BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

Data-driven time-frequency analysis of multivariate data

Empirical Mode Decomposition (EMD) is a data-driven method for the decomposition and time-frequency analysis of real world nonstationary signals. Its main advantages over other time-frequency methods are its locality, data-driven nature, multiresolution-based decomposition, higher time-frequency resolution and its ability to capture oscillation of any type (nonharmonic signals). These properties have made EMD a viable tool for real world nonstationary data analysis. Recent advances in sensor and data acquisition technologies have brought to light new classes of signals containing typically several data channels. Currently, such signals are almost invariably processed channel-wise, which is suboptimal. It is, therefore, imperative to design multivariate extensions of the existing nonlinear and nonstationary analysis algorithms as they are expected to give more insight into the dynamics and the interdependence between multiple channels of such signals. To this end, this thesis presents multivariate extensions of the empirical mode de- composition algorithm and illustrates their advantages with regards to multivariate non- stationary data analysis. Some important properties of such extensions are also explored, including their ability to exhibit wavelet-like dyadic filter bank structures for white Gaussian noise (WGN), and their capacity to align similar oscillatory modes from multiple data channels. Owing to the generality of the proposed methods, an improved multi- variate EMD-based algorithm is introduced which solves some inherent problems in the original EMD algorithm. Finally, to demonstrate the potential of the proposed methods, simulations on the fusion of multiple real world signals (wind, images and inertial body motion data) support the analysis

A novel facial expression recognition method using bi-dimensional EMD based edge detection

Facial expressions provide an important channel of nonverbal communication. Facial recognition techniques detect people’s emotions using their facial expressions and have found applications in technical fields such as Human-Computer-Interaction (HCI) and security monitoring. Technical applications generally require fast processing and decision making. Therefore, it is imperative to develop innovative recognition methods that can detect facial expressions effectively and efficiently. Traditionally, human facial expressions are recognized using standard images. Existing methods of recognition require subjective expertise and high computational costs. This thesis proposes a novel method for facial expression recognition using image edge detection based on Bi-dimensional Empirical Mode Decomposition (BEMD). In this research, a BEMD based edge detection algorithm was developed, a facial expression measurement metric was created, and an intensive database testing was conducted. The success rates of recognition suggest that the proposed method could be a potential alternative to traditional methods for human facial expression recognition with substantially lower computational costs. Furthermore, a possible blind-detection technique was proposed as a result of this research. Initial detection results suggest great potential of the proposed method for blind-detection that may lead to even more efficient techniques for facial expression recognition

A Study of Biomedical Time Series Using Empirical Mode Decomposition : Extracting event-related modes from EEG signals recorded during visual processing of contour stimuli

Noninvasive neuroimaging techniques like functional Magnetic Resonance Imaging (fMRI) and/or Electroencephalography (EEG) allow researchers to investigate and analyze brain activities during visual processing. EEG offers a high temporal resolution at a level of submilliseconds which can be combined favorably with fMRI which has a good spatial resolution on small spatial scales in the millimeter range. These neuroimaging techniques were, and still are instrumental in the diagnoses and treatments of neurological disorders in the clinical applications. In this PhD thesis we concentrate on lectrophysiological signatures within EEG recordings of a combined EEG-fMRI data set which where taken while performing a contour integration task. The estimation of location and distribution of the electrical sources in the brain from surface recordings which are responsible for interesting EEG waves has drawn the attention of many EEG/MEG researchers. However, this process which is called brain source localization is still one of the major problems in EEG. It consists of solving two modeling problems: forward and inverse. In the forward problem, one is interested in predicting the expected potential distribution on the scalp from given electrical sources that represent active neurons in the head. These evaluations are necessary to solve the inverse problem which can be defined as the problem of estimating the brain sources that generated the measured electrical potentials. This thesis presents a data-driven analysis of EEG data recorded during a combined EEG/fMRI study of visual processing during a contour integration task. The analysis is based on an ensemble empirical mode decomposition (EEMD) and discusses characteristic features of event related modes (ERMs) resulting from the decomposition. We identify clear differences in certain ERMs in response to contour vs non-contour Gabor stimuli mainly for response amplitudes peaking around 100 [ms] (called P100) and 200 [ms] (called N200) after stimulus onset, respectively. We observe early P100 and N200 responses at electrodes located in the occipital area of the brain, while late P100 and N200 responses appear at electrodes located in frontal brain areas. Signals at electrodes in central brain areas show bimodal early/late response signatures in certain ERMs. Head topographies clearly localize statistically significant response differences to both stimulus conditions. Our findings provide an independent proof of recent models which suggest that contour integration depends on distributed network activity within the brain. Next and based on the previous analysis, a new approach for source localization of EEG data based on combining ERMs, extracted with EEMD, with inverse models has been presented. As the first step, 64 channel EEG recordings are pooled according to six brain areas and decomposed, by applying an EEMD, into their underlying ERMs. Then, based upon the problem at hand, the most closely related ERM, in terms of frequency and amplitude, is combined with inverse modeling techniques for source localization. More specifically, the standardized low resolution brain electromagnetic tomography (sLORETA) procedure is employed in this work. Accuracy and robustness of the results indicate that this approach deems highly promising in source localization techniques for EEG data. Given the results of analyses above, it can be said that EMD is able to extract intrinsic signal modes, ERMs, which contain decisive information about responses to contour and non-contour stimuli. Hence, we introduce a new toolbox, called EMDLAB, which serves the growing interest of the signal processing community in applying EMD as a decomposition technique. EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on the EEG data. The main goal of EMDLAB toolbox is to extract characteristics of either the EEG signal by intrinsic mode functions (IMFs) or ERMs. Since IMFs reflect characteristics of the original EEG signal, ERMs reflect characteristics of ERPs of the original signal. The new toolbox is provided as a plug-in to the well-known EEGLAB which enables it to exploit the advantageous visualization capabilities of EEGLAB as well as statistical data analysis techniques provided there for extracted IMFs and ERMs of the signal

