Intelligent Robotic Visual Perception with Deep Learning
Autor Qiaokang Liang, Hai Qin, Shao Xiangen Limba Engleză Paperback – iul 2025
- 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
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Specificații
ISBN-13: 9780443335327
ISBN-10: 044333532X
Pagini: 470
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
ISBN-10: 044333532X
Pagini: 470
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Cuprins
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