標(biāo)題: Titlebook: Computer Vision and Machine Learning with RGB-D Sensors; Ling Shao,Jungong Han,Zhengyou Zhang Book 2014 Springer International Publishing [打印本頁] 作者: papertrans 時間: 2025-3-21 20:09
書目名稱Computer Vision and Machine Learning with RGB-D Sensors影響因子(影響力)
書目名稱Computer Vision and Machine Learning with RGB-D Sensors影響因子(影響力)學(xué)科排名
書目名稱Computer Vision and Machine Learning with RGB-D Sensors網(wǎng)絡(luò)公開度
書目名稱Computer Vision and Machine Learning with RGB-D Sensors網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computer Vision and Machine Learning with RGB-D Sensors被引頻次
書目名稱Computer Vision and Machine Learning with RGB-D Sensors被引頻次學(xué)科排名
書目名稱Computer Vision and Machine Learning with RGB-D Sensors年度引用
書目名稱Computer Vision and Machine Learning with RGB-D Sensors年度引用學(xué)科排名
書目名稱Computer Vision and Machine Learning with RGB-D Sensors讀者反饋
書目名稱Computer Vision and Machine Learning with RGB-D Sensors讀者反饋學(xué)科排名
作者: Arteriography 時間: 2025-3-21 23:13
Calibration Between Depth and Color Sensors for Commodity Depth Camerasation information between the color and the depth cameras. Traditional checkerboard-based calibration schemes fail to work well for the depth camera, since its corner features cannot be reliably detected in the depth image. In this chapter, we present a maximum likelihood solution for the joint dept作者: 字謎游戲 時間: 2025-3-22 01:17 作者: semiskilled 時間: 2025-3-22 08:19
Human Performance Capture Using Multiple Handheld Kinects. The reconstructed 3D performance can be used for character animation and free-viewpoint video. While most of the available performance capture approaches rely on a 3D video studio with tens of RGB cameras, this chapter presents a method for marker-less performance capture of single or multiple hum作者: 切碎 時間: 2025-3-22 10:30 作者: 一致性 時間: 2025-3-22 13:02
Matching of 3D Objects Based on 3D Curvesh device (RGB-D). Our processing pipeline consists of several steps. In the preprocessing step, we first detect edges in the depth image and merge them to 2D object curves which allows a back-projection to 3D space. Then, we estimate a local coordinate system for these 3D curves. In the next step, d作者: 一致性 時間: 2025-3-22 18:19 作者: Forehead-Lift 時間: 2025-3-22 23:47 作者: 采納 時間: 2025-3-23 02:43 作者: Euthyroid 時間: 2025-3-23 09:14 作者: Infuriate 時間: 2025-3-23 10:05
Hand Parsing and Gesture Recognition with a Commodity Depth Cameraers from the lack of discriminative features to differentiate and track hand parts. In this chapter, we present a robust hand parsing scheme to obtain a high-level and discriminative representation of the hand from raw depth image. A novel distance-adaptive feature selection method is proposed to ge作者: insightful 時間: 2025-3-23 16:34
Learning Fast Hand Pose Recognitionchitecture, the . (.), for addressing this challenge. The classifier architecture optimizes both classification speed and accuracy when a large training set is available. Speed is obtained using simple binary features and direct indexing into a set of tables, and accuracy by using a large capacity m作者: Coronary 時間: 2025-3-23 18:59
Real-Time Hand Gesture Recognition Using RGB-D Sensor hand motion capture procedure for establishing the real gesture data set. A hand partition scheme is designed for color-based semi-automatic labeling. This method is integrated into a vision-based hand gesture recognition framework for developing desktop applications. We use the Kinect sensor to ac作者: encomiast 時間: 2025-3-23 22:12
The Definitive Guide to Windows Installerlished to help improve calibration accuracy. Uncertainty in depth values has been taken into account systematically. The proposed algorithm is reliable and accurate, as demonstrated by extensive experimental results on simulated and real-world examples.作者: Anthrp 時間: 2025-3-24 03:29 作者: 蕨類 時間: 2025-3-24 10:33
https://doi.org/10.1007/978-1-4302-0176-2The matching process is transformed to the problem of Maximum Weight Subgraph search. Excellent retrieval results achieved in a comprehensive setup of challenging experiments show the benefits of our method comparing to the state-of-the-art.作者: 粉筆 時間: 2025-3-24 12:22
