標(biāo)題: Titlebook: Consumer Depth Cameras for Computer Vision; Research Topics and Andrea Fossati,Juergen Gall,Kurt Konolige Book 2013 Springer-Verlag London [打印本頁] 作者: DUCT 時(shí)間: 2025-3-21 19:35
書目名稱Consumer Depth Cameras for Computer Vision影響因子(影響力)
書目名稱Consumer Depth Cameras for Computer Vision影響因子(影響力)學(xué)科排名
書目名稱Consumer Depth Cameras for Computer Vision網(wǎng)絡(luò)公開度
書目名稱Consumer Depth Cameras for Computer Vision網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Consumer Depth Cameras for Computer Vision被引頻次
書目名稱Consumer Depth Cameras for Computer Vision被引頻次學(xué)科排名
書目名稱Consumer Depth Cameras for Computer Vision年度引用
書目名稱Consumer Depth Cameras for Computer Vision年度引用學(xué)科排名
書目名稱Consumer Depth Cameras for Computer Vision讀者反饋
書目名稱Consumer Depth Cameras for Computer Vision讀者反饋學(xué)科排名
作者: 剛開始 時(shí)間: 2025-3-21 23:09 作者: 法律 時(shí)間: 2025-3-22 03:47 作者: 厭煩 時(shí)間: 2025-3-22 07:34 作者: 花爭(zhēng)吵 時(shí)間: 2025-3-22 11:42
2191-6586 publicly-available code for many of the applications descriThe potential of consumer depth cameras extends well beyond entertainment and gaming, to real-world commercial applications. This authoritative text reviews the scope and impact of this rapidly growing field, describing the most promising K作者: 半圓鑿 時(shí)間: 2025-3-22 15:42 作者: 半圓鑿 時(shí)間: 2025-3-22 18:51
Jill Fain Lehman,Jaime G. Carbonellt Microsoft Research Cambridge, and discuss some of the remaining open challenges. Due to the summary nature of this chapter, we limit our description to the key insights and refer the reader to the original publications for the technical details.作者: 無法破譯 時(shí)間: 2025-3-23 00:04
M. Pistolesi,M. Miniati,M. R. Bonsignoren history images (MHIs). These depth-extended feature representation methods are evaluated comprehensively, and superior recognition performance related to their uni-modal (color only) counterparts is demonstrated.作者: 冒號(hào) 時(shí)間: 2025-3-23 02:08
Key Developments in Human Pose Estimation for Kinectt Microsoft Research Cambridge, and discuss some of the remaining open challenges. Due to the summary nature of this chapter, we limit our description to the key insights and refer the reader to the original publications for the technical details.作者: 圖表證明 時(shí)間: 2025-3-23 09:13
RGBD-HuDaAct: A Color-Depth Video Database for Human Daily Activity Recognitionn history images (MHIs). These depth-extended feature representation methods are evaluated comprehensively, and superior recognition performance related to their uni-modal (color only) counterparts is demonstrated.作者: CRACK 時(shí)間: 2025-3-23 13:13
Book 2013apture, 3D human body scans, and hand pose recognition for sign language parsing; provides a review of approaches to various recognition problems, including category and instance learning of objects, and human activity recognition; with a Foreword by Dr. Jamie Shotton.作者: LIEN 時(shí)間: 2025-3-23 14:30
Jill Fain Lehman,Jaime G. Carbonellarison of Kinect accuracy with stereo reconstruction from SLR cameras and a 3D-TOF camera. We propose a Kinect geometrical model and its calibration procedure providing an accurate calibration of Kinect 3D measurement and Kinect cameras. We compare our Kinect calibration procedure with its alternati作者: 痛打 時(shí)間: 2025-3-23 19:58
Jill Fain Lehman,Jaime G. Carbonellclosest point (ICP) algorithm on the graphics processing unit (GPU). Compared to state-of-the-art approaches that achieve real-time performance using projective data association schemes which operate on the 3-D scene geometry solely, our method allows to incorporate additional complementary informat作者: 詞匯表 時(shí)間: 2025-3-24 01:36
KNOME: Modeling What the User Knows in UCs Localization And Mapping or object recognition. With the introduction of the Kinect in 2010, Microsoft released a low cost depth camera that is now intensively used by researchers, especially in the field of indoor robotics. This chapter introduces a new 3D registration algorithm that can deal wit作者: artless 時(shí)間: 2025-3-24 05:05
Jill Fain Lehman,Jaime G. Carbonell that have been the dominant modes of interaction over the last few decades. An important milestone in the progress of natural user interfaces was the recent launch of Kinect with its unique ability to reliably estimate the pose of the human user in real time. Human pose estimation has been the subj作者: MODE 時(shí)間: 2025-3-24 08:08 作者: 慷慨援助 時(shí)間: 2025-3-24 12:21
Oxygen Transport Following Major Trauma availability of 3D body models. Although there has been a great deal of interest recently in the use of active depth sensing cameras, such as the Microsoft Kinect, for human pose tracking, little has been said about the related problem of human shape estimation. We present a method for human shape 作者: dictator 時(shí)間: 2025-3-24 16:53
Jean Louis Vincent (Assistant-Director)e for sign language recognition, since it would make classification of hand shapes and gestures possible. The recent introduction of the Kinect depth sensor has accelerated research in human body pose capture. This chapter describes a real-time hand pose estimation method employing an object recogni作者: pessimism 時(shí)間: 2025-3-24 19:41
