PCA Decomposition Research Articles

Optimised mesh traversal for dynamic mesh compression

During the last decade many algorithms for compressing 3D animations represented by sequences of triangular meshes have been proposed. Most of these algorithms are lossy in their nature, i.e. the reconstructed data do not exactly match the algorithm input. Quite surprisingly, most of the existing algorithms mainly use only general compression techniques, such as entropy coding, quantisation, PCA or wavelet decomposition, while the inherent geometrical properties of the compressed surface remain unexploited. In this paper we focus on geometry specific optimisation: we extend the PCA-based dynamic mesh compression by optimising the order in which the mesh is traversed. By considering the distribution of residuals and optimising the gate selection strategy we achieve data rate reductions by 5.9–29.1% over the existing approaches in the experiments, while the error introduced by compression remains unchanged. This optimisation improves the performance of our encoder above the performance of current state of the art algorithms.

Comparing independent component analysis and non-negative matrix factorisation in the identification of event-related brain dynamics

Event Abstract Back to Event Comparing independent component analysis and non-negative matrix factorisation in the identification of event-related brain dynamics Sandra Ortega-Martorell1*, Ivan Olier2, Alfredo Vellido3, Paulo J. Lisboa4 and Wael El-Deredy2 1 Universitat Autònoma de Barcelona, Spain 2 University of Manchester, United Kingdom 3 Universitat Politècnica de Catalunya, Spain 4 Liverpool John Moores University, United Kingdom Matrix factorisation methods (such as principal and independent component analysis - PCA, ICA-, or singular value decomposition SVD) are commonly used for decomposing EEG data into factors from which the likely (latent) generating sources could be inferred or identified (Jung et al., 2000; Hoang et al., 2004). Since such factorisations are non-unique, different methods impose different constraints or priors in order to obtain a solution (e.g. components must be orthogonal in PCA, whereas independent in ICA). In non-negative matrix factorisation NMF (Paatero & Tapper,1994; Lee & Seung, 1999), the data matrix, X, is factorised into two non-negative matrices, W and H, whose product provides a good approximation of the original (X ≈ WH). Iterative methods are used to minimise the divergence between X and the product of W and H. In this study, a least squares method (Paatero & Tapper,1994 ) was used. We compared NMF and ICA in terms of: i. Their ability to identify eye movement artifacts in high density EEG electrode arrays. ii. Specificity in the separation of different event related brain dynamics from simultaneously activated sensory modalities. iii. Their computational cost, to assess their suitability for real time processing for the purposes of BCI applications. We used EEG recordings from 15 subjects as in (Brown et al., 2008). NMF was as good as ICA for identifying eye movement and separating different events. The former was much better, though, in the resolution of the components obtained. Computationally, NMF was marginally more efficient than FastICA. Keywords: Brain Signals, EEG Conference: XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011. Presentation Type: Poster Presentation Topic: Poster Sessions: Modeling and Analysis of Brain Signals Citation: Ortega-Martorell S, Olier I, Vellido A, Lisboa PJ and El-Deredy W (2011). Comparing independent component analysis and non-negative matrix factorisation in the identification of event-related brain dynamics. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00082 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 16 Nov 2011; Published Online: 25 Nov 2011. * Correspondence: Dr. Sandra Ortega-Martorell, Universitat Autònoma de Barcelona, Barcelona, Spain, S.OrtegaMartorell@ljmu.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Sandra Ortega-Martorell Ivan Olier Alfredo Vellido Paulo J Lisboa Wael El-Deredy Google Sandra Ortega-Martorell Ivan Olier Alfredo Vellido Paulo J Lisboa Wael El-Deredy Google Scholar Sandra Ortega-Martorell Ivan Olier Alfredo Vellido Paulo J Lisboa Wael El-Deredy PubMed Sandra Ortega-Martorell Ivan Olier Alfredo Vellido Paulo J Lisboa Wael El-Deredy Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Thermal Analysis of Facial Muscles Contractions