Research summary, January 1989 - June 1990

The Research Institute for Advanced Computer Science (RIACS) was established at NASA ARC in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 62 universities with graduate programs in the aerospace sciences, under a Cooperative Agreement with NASA. RIACS serves as the representative of the USRA universities at ARC. This document reports our activities and accomplishments for the period 1 Jan. 1989 - 30 Jun. 1990. The following topics are covered: learning systems, networked systems, and parallel systems

Modeling EMI Resulting from a Signal Via Transition Through Power/Ground Layers

Signal transitioning through layers on vias are very common in multi-layer printed circuit board (PCB) design. For a signal via transitioning through the internal power and ground planes, the return current must switch from one reference plane to another reference plane. The discontinuity of the return current at the via excites the power and ground planes, and results in noise on the power bus that can lead to signal integrity, as well as EMI problems. Numerical methods, such as the finite-difference time-domain (FDTD), Moment of Methods (MoM), and partial element equivalent circuit (PEEC) method, were employed herein to study this problem. The modeled results are supported by measurements. In addition, a common EMI mitigation approach of adding a decoupling capacitor was investigated with the FDTD method

Statistical Analysis and Forecasting of Economic Structural Change

In 1984, the University of Bonn (FRG) and IIASA created a joint research group to analyze the relationship between economic growth and structural change. The research team was to examine the commodity composition as well as the size and direction of commodity and credit flows among countries and regions. Krelle (1988) reports on the results of this "Bonn-IIASA" research project. At the same time, an informal IIASA Working Group was initiated to deal with problems of the statistical analysis of economic data in the context of structural change: What tools do we have to identify nonconstancy of model parameters? What type of models are particularly applicable to nonconstant structure? How is forecasting affected by the presence of nonconstant structure? What problems should be anticipated in applying these tools and models? Some 50 experts, mainly statisticians or econometricians from about 15 countries, came together in Lodz, Poland (May 1985); Berlin, GDR (June 1986); and Sulejov, Poland (September 1986) to present and discuss their findings. This volume contains a selected set of those conference contributions as well as several specially invited chapters. The introductory chapter "What can statistics contribute to the analysis of economic structural change?", discusses not only the role of statistics in the detection and assimilation of structural changes, but also the relevance of respective methods in the evaluation of econometric models. Trends in the development of these methods are indicated, and the contributions to the present volume are put into a broader context of empirical economics to help to bridge the gap between economists and statisticians. The chapters in the first section are concerned with the detection of parameter nonconstancy. The procedures discussed range from classical methods, such as the CUSUM test, to new concepts, particularly those based on nonparametric statistics. Several chapters assess the conditions under which these methods can be applied and their robustness under such conditions. The second section addresses models that are in some sense generalizations of nonconstant-parameter models, so that they can assimilate structural changes. The last section deals with real-life structural change situations

Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022

This open access book provides an overview of the progress in landslide research and technology and is part of a book series of the International Consortium on Landslides (ICL). It gives an overview of recent progress in landslide research and technology for practical applications and the benefit for the society contributing to understanding and reducing landslide disaster risk