Akademibokhandeln
Jiandong Tian - All Weather Robot Vision, Inbunden

Inbunden, Engelska, 2021

Del i serien Research on Intelligent Manufacturing

All Weather Robot Vision

Av Jiandong Tian

2109 kr

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Beskrivning
Complex illumination and meteorological conditions can significantly limit the robustness of robotic vision systems. This book focuses on image pre-processing for robot vision in complex illumination and dynamic weather conditions. It systematically covers cutting-edge models and algorithms, approaching them from a novel viewpoint based on studying the atmospheric physics and imaging mechanism. It provides valuable insights and practical methods such as illumination calculations, scattering modeling, shadow/highlight detection and removal, intrinsic image derivation, and rain/snow/fog removal technologies that will enable robots to be effective in diverse lighting and weather conditions, i.e., ensure their all-weather operating capacity. As such, the book offers a valuable resource for researchers, graduate students and engineers in the fields of robot engineering and computer science.
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Dr. Jiandong Tian received his B.S. degree in the department of automation, Heilongjiang University, P. R. China, in 2005. In 2011 he received his doctoral degree in Pattern Recognition and Intelligent Systems from Shenyang Institute of Automation, Chinese Academy of Sciences. Currently he is a professor in State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences. During 2015.6-2016.6, he was a Visiting Professor at Center for Visual Computing, University of California, San Diego. His research interests include robot vision, illumination modeling, and image/video processing. He has published more than 50 papers in this field. Fairly amount of them have been published in well-known journals and top conferences in computer/robot vision. Dr. Tian gained the first prize of natural science award of Chinese Association of Automation in 2019 and the first prize of natural science award of Liaoning Province in 2020, respectively.

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