Guest Editors
Dr. Guanqiu Qi
Email: qig@buffalostate.edu
Affiliation: Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY, USA
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Research Interests: deep learning, machine learning, image processing, software engineering, cloud computing
Dr. Zhiqin Zhu
Email: zhuzq@cqupt.edu.cn
Affiliation: College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
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Research Interests: image processing, medical imaging, machine learning
Dr. Zhihao Zhou
Email: zhouzh@cqupt.edu.cn
Affiliation: School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
Homepage:
Research Interests: flexible devices, wearable electronics, bioelectronics
Prof. Yinong Chen
Email: yinong.chen@asu.edu
Affiliation: School of Computing and Augmented Intelligence, Arizona State University, Tempe, United States
Homepage:
Research Interests: machine learning, robotics, AI, software engineering, service-oriented computing, big data processing, computer science education, visual programming, internet of things
Summary
In digital image processing, the detection of specific objects and the resulting tasks of object classification, recognition, and segmentation are extremely important. These related technologies have now been widely applied in various fields such as autonomous driving, aerospace, medical diagnosis, remote sensing image analysis, and more. They have achieved numerous breakthrough applications, transforming the path of human societal progress. In recent years, with the rapid evolution of deep learning technology, object detection-related technologies have further developed at a fast pace. They have been extensively applied to the identification and detection of various signs and objects in autonomous driving, autonomous flight and delivery by drones, tumor segmentation and lesion diagnosis in medical imaging, and the interpretation and key object recognition in remote sensing imagery, all of which have advanced the mode of social operation.
Keywords
image object detection, object detection for autopilot, image segmentation for autopilot, object detection for remote sensing, image segmentation for remote sensing, medical image lesion detection, medical image segmentation, image object classification, object classification for autopilot, human machine interface, flexible sensing technology, flexible display, data security and privacy considerations in digital health solutions
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