Robotic Vision: Technologies for Machine Learning and Vision Applications

Robotic systems consist of object or scene recognition, vision-based motion control, vision-based mapping, and dense range sensing, and are used for identification and navigation. As these computer vision and robotic connections continue to develop, the benefits of vision technology including savings, improved quality, reliability, safety, and productivity are revealed. Robotic Vision: Technologies for Machine Learning and Vision Applications is a comprehensive collection which highlights a solid framework for understanding existing work and planning future research. This book includes current research on the fields of robotics, machine vision, image processing and pattern recognition that is important to applying machine vision methods in the real world.

José García Rodríguez received his BSc, MSc and Phd in Computer Science from the University of Alicante (Spain) in 1994, 1996 and 2009 respectively. He is currently Associate Professor in the Department of Computer Technology at the University of Alicante. His research interest are focused on computer vision, neural networks, man-machine interaction, ambient intelligence, robotics and algorithms parallelization and acceleration.

Miguel Cazorla received a BS degree in Computer Science from the University of Alicante (Spain) in 1995 and a PhD in Computer Science from the same University in 2000. He is currently Associate Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante. He has done several postdocs stays: ACFR at University of Sydney with Eduardo Nebot, IPAB at University of Edinburgh with Robert Fisher, CMU with Sebastian Thrun and SKERI with Alan Yuille. He has published several papers on robotics and computer vision. His research interest are focused on computer vision and mobile robotics (mainly using vision to implement robotics tasks).