Results 21 to 30 of about 49,587 (264)
An evaluation of the RGB-D SLAM system [PDF]
2012 IEEE International Conference on Robotics and Automation, 2012 We present an approach to simultaneous localization and mapping (SLAM) for RGB-D cameras like the Microsoft Kinect. Our system concurrently estimates the trajectory of a hand-held Kinect and generates a dense 3D model of the environment. We present the key features of our approach and evaluate its performance thoroughly on a recently published dataset,
Felix Endres +5 more
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RGB-D scene analysis in the NICU
Computers in Biology and Medicine, 2021 Continuity of care is achieved in the neonatal intensive care unit (NICU) through careful documentation of all events of clinical significance, including clinical interventions and routine care events (e.g., feeding, diaper change, weighing, etc.). As a step towards automating this documentation process, we propose a scene recognition algorithm that ...
Yasmina Souley Dosso +3 more
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RGB-D salient object detection: A survey [PDF]
Computational Visual Media, 2021 Salient object detection (SOD), which simulates the human visual perception system to locate the most attractive object(s) in a scene, has been widely applied to various computer vision tasks. Now, with the advent of depth sensors, depth maps with affluent spatial information that can be beneficial in boosting the performance of SOD, can easily be ...
Tao Zhou 0002 +4 more
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Efficient RGB–D data processing for feature–based self–localization of mobile robots
International Journal of Applied Mathematics and Computer Science, 2016 The problem of position and orientation estimation for an active vision sensor that moves with respect to the full six degrees of freedom is considered. The proposed approach is based on point features extracted from RGB-D data.
Kraft Marek +4 more
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AN RGB-D DATA PROCESSING FRAMEWORK BASED ON ENVIRONMENT CONSTRAINTS FOR MAPPING INDOOR ENVIRONMENTS [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 The adoption of RGB and depth (RGB-D) sensors for surveying applications (i.e., building information modeling [BIM], indoor navigation, and three-dimensional [3D] models) to replace expensive and time-consuming methods (e.g., stereo cameras, laser ...
W. Darwish +5 more
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IEEE Access, 2022 The RGB-D-based human action recognition is gaining increasing attention because the different modalities can provide complementary information. However, the recognition performance is still not satisfactory due to the limited ability to learn spatial ...
Qin Cheng +4 more
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Fusion in dissimilarity space for RGB-D person re-identification
Array, 2021 Person re-identification (Re-id) is the task of recognizing people across different non-overlapping sensors of a camera network. Despite the recent advances with deep learning (DL) models for multi-modal fusion, state-of-the-art Re-id approaches fail to ...
Md Kamal Uddin +4 more
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Neural RGB-D Surface Reconstruction
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance for upcoming applications in AR or VR. These range from mixed reality applications for teleconferencing, virtual measuring, virtual room planing, to robotic applications.
Azinovic, D. +4 more
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PL-GM:RGB-D SLAM With a Novel 2D and 3D Geometric Constraint Model of Point and Line Features
IEEE Access, 2021 In the study of RGB-D SLAM (Simultaneous Localization and Mapping), two types of primary visual features, point and line features, have been widely utilized to calculate the camera pose.
Chenyang Zhang
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RGB-D Segmentation of Poultry Entrails [PDF]
, 2016 This paper presents an approach for automatic visual inspection of chicken entrails in RGB-D data. The point cloud is first over-segmented into supervoxels based on color, spatial and geometric information. Color, position and texture features are extracted from each of the resulting supervoxels and passed to a Random Forest classifier, which ...
Mark Philip Philipsen +3 more
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