Qiaokang Liang,
Hai Qin,
Shao Xiang
Intelligent Robotic Visual Perception With Deep Learning
Paperback Engels 2025 9780443335327Levertijd ongeveer 9 werkdagen
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Samenvatting
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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Inhoudsopgave
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
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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