Intelligent Robotic Visual Perception with Deep Learning

by Qiaokang Liang PhD, Hai Qin PhD candidate, Shao Xiang

Overview

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. - Includes a detailed exploration of both algorithmic theory and practical applications - Provides a hands-on approach with case studies presented to help illustrate highly practical approaches - Shows readers how to construct intelligent robot vision perception systems tailored to real-world applications

Product Details

ISBN-13:	9780443335334
Publisher:	Elsevier Science
Publication date:	09/01/2025
Sold by:	Barnes & Noble
Format:	eBook
Pages:	470
File size:	36 MB
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About the Author

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 interactionHai Qin is a Ph.D. candidate at the College of Electrical and Information Engineering, Hunan University, China, and a research member at the National Engineering Research Center for Robot Vision Perception and Control. His research interests encompass intelligent robotic perception, computer vision, and machine learningShao Xiang is a researcher based at the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China; Shao Xiang is also a member of the National Engineering Research Center for Robot Vision Perception and Control.His research interests include change detection of remote sensing, image compression, object detection and semantic segmentation

Table of Contents

1. An overview of the development and challenges of robot vision perception systems2. The components, main implementation steps, and typical applications of robot vision perception systems3. D deep learning technologies in robot vision perception systems4. Text detection based on image segmentation and sequence-based scene text recognition technologies in natural scenes5. Visual object detection technologies, with a focus on R-FCN-based and Mask RCNN-based object detection methods6. Multi-object tracking technologies, emphasizing sequence feature-based and context graph model-based multi-object tracking methods7. Image segmentation methods, with a focus on remote sensing image semantic segmentation using adaptive feature selection networks and region segmentation based on SU-SWA