Qiaokang Liang, Hai Qin, Shao Xiang

Intelligent Robotic Visual Perception with Deep Learning

• Broschiertes Buch

Produktbeschreibung

Intelligent Robotic Visual Perception with Deep Learning provides an in-depth exploration of deep learning-based robot Intelligent vision perception technologies that helps readers establish a solid foundation to learn about the applications and latest theoretical methods in visual perception. The book, in a comprehensive manner, covers the research aspects of deep learning technology in intelligent visual perception, ranging from methods to practical applications, algorithm analysis, and model construction. Users will find the latest international research trends that are essential for researchers working in the area.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 470
• Erscheinungstermin: 1. Juli 2025
• Englisch
• ISBN-13: 9780443335327
• ISBN-10: 044333532X
• Artikelnr.: 72489355

Autorenporträt

Dr. Qiakang Liang is a Professor at the College of Electrical and Information Engineering, Hunan University, China. He also serves as the Deputy Director of the National Engineering Research Center for Robot Vision Perception and Control. His research interests include robotics and mechatronics, biomimetic sensing, advanced robot technology, and human-computer interaction

Inhaltsangabe

1. An overview of the development and challenges of robot vision perception
systems
2. The components, main implementation steps, and typical applications of
robot vision perception systems
3. D deep learning technologies in robot vision perception systems
4. Text detection based on image segmentation and sequence-based scene text
recognition technologies in natural scenes
5. Visual object detection technologies, with a focus on R-FCN-based and
Mask RCNN-based object detection methods
6. Multi-object tracking technologies, emphasizing sequence feature-based
and context graph model-based multi-object tracking methods
7. Image segmentation methods, with a focus on remote sensing image
semantic segmentation using adaptive feature selection networks and region
segmentation based on SU-SWA