Facial expressions can be systematically coded using the Facial Action Coding System (FACS) that describes the specific action unit (AU) or combination of AUs elicited during different kinds of expressions. This study investigated the thermal patterns concomitant to specific action units performance. As thermal imaging can track dynamic patterns in facial temperature at any distance (>0.4 m), with high temporal (<20 m) and thermal (<20 mK@300 K) resolutions, this noninvasive technique was tested as a method to assess fluctuations of facial heat patterns induced by facial muscles contractions. Four FACS-trained coders produced nine different AUs or combination of AUs at various speeds and intensities. Using a spatial pattern approach based on PCA decomposition of the thermal signal, we showed that thermal fluctuations are specific to the activated AUs and are sensitive to the kinetics and intensities of AU production. These results open new avenues for studying patterns of facial muscle activity related to emotion or other cognitively induced activities, in a noninvasive manner, avoiding potential lighting issues.

Discrete wavelet and modified PCA decompositions for postural stability analysis in biometric applications

The aim of this study is to compare the Discrete wavelet decomposition and the modified Principal Analysis Component (PCA) decomposition to analyze the stabilogram for the purpose to provide a new insight about human postural stability. Discrete wavelet analysis is used to decompose the stabilogram into several timescale components (i.e. detail wavelet coefficients and approximation wavelet coefficients). Whereas, the modified PCA decomposition is applied to decompose the stabilogram into three components, namely: trend, rambling and trembling. Based on the modified PCA analysis, the trace of analytic trembling and rambling in the complex plan highlights a unique rotation center. The same property is found when considering the detail wavelet coefficients. Based on this property, the area of the circle in which 95% of the trace’s data points are located, is extracted to provide important information about the postural equilibrium status of healthy subjects (average age 31 ± 11 years). Based on experimental results, this parameter seems to be a valuable parameter in order to highlight the effect of visual entries, stabilogram direction, gender and age on the postural stability. Obtained results show also that wavelets and the modified PCA decomposition can discriminate the subjects by gender which is particularly interesting in biometric applications and human stability simulation. Moreover, both techniques highlight the fact that male are less stable than female and the fact that there is no correlation between human stability and his age (under 60).

A PCA Decomposition for Real‐time BRDF Editing and Relighting with Global Illumination

AbstractWe propose a novel rendering method which supports interactive BRDF editing as well as relighting on a 3D scene. For interactive BRDF editing, we linearize an analytic BRDF model with basis BRDFs obtained from a principal component analysis. For each basis BRDF, the radiance transfer is precomputed and stored in vector form. In rendering time, illumination of a point is computed by multiplying the radiance transfer vectors of the basis BRDFs by the incoming radiance from gather samples and then linearly combining the results weighted by user‐controlled parameters. To improve the level of accuracy, a set of sub‐area samples associated with a gather sample refines the glossy reflection of the geometric details without increasing the precomputation time. We demonstrate this program with a number of examples to verify the real‐time performance of relighting and BRDF editing on 3D scenes with complex lighting and geometry.

PROJECTIVE NON-NEGATIVE MATRIX FACTORIZATION WITH APPLICATIONS TO FACIAL IMAGE PROCESSING

We propose a new variant of Non-negative Matrix Factorization (NMF), including its model and two optimization rules. Our method is based on positively constrained projections and is related to the conventional SVD or PCA decomposition. The new model can potentially be applied to image compression and feature extraction problems. Of the latter, we consider processing of facial images, where each image consists of several parts and for each part the observations with different lighting mainly distribute along a straight line through the origin. No regularization terms are required in the objective functions and both suggested optimization rules can easily be implemented by matrix manipulations. The experiments show that the derived base vectors are spatially more localized than those of NMF. In turn, the better part-based representations improve the recognition rate of semantic classes such as the gender or existence of mustache in the facial images.

