A hybrid approach to keyframe extraction from motion capture data using curve simplification and principal component analysis

2014 ◽  
Vol 9 (6) ◽  
pp. 697-699 ◽  
Author(s):  
Takeshi Miura ◽  
Takaaki Kaiga ◽  
Takeshi Shibata ◽  
Hiroaki Katsura ◽  
Katsubumi Tajima ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Motion Capture ◽  
Hybrid Approach ◽  
Principal Component ◽  
Component Analysis ◽  
Motion Capture Data ◽  
Keyframe Extraction ◽  
Curve Simplification

scholarly journals Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259464
Author(s):  
Félix Bigand ◽  
Elise Prigent ◽  
Bastien Berret ◽  
Annelies Braffort
Keyword(s):  
Principal Component Analysis ◽  
Sign Language ◽  
Motion Capture ◽  
Computational Models ◽  
Body Movement ◽  
Principal Component ◽  
Component Analysis ◽  
Body Motion ◽  
Upper Body ◽  
Motion Capture Data

Sign Language (SL) is a continuous and complex stream of multiple body movement features. That raises the challenging issue of providing efficient computational models for the description and analysis of these movements. In the present paper, we used Principal Component Analysis (PCA) to decompose SL motion into elementary movements called principal movements (PMs). PCA was applied to the upper-body motion capture data of six different signers freely producing discourses in French Sign Language. Common PMs were extracted from the whole dataset containing all signers, while individual PMs were extracted separately from the data of individual signers. This study provides three main findings: (1) although the data were not synchronized in time across signers and discourses, the first eight common PMs contained 94.6% of the variance of the movements; (2) the number of PMs that represented 94.6% of the variance was nearly the same for individual as for common PMs; (3) the PM subspaces were highly similar across signers. These results suggest that upper-body motion in unconstrained continuous SL discourses can be described through the dynamic combination of a reduced number of elementary movements. This opens up promising perspectives toward providing efficient automatic SL processing tools based on heavy mocap datasets, in particular for automatic recognition and generation.

COMBINING TAGUCHI METHOD, PRINCIPAL COMPONENT ANALYSIS AND FUZZY LOGIC TO THE TOLERANCE DESIGN OF A DUAL-PURPOSE SIX-BAR MECHANISM

2010 ◽  
Vol 34 (2) ◽  
pp. 277-293 ◽  
Author(s):  
Fu-Chen Chen ◽  
Yih-Fong Tzeng ◽  
Meng-Hui Hsu ◽  
Wei-Ren Chen
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Logic ◽  
Taguchi Method ◽  
Hybrid Approach ◽  
Principal Component ◽  
Component Analysis ◽  
Tolerance Design ◽  
Dual Purpose ◽  
Control Factors ◽  
Key Dimensions

A hybrid approach of combining Taguchi method, principal component analysis and fuzzy logic for the tolerance design of a dual-purpose six-bar mechanism is proposed. The approach is to firstly use the Taguchi orthogonal array to carry out experiments for calculating the S/N ratios of the positional errors to the angular error of the dual-purpose six-bar mechanism. The principal component analysis is then applied to determine the principal components of the S/N ratios, which are transformed via fuzzy logic reasoning into a multiple performance index (MPI) for further analysis of the effect of each control factors on the quality of the mechanism. Through the analysis of response table and diagram, key dimensional tolerances can be classified, which allows the decision of either to tighten the key tolerances to improve mechanism quality or to relax the tolerance of non-key dimensions to reduce manufacturing costs to be made.

scholarly journals A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2229 ◽  
Author(s):  
Mansoor Khan ◽  
Tianqi Liu ◽  
Farhan Ullah
Keyword(s):  
Principal Component Analysis ◽  
Renewable Energy ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Algorithm ◽  
Hybrid Approach ◽  
Principal Component ◽  
Component Analysis ◽  
Wind Data ◽  
Deep Learning Algorithm

Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results.

scholarly journals PREDICTING LAND PRICES AND MEASURING UNCERTAINTY BY COMBINING SUPERVISED AND UNSUPERVISED LEARNING

2021 ◽  
Vol 25 (2) ◽  
pp. 169-178
Author(s):  
Changro Lee
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Deep Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Hybrid Approach ◽  
Principal Component ◽  
Component Analysis ◽  
Land Prices ◽  
Property Valuation

Despite the popularity deep learning has been gaining, measuring the uncertainty within the result has not met expectations in many deep learning applications and this includes property valuation. In real-world tasks, however, rather than simply requiring predictions, assurance of the certainty of the predictions is also demanded. In this study, supervised learning is combined with unsupervised learning to bridge this gap. A method based on principal component analysis, a popular tool of unsupervised learning, was developed and used to represent the uncertainty in property valuation. Then, a neural network, a representative algorithm to implement supervised learning, was constructed, and trained to predict land prices. Finally, the uncertainty that was measured using principal component analysis was incorporated into the price predicted by the neural network. This hybrid approach is shown to be likely to improve the credibility of the valuation work. The findings of this study are expected to generate interest in the integration of the two learning approaches, thereby promoting the rapid adoption of deep learning tools in the property valuation industry.

scholarly journals Face models based on a guided PCA of motion-capture data: speaker dependent variability in /s/-/R/ contrast production

2005 ◽  
Vol 40 ◽  
pp. 95-108
Author(s):  
Shinji Maeda
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Motion Capture ◽  
Principal Component ◽  
Acoustic Properties ◽  
Motion Capture Data ◽  
Motion Capture System ◽  
Reasonable Accuracy ◽  
Speaker Variability ◽  
Coordinate Data

We measure face deformations during speech production using a motion capture system, which provides 3D coordinate data of about 60 markers glued on the speaker's face. An arbitrary orthogonal factor analysis followed by a principal component analysis (together called a guided PCA) of the data has showed that the first 6 factors explain about 90% of the variance, for each of our 3 speakers. The 6 derived factors, therefore, allow us to efficiently analyze or to reconstruct with a reasonable accuracy the observed face deformations. Since these factors can be interpreted in articulatory terms, they can reveal underlying articulatory organizations. The comparison of lip gestures in terms of data derived factors suggests that these speakers differently maneuver the lips to achieve contrast between /s/ and /R/. Such inter-speaker variability can occur because the acoustic contrast of these fricatives is shaped not only by the lip tube but also by cavities inside the mouth such as the sublingual cavity. In other words, these tube and cavity can acoustically compensate each other to produce their required acoustic properties.  

Generating Real-Time Responsive Balance Recovery Animation

2011 ◽  
Vol 219-220 ◽  
pp. 391-395
Author(s):  
Rong Fei Ma
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Motion Capture ◽  
Biomechanical Model ◽  
Principal Component ◽  
Component Analysis ◽  
Contact Forces ◽  
Balance Recovery ◽  
Empirical Results

We propose a novel Biomechanics-based Responsive Balance Recovery (BRBR) technique for synthesizing realistic balance recovery animations. First, our BRBR technique is based on a simplified human biomechanical model of keeping balance, so as to interactively respond to contact forces in the environment. Then, we employ the Principal Component Analysis (PCA) to reduce the dimensions of the mocap (motion capture) database to ensure the search for the most qualified return-to segment in real-time. Finally, empirical results from three cases validate the approach.

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