimalesvaran, Howell Fu, Dennis Robert, Shamie Kumar, Swetha Tanamala, Kanika Bhatia, Andrea Romsauerova, Abdalá Espinosa, Mariapaola Narbone, Rahul Dharmadhikari, Jane Gooch, Nicholas Woznitza, Nabeeha Salik, Alan Campbell, Farhaan Khan, Haris Shuaib

“…Introduction A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. …”
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Estimating precision and accuracy of automated video post-processing: A step towards implementation of AI/ML for optics-based fish sampling by Jack H. Prior, Matthew D. Campbell, Matthew Dawkins, Paul F. Mickle, Robert J. Moorhead, Simegnew Y. Alaba, Chiranjibi Shah, Joseph R. Salisbury, Kevin R. Rademacher, A. Paul Felts, Farron Wallace

“…Automated image analysis through computer vision algorithms has emerged as a tool for fisheries to address big data needs, reduce human intervention, lower costs, and improve timeliness. …”
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A support vector machine and image processing based approach for counting open cotton bolls and estimating lint yield from UAV imagery by Arun Bawa, Sayantan Samanta, Sushil Kumar Himanshu, Jasdeep Singh, JungJin Kim, Tian Zhang, Anjin Chang, Jinha Jung, Paul DeLaune, James Bordovsky, Edward Barnes, Srinivasulu Ale

“…Cotton boll count is an important phenotypic trait that aids in a better understanding of the genetic and physiological mechanisms of cotton growth. Several computer vision technologies are available for cotton boll segmentation. …”
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DBGC: Dimension-Based Generic Convolution Block for Object Recognition by Chirag Patel, Dulari Bhatt, Urvashi Sharma, Radhika Patel, Sharnil Pandya, Kirit Modi, Nagaraj Cholli, Akash Patel, Urvi Bhatt, Muhammad Ahmed Khan, Shubhankar Majumdar, Mohd Zuhair, Khushi Patel, Syed Aziz Shah, Hemant Ghayvat

“…Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. …”
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The Constraints between Edge Depth and Uncertainty for Monocular Depth Estimation by Shouying Wu, Wei Li, Binbin Liang, Guoxin Huang

“…The self-supervised monocular depth estimation paradigm has become an important branch of computer vision depth-estimation tasks. However, the depth estimation problem arising from object edge depth pulling or occlusion is still unsolved. …”
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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care by Lena Maier-Hein, Matthias Eisenmann, Annika Reinke, Sinan Onogur, Marko Stankovic, Patrick Scholz, Tal Arbel, Hrvoje Bogunovic, Andrew P. Bradley, Aaron Carass, Carolin Feldmann, Alejandro F. Frangi, Peter M. Full, Bram van Ginneken, Allan Hanbury, Katrin Honauer, Michal Kozubek, Bennett A. Landman, Keno März, Oskar Maier, Klaus Maier-Hein, Bjoern H. Menze, Henning Müller, Peter F. Neher, Wiro Niessen, Nasir Rajpoot, Gregory C. Sharp, Korsuk Sirinukunwattana, Stefanie Speidel, Christian Stock, Danail Stoyanov, Abdel Aziz Taha, Fons van der Sommen, Ching-Wei Wang, Marc-André Weber, Guoyan Zheng, Pierre Jannin, Annette Kopp-Schneider

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Technology Modules Providing Solutions for Agile Manufacturing by Miha Deniša, Aleš Ude, Mihael Simonič, Tero Kaarlela, Tomi Pitkäaho, Sakari Pieskä, Janis Arents, Janis Judvaitis, Kaspars Ozols, Levente Raj, András Czmerk, Morteza Dianatfar, Jyrki Latokartano, Patrick Alexander Schmidt, Anton Mauersberger, Adrian Singer, Halldor Arnarson, Beibei Shu, Dimosthenis Dimosthenopoulos, Panagiotis Karagiannis, Teemu-Pekka Ahonen, Veikko Valjus, Minna Lanz

“…These solutions encompass a wide range of key technologies, including reconfigurable fixtures, low-cost automation for printed circuit board (PCB) assembly, computer-vision-based control, wireless sensor networks (WSNs) simulations, predictive maintenance based on Internet of Things (IoT), virtualization for operator training, intuitive robot programming using virtual reality (VR), autonomous trajectory generation, programming by demonstration for force-based tasks, on-line task allocation in human–robot collaboration (HRC), projector-based graphical user interface (GUI) for HRC, human safety in collaborative work cells, and integration of automated ground vehicles for intralogistics. …”
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Automated Image Annotation With Novel Features Based on Deep ResNet50-SLT by Myasar Mundher Adnan, Mohd Shafry Mohd Rahim, Amjad Rehman Khan, Ahmed Alkhayyat, Faten S. Alamri, Tanzila Saba, Saeed Ali Bahaj

“…One of the most significant challenges in computer vision and multimedia is image annotation, which involves labeling images with descriptive keywords. …”
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