Studies in Contemporary Economicstection dataset to the forefront. Such a dataset can be used for object recognition in a spirit usually reserved for the large collections of intensity images typically collected from the Internet. Here, we will review current 3D datasets and find them lacking in variation of scene, category, instan作者: 樂章 時(shí)間: 2025-3-25 01:25
https://doi.org/10.1007/978-3-642-83455-4d instance detection. Today we are witnessing the birth of a new generation of sensing technologies capable of providing high quality synchronized videos of both color and depth, the RGB-D (Kinect-style) camera. With its advanced sensing capabilities and the potential for mass adoption, this technol作者: 動(dòng)脈 時(shí)間: 2025-3-25 04:28 作者: conduct 時(shí)間: 2025-3-25 08:03
Consumer Depth Cameras for Computer Vision978-1-4471-4640-7Series ISSN 2191-6586 Series E-ISSN 2191-6594 作者: 刺激 時(shí)間: 2025-3-25 14:06
https://doi.org/10.1007/978-1-4471-4640-73D Point Cloud; Computer Vision; Consumer Depth Cameras; Kinect; Pattern Recognition作者: LATER 時(shí)間: 2025-3-25 19:35
978-1-4471-6977-2Springer-Verlag London 2013作者: preservative 時(shí)間: 2025-3-25 23:56 作者: escalate 時(shí)間: 2025-3-26 00:47
Advances in Computer Vision and Pattern Recognitionhttp://image.papertrans.cn/c/image/236188.jpg作者: 巨大沒有 時(shí)間: 2025-3-26 05:16
2191-6586 a review of approaches to various recognition problems, including category and instance learning of objects, and human activity recognition; with a Foreword by Dr. Jamie Shotton.978-1-4471-6977-2978-1-4471-4640-7Series ISSN 2191-6586 Series E-ISSN 2191-6594 作者: 歡樂中國(guó) 時(shí)間: 2025-3-26 08:55 作者: Deject 時(shí)間: 2025-3-26 13:39
A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camerach are fused using a fast selection scheme. Our algorithm reconstructs complex full-body poses in real time and effectively prevents temporal drifting, thus making it suitable for various real-time interaction scenarios.作者: Allowance 時(shí)間: 2025-3-26 19:51 作者: 上漲 時(shí)間: 2025-3-26 22:41 作者: ROOF 時(shí)間: 2025-3-27 03:04 作者: Graduated 時(shí)間: 2025-3-27 05:49
RGB-D Object Recognition: Features, Algorithms, and a Large Scale Benchmarknts. The dataset has been made publicly available to the research community so as to enable rapid progress based on this promising technology. We describe the dataset collection procedure and present techniques for RGB-D object recognition and detection of objects in scenes recorded using RGB-D vide作者: buoyant 時(shí)間: 2025-3-27 10:57 作者: 貝雷帽 時(shí)間: 2025-3-27 17:29 作者: audiologist 時(shí)間: 2025-3-27 18:37 作者: Bother 時(shí)間: 2025-3-28 01:45 作者: 溺愛 時(shí)間: 2025-3-28 02:21
Studies in Contemporary Economicsseful features from range images is addressed. There has been much success in using the histogram of oriented gradients (HOG) as a global descriptor for object detection in intensity images. There are also many proposed descriptors designed specifically for depth data (spin images, shape context, et作者: addict 時(shí)間: 2025-3-28 08:28
https://doi.org/10.1007/978-3-642-83455-4nts. The dataset has been made publicly available to the research community so as to enable rapid progress based on this promising technology. We describe the dataset collection procedure and present techniques for RGB-D object recognition and detection of objects in scenes recorded using RGB-D vide作者: Engulf 時(shí)間: 2025-3-28 10:24
3D with Kinectarison of Kinect accuracy with stereo reconstruction from SLR cameras and a 3D-TOF camera. We propose a Kinect geometrical model and its calibration procedure providing an accurate calibration of Kinect 3D measurement and Kinect cameras. We compare our Kinect calibration procedure with its alternati作者: COWER 時(shí)間: 2025-3-28 16:46 作者: infinite 時(shí)間: 2025-3-28 19:58 作者: visceral-fat 時(shí)間: 2025-3-28 23:31 作者: 直覺沒有 時(shí)間: 2025-3-29 06:05
A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Cameracomes more feasible when using streams of 2.5D monocular depth images as provided by a depth camera. However, due to low resolution of and challenging noise characteristics in depth camera images as well as self-occlusions in the movements, the pose estimation task is still far from being simple. Fu作者: DIS 時(shí)間: 2025-3-29 08:17
Home 3D Body Scans from a Single Kinect availability of 3D body models. Although there has been a great deal of interest recently in the use of active depth sensing cameras, such as the Microsoft Kinect, for human pose tracking, little has been said about the related problem of human shape estimation. We present a method for human shape 作者: notion 時(shí)間: 2025-3-29 13:07 作者: 排出 時(shí)間: 2025-3-29 16:40
A Category-Level 3D Object Dataset: Putting the Kinect to Worktection dataset to the forefront. Such a dataset can be used for object recognition in a spirit usually reserved for the large collections of intensity images typically collected from the Internet. Here, we will review current 3D datasets and find them lacking in variation of scene, category, instan作者: Employee 時(shí)間: 2025-3-29 22:49 作者: GEST 時(shí)間: 2025-3-30 00:41
