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Cristiano Cervellera, Danilo Maccio
In this paper, we discuss how the extreme learning machine (ELM) framework can be effectively employed in the unsupervised context of multivariate density estimation. In particular, two algorithms are introduced, one for the estimation of the cumulative distribution function underlying the observed data, and one for the estimation of the probability density function. The algorithms rely on the concept of $F$-discrepancy, which is closely related to the Kolmogorov-Smirnov criterion for goodness of fit. Both methods retain the key feature of the ELM of providing the solution through random assignment of the hidden feature map and a very light computational burden...
January 17, 2017: IEEE Transactions on Cybernetics
Yiyi Ren, Xiang Xie, Guolin Li, Zhihua Wang
A hand segmentation framework is proposed for 3-D hand gesture interaction for wearable devices. In this framework, all the objects in a scene are regarded as directed trees in a forest, and the problem of the hand segmentation can be converted into finding the target tree (called hand tree) in the forest with proper hand properties including color consistency, space consistency, disparity, and hand shape constraints. The forest grows scan-line by scan-line from high reliable regions to low reliable regions...
January 16, 2017: IEEE Transactions on Cybernetics
Pranav Deshpande, M Sabarimalai Manikandan
Accurate determination of glottal instants and electroglottographic (EGG) parameters is most important in voice pathology analysis including multiple voice disorders: neurological, functional, and laryngeal diseases. In this paper, we present a new effective method for reliable detection of glottal instants and EGG parameters from an EGG signal composed of voiced and non-voice segments. In the first stage, we present an adaptive variational mode decomposition (aVMD) based algorithm for suppressing low-frequency artifacts and additive high-frequency noises...
January 17, 2017: IEEE Journal of Biomedical and Health Informatics
Avan Suinesiaputra, Pierre Ablin, Xenia Alba, Martino Alessandrini, Jack Allen, W Bai, Serkan Cimen, Peter Claes, Brett R Cowan, Jan D'hooge, Nicolas Duchateau, Jan Ehrhardt, Alejandro F Frangi, Ali Gooya, Vicente Grau, Karim Lekadir, Allen Lu, Anirban Mukhopadhyay, Ilkay Oksuz, Xavier Pennec, Marco Pereanez, Catarina Pinto, Paolo Piras, Marc-Michel Rohe, Daniel Rueckert, Maxime Sermesant, Kaleem Siddiqi, Mahdi Tabassian, Luciano Teresi, Sotirios A Tsaftaris, Matthias Wilms, Alistair A Young, Xingyu Zhang, Pau Medrano-Gracia
Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients...
January 17, 2017: IEEE Journal of Biomedical and Health Informatics
Vincent Drouard, Radu Horaud, Antoine Deleforge, Sileye Ba, Georgios Evangelidis
Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to- face alignment errors. We propose tu use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Zhao Zhang, Fanzhang Li, Mingbo Zhao, Li Zhang, Shuicheng Yan
We propose two nuclear- and L2,1-norm regularized two-dimensional neighborhood preserving projection (2DNPP) methods for extracting representative 2D image features. 2DNPP extracts neighborhood preserving features via minimizing the Frobenius norm based reconstruction error that is very sensitive to noise and outliers in data. To make the distance metric more reliable and robust, and encode the neighborhood reconstruction error more accurately, we minimize the nuclear- and L2,1-norm based reconstruction error respectively and measure it over each image...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Vincent Van Nieuwenhove, Jan De Beenhouwer, Thomas De Schryver, Luc Van Hoorebeke, Jan Sijbers
In Computed Tomography (CT), motion and deformation during the acquisition lead to streak artefacts and blurring in the reconstructed images. To remedy these artefacts, we introduce an efficient algorithm to estimate and correct for global affine deformations directly on the cone beam projections. The proposed technique is data-driven and thus removes the need for markers and/or a tracking system. A relationship between affine transformations and the cone beam transform is proven and used to correct the projections...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Seungeon Kim, Yongjin Chang, Jong Beom Ra
Even though the X-ray CT scan is considered suitable for fast imaging, motion-artifact-free cardiac imaging is still an important issue because the gantry rotation speed is not fast enough compared with the heart motion. To obtain a heart image with less motion artifacts, a motion estimation (ME) and motion compensation (MC) approach is usually adopted. In this paper, we propose a ME/MC algorithm that can estimate a non-linear heart motion model from a sinogram with a rotation angle of less than 360°. In this algorithm, we first assume the heart motion to be non-rigid but linear, and thereby estimate an initial 4D motion vector field (MVF) during a half rotation by using conjugate partial angle reconstructed images, as in our previous ME/MC algorithm...
January 17, 2017: IEEE Transactions on Medical Imaging
Mehrtash Harandi, Mathieu Salzmann, Richard Hartley
Representing images and videos with Symmetric Positive Definite (SPD) matrices, and considering the Riemannian geometry of the resulting space, has been shown to yield high discriminative power in many visual recognition tasks. Unfortunately, computation on the Riemannian manifold of SPD matrices -especially of high-dimensional ones- comes at a high cost that limits the applicability of existing techniques. In this paper, we introduce algorithms able to handle high-dimensional SPD matrices by constructing a lower-dimensional SPD manifold...