A new local PCA-SOM algorithm

This paper proposes a local PCA-SOM algorithm. The new competition measure is computational efficient, and implicitly incorporates the Mahalanobis distance and the reconstruction error. The matrix inversion or PCA decomposition for each data input is not needed as compared to the previous models. Moreover, the local data distribution is completely stored in the covariance matrix instead of the pre-defined numbers of the principal components. Thus, no priori information of the optimal principal subspace is required. Experiments on both the synthesis data and a pattern learning task are carried out to show the performance of the proposed method.

On-line novelty detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions

Leakage detection is a common chemical-sensing application. Leakage detection by thresholds on a single sensor signal suffers from important drawbacks when sensors show drift effects or when they are affected by other long-term cross sensitivities. In this paper, we present an adaptive method based on a recursive dynamic principal component analysis (RDPCA) algorithm that models the relationships between the sensors in the array and their past history. In normal conditions, a certain variance distribution characterizes sensor signals, however, in the presence of a new source of variance the PCA decomposition changes drastically. In order to prevent the influence of sensor drift, the model is adaptive, and it is calculated in a recursive manner with minimum computational effort. The behavior of this technique is studied with synthetic and real signals arising by oil vapor leakages in an air compressor. Results clearly demonstrate the efficiency of the proposed method

Active phases and pauses during the installation of the West African monsoon through 5‐day CMAP rainfall data (1979–2001)

Objective diagnostic analyses based on PCA decomposition, Varimax rotation and low‐pass filters (≥1 month) are performed on the 5‐day CMAP rainfall data over West Africa. The northward excursion of the rainbelt can be divided into 4 basic subperiods each composed of an equilibrium period (pause) followed by a rapid increase (active phase) of the system. We suggest easy criteria to automatically detect these events and propose a calendar for the 23 years. The pauses are centered around 27–28 March (pause #1), between 29 April and 2 May (pause #2), 11 June (pause #3) and 24 July (pause # 4) with standard deviations of 7–11 days. The phase of any pause impacts on the phase of other pauses. Pause #4, starting earlier, is preceded by earlier pauses #2 and #3, then is followed by an earlier annual rainfall maximum and more abundant June–August amounts.

Sensor Fault Detection via Multiscale Analysis and Dynamic PCA

In this paper, a new approach to sensor validation in real time is described that is based on (1) representation of the sensor signal by wavelets, (2) decomposition of the signal for different frequency ranges, (3) formation of a matrix of lagged details from a window of the original sensor data at different frequencies, (4) application of PCA decomposition to the matrix of details, and (5) diagnosis of the existence of faults via tests on the PCA statistics such as T2 and Q. The proposed strategy is able to isolate the effect of noise and process changes from the effects of physical changes in the sensor itself. For comparison we applied all of the tests and diagnoses to the original signal itself as well as to the wavelet details. To demonstrate the circumstances under which the above strategy might be used, a simulated noisy signal from a CSTR and a temperature signal from an operating pilot distillation column were analyzed. Faults were introduced into the thermocouple, and the diagnosis carried out. The results of the diagnosis indicated that the proposed strategy had low type I (false alarm) and type II (failure to detect faults) errors.

Mechanism of reaction of palladium(II) carboxylates with carbon monoxide in nonaqueous media

Abstract The reduction of Pd II carboxylates by carbon monoxide in benzene and AcOH solutions has been studied. In the case of Pd II acetate the reaction is found to proceed through an intermediate complex with a Pd-CO-CH 3 group and the Pd I carbonylacetate [Pd(CO)(OAc)] 4 (PCA). A reductive condensation type of mechanism is suggested for the PCA decomposition on the base of kinetic data. The metal cluster skeleton is proposed to increase stepwise as the oxidation state of Pd decreases. A number of Pd I complexes, PCA analogs having composition [Pd(CO)(OCOR)] 4 , where R = C 2 H 5 , C 6 H 5 , CF 3 , CCl 3 , CH 2 Cl, have been prepared.