![]() ![]() The accuracy is about the same comparing 2 Kinects and 4 PS Eyes. Also, the calibration process is very simple for dual Kinects, it takes about 10 minutes. For home usage, the dual Kinect configuration is better suited, as it does not require much space. There is far too much setting up and waiting around for results and starting again.Īll space requirements are listed on our website. And then you cannot see if your capture was successful until it has been 'solved' off line. Then you have to 'calibrate' them which is a real trial and error pain in the ass. What they dont tell you is that you need loads of room for the spacing of the cameras, ( so it is not a desktop / home solution ) and that you need to distrubite all the camers out on seperate usb's. They also recommend using all of them ( 6 I think ). The makers themselves recomend the PS3 camera's for accuracy rather than Kinects. The book also serves as a useful reference for graduate students studying computer vision, pattern recognition or multimedia.Originally posted by grrinc:Yes. Researchers and practitioners working in computer vision, HCI and machine learning will find this to be a must-read text. Proposes a new classifier architecture for real-time hand pose recognition, and a novel hand segmentation and gesture recognition system.Examines the effective features that characterize static hand poses, and introduces a unified framework to enforce both temporal and spatial constraints for hand parsing.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.Presents an innovative approach for 3D object retrieval, and for the reconstruction of gas flow from multiple Kinect cameras.Reviews a selection of applications which use RGB-D information to reconstruct human figures, evaluate energy consumption, and obtain accurate action classification.Discusses the calibration of color and depth cameras, the reduction of noise on depth maps, and methods for capturing human performance in 3D.The work continues with a section devoted to novel techniques that employ depth data for object detection, segmentation and tracking, and concludes with examples of accurate human action interpretation aided by depth sensors. Divided into four sections, the book opens with a detailed survey of the field, followed by a focused examination of RGB-D based 3D reconstruction, mapping and synthesis. This authoritative text/reference presents an interdisciplinary selection of important, cutting-edge research on RGB-D based computer vision. 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. ![]()
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