Spatiotemporal Fusion Framework for Multi-camera Face Orientation Analysis

2007 ◽  
pp. 156-167
Author(s):  
Chung-Ching Chang ◽  
Hamid Aghajan
Keyword(s):  
Orientation Analysis ◽  
Fusion Framework

Orientation analysis of Web reviews

2010 ◽  
Vol 30 (11) ◽  
pp. 2937-2940 ◽  
Author(s):  
Jian-li DING ◽  
Xiang CI ◽  
Jian-xiong HUANG
Keyword(s):  
Orientation Analysis

scholarly journals Infrared and visible image fusion via octave Gaussian pyramid framework

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Yan ◽  
Qun Hao ◽  
Jie Cao ◽  
Rizvi Saad ◽  
Kun Li ◽  
...  
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Visible Image ◽  
Composite Image ◽  
Multiscale Decomposition ◽  
Gaussian Pyramid ◽  
Fusion Methods ◽  
Scale Spaces ◽  
Fusion Framework

AbstractImage fusion integrates information from multiple images (of the same scene) to generate a (more informative) composite image suitable for human and computer vision perception. The method based on multiscale decomposition is one of the commonly fusion methods. In this study, a new fusion framework based on the octave Gaussian pyramid principle is proposed. In comparison with conventional multiscale decomposition, the proposed octave Gaussian pyramid framework retrieves more information by decomposing an image into two scale spaces (octave and interval spaces). Different from traditional multiscale decomposition with one set of detail and base layers, the proposed method decomposes an image into multiple sets of detail and base layers, and it efficiently retains high- and low-frequency information from the original image. The qualitative and quantitative comparison with five existing methods (on publicly available image databases) demonstrate that the proposed method has better visual effects and scores the highest in objective evaluation.

A Feature Fusion Framework and Its Application to Automatic Seizure Detection

2021 ◽  
pp. 1-1
Author(s):  
Chengbin Huang ◽  
Weiting Chen ◽  
Mingsong Chen ◽  
Binhang Yuan
Keyword(s):  
Feature Fusion ◽  
Seizure Detection ◽  
Fusion Framework

scholarly journals Trajectory representation and landmark projection for continuous-time structure from motion

2019 ◽  
Vol 38 (6) ◽  
pp. 686-701 ◽  
Author(s):  
Hannes Ovrén ◽  
Per-Erik Forssén
Keyword(s):  
Structure From Motion ◽  
Continuous Time ◽  
Spline Interpolation ◽  
Time Structure ◽  
Robot Arm ◽  
Good Sense ◽  
Trajectory Representation ◽  
Inertial Measurements ◽  
Derivatives Of ◽  
Fusion Framework

This paper revisits the problem of continuous-time structure from motion, and introduces a number of extensions that improve convergence and efficiency. The formulation with a [Formula: see text]-continuous spline for the trajectory naturally incorporates inertial measurements, as derivatives of the sought trajectory. We analyze the behavior of split spline interpolation on [Formula: see text] and on [Formula: see text], and a joint spline on [Formula: see text], and show that the latter implicitly couples the direction of translation and rotation. Such an assumption can make good sense for a camera mounted on a robot arm, but not for hand-held or body-mounted cameras. Our experiments in the Spline Fusion framework show that a split spline on [Formula: see text] is preferable over an [Formula: see text] spline in all tested cases. Finally, we investigate the problem of landmark reprojection on rolling shutter cameras, and show that the tested reprojection methods give similar quality, whereas their computational load varies by a factor of two.

Multimodal data fusion framework enhanced robot-assisted minimally invasive surgery

2021 ◽  
pp. 014233122098435
Author(s):  
Wen Qi ◽  
Hang Su ◽  
Ke Fan ◽  
Ziyang Chen ◽  
Jiehao Li ◽  
...  
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Data Fusion ◽  
Invasive Surgery ◽  
Robot Control ◽  
Surgical Robot ◽  
Robot Assisted ◽  
Multimodal Data ◽  
Multimodal Data Fusion ◽  
Fusion Framework

The generous application of robot-assisted minimally invasive surgery (RAMIS) promotes human-machine interaction (HMI). Identifying various behaviors of doctors can enhance the RAMIS procedure for the redundant robot. It bridges intelligent robot control and activity recognition strategies in the operating room, including hand gestures and human activities. In this paper, to enhance identification in a dynamic situation, we propose a multimodal data fusion framework to provide multiple information for accuracy enhancement. Firstly, a multi-sensors based hardware structure is designed to capture varied data from various devices, including depth camera and smartphone. Furthermore, in different surgical tasks, the robot control mechanism can shift automatically. The experimental results evaluate the efficiency of developing the multimodal framework for RAMIS by comparing it with a single sensor system. Implementing the KUKA LWR4+ in a surgical robot environment indicates that the surgical robot systems can work with medical staff in the future.

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