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.

Full Product Details

Author:   Qiaokang Liang, PhD (Professor at the College of Electrical and Information Engineering, Hunan University, China) ,  Hai Qin, PhD candidate (College of Electrical and Information Engineering, Hunan University, China) ,  Shao Xiang (Researcher, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China)
Publisher:   Elsevier - Health Sciences Division
Imprint:   Elsevier - Health Sciences Division
Weight:   0.450kg
ISBN:  

9780443335327

ISBN 10:   044333532
Pages:   294
Publication Date:   15 September 2025
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

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

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Author Information

Dr. Qiaokang 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 Hai 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 learning Shao 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

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