January 18, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Nordine Sebkhi, Dhyey Desai, Mohammad Islam, Jun Lu, Kimberly Wilson, Maysam Ghovanloo
Speech-language pathologists (SLPs) are trained to correct articulation of people diagnosed with motor speech disorders by analyzing articulators' motion and assessing speech outcome while patients speak. To assist SLPs in this task, we are presenting the Multimodal Speech Capture System (MSCS) that records and displays kinematics of key speech articulators, the tongue and lips, along with voice, using unobtrusive methods. Collected speech modalities, tongue motion, lips gestures, and voice, are visualized not only in real-time to provide patients with instant feedback but also offline to allow SLPs to perform post-analysis of articulators' motion, particularly the tongue, with its prominent but hardly visible role in articulation...
January 18, 2017: IEEE Transactions on Bio-medical Engineering
Weili Shi
The Shan Shui in the World project recreated Manhattan, New York, in the style of traditional Chinese shanshui paintings by using a generative algorithm to transform its buildings into mountains. This project revisits the ideas implicit in Chinese literati paintings of shan shui: the relationship between urban life and people's yearning for nature and between social responsibility and spiritual purity. Shan Shui in the World was made possible by the growing ubiquitousness of data and the development of data visualization techniques, especially generative art...
January 2017: IEEE Computer Graphics and Applications
Matthias F Stallmann
Although surveys suggest positive student attitudes toward the use of algorithm animations, it is not clear that they improve learning outcomes. The Graph Algorithm Animation Tool, or Galant, challenges and motivates students to engage more deeply with algorithm concepts, without distracting them with programming language details or GUIs. Even though Galant is specifically designed for graph algorithms, it has also been used to animate other algorithms, most notably sorting algorithms.
January 2017: IEEE Computer Graphics and Applications
Giuliano Grossi, Raffaella Lanzarotti, Jianyi Lin
In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error...
2017: PloS One
Erik Andrews, Yue Wang, Tian Xia, Wenqing Cheng, Chao Cheng
Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use...
January 19, 2017: PLoS Computational Biology
Edward S Lee, Paul G Thomas, Jeff E Mold, Andrew J Yates
Characterisation of the T cell receptors (TCR) involved in immune responses is important for the design of vaccines and immunotherapies for cancer and autoimmune disease. The specificity of the interaction between the TCR heterodimer and its peptide-MHC ligand derives largely from the juxtaposed hypervariable CDR3 regions on the TCRα and TCRβ chains, and obtaining the paired sequences of these regions is a standard for functionally defining the TCR. A brute force approach to identifying the TCRs in a population of T cells is to use high-throughput single-cell sequencing, but currently this process remains costly and risks missing small clones...
January 19, 2017: PLoS Computational Biology
Ioana Frinc, Petru Ilies, Florin Zaharie, Delia Dima, Alina Tanase, Ljubomir Petrov, Alexandru Irimie, Cristian Berce, Cosmin Lisencu, Ioana Berindan-Neagoe, Ciprian Tomuleasa, Anca Bojan
In the last decade, there is significant progress in clinical hematology with the discovery of targeted molecules and thus the achievement of both hematologic and molecular responses. Nevertheless, chemotherapy remains the treatment of choice for many types of hematological malignancies. Aggressive chemotherapy leads to immunosuppression, accompanied by a high rate of infections and an increased rate of treatment-related mortality. Invasive fungal infections, as well as more common bacterial and viral infections are frequent in immunocompromised patients as they are difficult to diagnose and treat...
January 18, 2017: Romanian Journal of Internal Medicine, Revue Roumaine de Médecine Interne
Faliang Huang, Xuelong Li, Shichao Zhang, Jilian Zhang
To automatically determine the number of clusters and generate more quality clusters while clustering data samples, we propose a harmonious genetic clustering algorithm, named HGCA, which is based on harmonious mating in eugenic theory. Different from extant genetic clustering methods that only use fitness, HGCA aims to select the most suitable mate for each chromosome and takes into account chromosomes gender, age, and fitness when computing mating attractiveness. To avoid illegal mating, we design three mating prohibition schemes, i...
January 5, 2017: IEEE Transactions on Cybernetics
Damien Lefloch, Markus Kluge, Hamed Sarbolandi, Tim Weyrich, Andreas Kolb
Interactive real-time scene acquisition from hand-held depth cameras has recently developed much momentum, enabling applications in ad-hoc object acquisition, augmented reality and other fields. A key challenge to online reconstruction remains error accumulation in the reconstructed camera trajectory, due to drift-inducing instabilities in the range scan alignments of the underlying iterative-closest-point (ICP) algorithm. Various strategies have been proposed to mitigate that drift, including SIFT-based pre-alignment, color-based weighting of ICP pairs, stronger weighting of edge features, and so on...
January 5, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Israel Gebru, Sileye Ba, Xiaofei Li, Radu Horaud
Speaker diarization consists of assigning speech signals to people engaged in a dialogue. An audio-visual spatiotemporal diarization model is proposed. The model is well suited for challenging scenarios that consist of several participants engaged in multi-party interaction while they move around and turn their heads towards the other participants rather than facing the cameras and the microphones. Multiple-person visual tracking is combined with multiple speech-source localization in order to tackle the speech-to-person association problem...
January 5, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Luca Puggini, Sean Mcloone
Principal Component Analysis (PCA) is a powerful and widely used tool for dimensionality reduction. However, the principal components generated are linear combinations of all the original variables and this often makes interpreting results and root-cause analysis difficult. Forward Selection Component Analysis (FSCA) is a recent technique that overcomes this difficulty by performing variable selection and dimensionality reduction at the same time. This paper provides, for the first time, a detailed presentation of the FSCA algorithm, and introduces a number of new variants of FSCA that incorporate a refinement step to improve performance...
January 5, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
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