Computer Vision and Machine Learning with RGB-D Sensors (Advances in Computer Vision and Pattern Recognition)

Shao, Ling (EDT)/ Han, Jungong (EDT)/ Kohli, Pushmeet (EDT)

Springer International Publishing AG（2016/09発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 316 p.
• 言語 ENG
• 商品コード 9783319381053
• DDC分類 005.437

Full Description

This book presents an interdisciplinary selection of cutting-edge research on RGB-D based computer vision. Features: discusses the calibration of color and depth cameras, the reduction of noise on depth maps and methods for capturing human performance in 3D; reviews a selection of applications which use RGB-D information to reconstruct human figures, evaluate energy consumption and obtain accurate action classification; presents an approach for 3D object retrieval and for the reconstruction of gas flow from multiple Kinect cameras; describes an RGB-D computer vision system designed to assist the visually impaired and another for smart-environment sensing to assist elderly and disabled people; examines the effective features that characterize static hand poses and introduces a unified framework to enforce both temporal and spatial constraints for hand parsing; proposes a new classifier architecture for real-time hand pose recognition and a novel hand segmentation and gesture recognition system.

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.