Adaptive Trees and Pose Identification from External Contours of Polyhedra

2005 ◽  
pp. 157-168 ◽  
Author(s):  
Yannick L. Kergosien
Keyword(s):  
Pose Identification

Suryanamaskar Pose Identification and Estimation Using No Code Computer Vision

2021 ◽  
pp. 85-90
Author(s):  
Ujjayanta Bhaumik ◽  
Siddharth Chatterjee ◽  
Koushlendra Kumar Singh
Keyword(s):  
Computer Vision ◽  
Pose Identification

scholarly journals Real-Time Embedded EMG Signal Analysis for Wrist-Hand Pose Identification

2020 ◽  
Vol 68 ◽  
pp. 2713-2723 ◽  
Author(s):  
Sumit A. Raurale ◽  
John McAllister ◽  
Jesus Martinez del Rincon
Keyword(s):  
Real Time ◽  
Signal Analysis ◽  
Emg Signal ◽  
Pose Identification ◽  
Hand Pose

Quaternion epipolar decomposition for camera pose identification and animation

2013 ◽  
Vol 21 (1) ◽  
Author(s):  
W. Skarbek ◽  
M. Tomaszewski
Keyword(s):  
Computer Vision ◽  
Measurement Error ◽  
Computer Graphics ◽  
Quadratic Form ◽  
Essential Matrix ◽  
Virtual Camera ◽  
Camera Systems ◽  
Camera Pose ◽  
Pose Identification ◽  
And Robotics

AbstractIn the literature of computer vision, computer graphics and robotics, the use of quaternions is exclusively related to 3D rotation representation and interpolation. In this research we found how epipoles in multi-camera systems can be used to represent camera poses in the quaternion domain. The rotational quaternion is decomposed in two epipole rotational quaternions and one z axis rotational quaternion. Quadratic form of the essential matrix is also related to quaternion factors. Thus, five pose parameters are distributed into three independent rotational quaternions resulting in measurement error separation at camera pose identification and greater flexibility at virtual camera animation. The experimental results refer to the design of free viewpoint television.

REAL-TIME FACIAL POSE IDENTIFICATION WITH HIERARCHICALLY STRUCTURED ML POSE CLASSIFIER

2004 ◽  
Vol 18 (02) ◽  
pp. 127-142 ◽  
Author(s):  
JAE-WON SUNG ◽  
DAIJIN KIM
Keyword(s):  
Real Time ◽  
High Speed ◽  
Tracking System ◽  
Computation Time ◽  
Feature Space ◽  
Identification Problem ◽  
Pose Tracking ◽  
Time Simulation ◽  
The Face ◽  
Pose Identification

Since pose-varying face images form nonlinear convex manifold in high dimensional image space, it is difficult to model their pose distribution in terms of a simple probabilistic density function. To solve this difficulty, we divide the pose space into many constituent pose classes and treat the continuous pose estimation problem as a discrete pose-class identification problem. We propose to use a hierarchically structured ML (Maximum Likelihood) pose classifiers in the reduced feature space to decrease the computation time for pose identification, where pose space is divided into several pose groups and each group consists of a number of similar neighboring poses. We use the CONDENSATION algorithm to find a newly appearing face and track the face with a variety of poses in real-time. Simulation results show that our proposed pose identification using the hierarchically structured ML pose classifiers can perform a faster pose identification than conventional pose identification using the flat structured ML pose classifiers. A real-time facial pose tracking system is built with high speed hierarchically structured ML pose classifiers.

scholarly journals Dance Pose Identification from Motion Capture Data: A Comparison of Classifiers

Technologies ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 31 ◽  
Author(s):  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Anastasios Doulamis ◽  
Stephanos Camarinopoulos ◽  
Nikolaos Doulamis ◽  
...  
Keyword(s):  
Motion Capture ◽  
Motion Capture Data ◽  
Pose Identification
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