Nonlinear dance motion analysis and motion editing using Hilbert-Huang transform

Human motions (especially dance motions) are very noisy, and it is hard to analyze and edit the motions. To resolve this problem, we propose a new method to decompose and modify the motions using the Hilbert-Huang transform (HHT). First, HHT decomposes a chromatic signal into "monochromatic" signals that are the so-called Intrinsic Mode Functions (IMFs) using an Empirical Mode Decomposition (EMD) [6]. After applying the Hilbert Transform to each IMF, the instantaneous frequencies of the "monochromatic" signals can be obtained. The HHT has the advantage to analyze non-stationary and nonlinear signals such as human-joint-motions over FFT or Wavelet transform. In the present paper, we propose a new framework to analyze and extract some new features from a famous Japanese threesome pop singer group called "Perfume", and compare it with Waltz and Salsa dance. Using the EMD, their dance motions can be decomposed into motion (choreographic) primitives or IMFs. Therefore we can scale, combine, subtract, exchange, and modify those IMFs, and can blend them into new dance motions self-consistently. Our analysis and framework can lead to a motion editing and blending method to create a new dance motion from different dance motions.Comment: 6 pages, 10 figures, Computer Graphics International 2017, Conference short pape

Introducing Adaptive Incremental Dynamic Analysis: A New Tool for Linking Ground Motion Selection and Structural Response Assessment

Adaptive Incremental Dynamic Analysis (AIDA) is a novel ground motion selection scheme that adaptively changes the ground motion suites at different ground motion intensity levels to match hazardconsistent properties for structural response assessment. Incremental DynamicAnalysis (IDA), a current dynamic response history analysis practice in Performance-Based Earthquake Engineering (PBEE), uses the same suite of ground motions at all Intensity Measure (IM) levels to estimate structural response. Probabilistic Seismic Hazard Analysis (PSHA) deaggregation tells us, however, that the target distributions of important ground motion properties change as the IM levels change. To match hazard-consistent ground motion properties, ground motions can be re-selected at each IM level, but ground motion continuity is lost when using such “stripes” (i.e., individual analysis points at each IM level). Alternatively, the data from the same ground motions in IDA can be re-weighted at various IM levels to match their respective target distributions of properties, but this implies potential omission of data and curse of dimensionality. Adaptive Incremental Dynamic Analysis, in contrast, gradually changes ground motion records to match ground motion properties as the IM level changes, while also partially maintaining ground motion continuity without the omission of useful data. AIDA requires careful record selection across IM levels. Potential record selection criteria include ground motion properties from deaggregation, or target spectrum such as the Conditional Spectrum. Steps to perform AIDA are listed as follows: (1) obtain target ground motion properties for each IM level; (2) determine “bin sizes” (i.e., tolerance for acceptable ground motion properties) and identify all candidate ground motions that fall within target bins; (3) keep ground motions that are usable at multiple IM levels, to maintain continuity; (4) use each ground motion for IDA within its allowable IM range. As a result, if we keep increasing the “bin sizes”, AIDA will approach IDA asymptotically; on the other hand, if we decrease the “bin sizes”, AIDA will approach the other end of “stripes”. This paper addresses the challenges of changing records across various IM levels. Different ground motion selection schemes are compared with AIDA to demonstrate the advantages of using AIDA. Example structural analyses are used to illustrate the impact of AIDA on the estimation of structural response in PBEE. By combining the benefits of IDA and PSHA without the omission of useful data, AIDA is a promising new tool for linking ground motion selection and structural response assessment

PCA-based lung motion model

Organ motion induced by respiration may cause clinically significant targeting errors and greatly degrade the effectiveness of conformal radiotherapy. It is therefore crucial to be able to model respiratory motion accurately. A recently proposed lung motion model based on principal component analysis (PCA) has been shown to be promising on a few patients. However, there is still a need to understand the underlying reason why it works. In this paper, we present a much deeper and detailed analysis of the PCA-based lung motion model. We provide the theoretical justification of the effectiveness of PCA in modeling lung motion. We also prove that under certain conditions, the PCA motion model is equivalent to 5D motion model, which is based on physiology and anatomy of the lung. The modeling power of PCA model was tested on clinical data and the average 3D error was found to be below 1 mm.Comment: 4 pages, 1 figure. submitted to International Conference on the use of Computers in Radiation Therapy 201

Volumetric microvascular imaging of human retina using optical coherence tomography with a novel motion contrast technique

Phase variance-based motion contrast imaging is demonstrated using a spectral domain optical coherence tomography system for the in vivo human retina. This contrast technique spatially identifies locations of motion within the retina primarily associated with vasculature. Histogram-based noise analysis of the motion contrast images was used to reduce the motion noise created by transverse eye motion. En face summation images created from the 3D motion contrast data are presented with segmentation of selected retinal layers to provide non-invasive vascular visualization comparable to currently used invasive angiographic imaging. This motion contrast technique has demonstrated the ability to visualize resolution-limited vasculature independent of vessel orientation and flow velocity

Orthogonal Matching Pursuit: A Brownian Motion Analysis

A well-known analysis of Tropp and Gilbert shows that orthogonal matching pursuit (OMP) can recover a k-sparse n-dimensional real vector from 4 k log(n) noise-free linear measurements obtained through a random Gaussian measurement matrix with a probability that approaches one as n approaches infinity. This work strengthens this result by showing that a lower number of measurements, 2 k log(n - k), is in fact sufficient for asymptotic recovery. More generally, when the sparsity level satisfies kmin <= k <= kmax but is unknown, 2 kmax log(n - kmin) measurements is sufficient. Furthermore, this number of measurements is also sufficient for detection of the sparsity pattern (support) of the vector with measurement errors provided the signal-to-noise ratio (SNR) scales to infinity. The scaling 2 k log(n - k) exactly matches the number of measurements required by the more complex lasso method for signal recovery with a similar SNR scaling.Comment: 11 pages, 2 figure

The role of motion analysis in elite soccer

The optimal physical preparation of elite soccer (association football) players has become an indispensable part of the professional game especially due to the increased physical demands of match-play. The monitoring of players’ work-rate profiles during competition is now feasible through computer-aided motion analysis. Traditional methods of motion analysis were extremely labour intensive and were largely restricted to university- based research projects. Recent technological developments have meant that sophisticated systems, capable of quickly recording and processing the data of all players’ physical contributions throughout an entire match, are now being used in elite club environments. In recognition of the important role motion analysis now plays as a tool for measuring the physical performance of soccer players, this review critically appraises various motion analysis methods currently employed in elite soccer and explores research conducted using these methods. This review therefore aims to increase the awareness of both practitioners and researchers of the various motion analysis systems available, identify practical implications of the established body of knowledge, while highlighting areas that require further exploration

Analysis Methods of Errors (Motion and Atmospheric) in Synthetic Aperture Radar (SAR) Images

A method to allow the analysis of the effects of motion and atmospheric errors in SAR images is here presented. The objective of the method is to allow the visualization of the effects of motion errors and atmospheric artefacts on the processed (focused) SAR image. The method is intended to allow the analysis of the interaction of motion and atmospheric errors with the adopted SAR processing procedure and motion compensation algorithms. In this article the analysis method has been applied and tested to a C-Band E-SAR (DLR airborne SAR system) data set where we see that the effects of linear and non-linear phase errors observed are in agreement with the theory