Computer Vision and Machine Learning with RGB-D Sensors
The combination of high-resolution visual and depth sensing, supported by machine learning, opens up new opportunities to solve real-world problems in computer vision. This authoritative text/reference presents an interdisciplinary selection of important, cutting-edge research on RGB-D based compute...
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| Other Authors: | , , , |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2014.
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| Edition: | 1st ed. 2014. |
| Series: | Advances in Computer Vision and Pattern Recognition,
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-319-08651-4 |
Table of Contents:
- Part I: Surveys
- 3D Depth Cameras in Vision: Benefits and Limitations of the Hardware
- A State-of-the-Art Report on Multiple RGB-D Sensor Research and on Publicly Available RGB-D Datasets
- Part II: Reconstruction, Mapping and Synthesis
- Calibration Between Depth and Color Sensors for Commodity Depth Cameras
- Depth Map Denoising via CDT-Based Joint Bilateral Filter
- Human Performance Capture Using Multiple Handheld Kinects
- Human Centered 3D Home Applications via Low-Cost RGBD Cameras
- Matching of 3D Objects Based on 3D Curves
- Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinects
- Part III: Detection, Segmentation and Tracking
- RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons
- RGB-D Human Identification and Tracking in a Smart Environment
- Part IV: Learning-Based Recognition
- Feature Descriptors for Depth-Based Hand Gesture Recognition
- Hand Parsing and Gesture Recognition with a Commodity Depth Camera
- Learning Fast Hand Pose Recognition
- Real time Hand-Gesture Recognition Using RGB-D Sensor.