Technical Considerations When Using db4o with simple contour model and thus supports complex real-time interactions. The experimental evaluations and a real-world demo of hand gesture interaction demonstrate the effectiveness of this framework.作者: Osteoarthritis 時間: 2025-3-24 15:55
Calibration Between Depth and Color Sensors for Commodity Depth Cameraslished to help improve calibration accuracy. Uncertainty in depth values has been taken into account systematically. The proposed algorithm is reliable and accurate, as demonstrated by extensive experimental results on simulated and real-world examples.作者: UTTER 時間: 2025-3-24 21:02 作者: 飾帶 時間: 2025-3-24 23:56
Matching of 3D Objects Based on 3D CurvesThe matching process is transformed to the problem of Maximum Weight Subgraph search. Excellent retrieval results achieved in a comprehensive setup of challenging experiments show the benefits of our method comparing to the state-of-the-art.作者: 莊嚴(yán) 時間: 2025-3-25 06:52 作者: cochlea 時間: 2025-3-25 09:13
Book 2014e 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.作者: 責(zé)問 時間: 2025-3-25 15:30
Book 2014r 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 3作者: Essential 時間: 2025-3-25 19:45 作者: Cumulus 時間: 2025-3-25 20:07
https://doi.org/10.1007/978-1-4302-0176-2narios by a model with tracked skeleton, which may help users to know their exercise effects and even diet or reduce their weights. The final application presents a real-time system that automatically classifies the human action acquired by consumer-priced RGBD sensor.作者: geriatrician 時間: 2025-3-26 00:37 作者: Vertebra 時間: 2025-3-26 06:31
2191-6586 ng the RGBD information.Covers a range of different techniquThis 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 作者: 聯(lián)合 時間: 2025-3-26 10:24 作者: tolerance 時間: 2025-3-26 15:29
Using Sparse Optical Flow for Two-Phase Gas Flow Capturing with Multiple Kinect in the Kinect to retrieve subtle scene data alterations for reconstruction. The method is employed in a multiple Kinect vision architecture to detect the interface of propane flow around occluding objects in air.作者: 書法 時間: 2025-3-26 17:12 作者: 有組織 時間: 2025-3-27 00:24 作者: Medley 時間: 2025-3-27 04:28 作者: ANTH 時間: 2025-3-27 07:03
The Definitive Guide to TerracottaThe second-generation Microsoft Kinect uses time-of-flight technology, while the first-generation Kinect uses structured light technology. This raises the question whether one of these technologies is “better” than the other. In this chapter, readers will find an overview of 3D camera technology and the artifacts that occur in depth maps.作者: optic-nerve 時間: 2025-3-27 09:43
3D Depth Cameras in Vision: Benefits and Limitations of the HardwareThe second-generation Microsoft Kinect uses time-of-flight technology, while the first-generation Kinect uses structured light technology. This raises the question whether one of these technologies is “better” than the other. In this chapter, readers will find an overview of 3D camera technology and the artifacts that occur in depth maps.作者: 飛鏢 時間: 2025-3-27 15:12
Computer Vision and Machine Learning with RGB-D Sensors978-3-319-08651-4Series ISSN 2191-6586 Series E-ISSN 2191-6594 作者: Gingivitis 時間: 2025-3-27 21:26 作者: Onerous 時間: 2025-3-28 00:34 作者: perimenopause 時間: 2025-3-28 03:27 作者: Vo2-Max 時間: 2025-3-28 06:46 作者: constellation 時間: 2025-3-28 12:14 作者: 顯示 時間: 2025-3-28 18:11 作者: Rct393 時間: 2025-3-28 20:01 作者: eustachian-tube 時間: 2025-3-28 23:51 作者: rectum 時間: 2025-3-29 05:49 作者: Apoptosis 時間: 2025-3-29 10:39 作者: Handedness 時間: 2025-3-29 12:16 作者: figment 時間: 2025-3-29 17:52 作者: 新陳代謝 時間: 2025-3-29 23:12 作者: 因無茶而冷淡 時間: 2025-3-30 02:47
Ling Shao,Jungong Han,Zhengyou ZhangDescribes recent advances in RGB-D based computer vision algorithms, with an emphasis on advanced machine learning techniques for interpreting the RGBD information.Covers a range of different techniqu作者: 橫條 時間: 2025-3-30 05:24
Advances in Computer Vision and Pattern Recognitionhttp://image.papertrans.cn/c/image/234070.jpg作者: 權(quán)宜之計 時間: 2025-3-30 12:14 作者: 樸素 時間: 2025-3-30 15:15
