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CiteSeerX — Multilinear Independent Components Analysis
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Independent Components Analysis (ICA) maximizes the statistical independence of the representational components of a training image ensemble, but it cannot distinguish between the different factors, or modes, inherent to image formation, including scene structure, illumination, and imaging.
Can Sony bring Edge AI and software subscription together ...
The COVID-19 pandemic has created an urgent need in the biometrics market for identification systems that can support evolving requirements for temperature scanning and face mask usage. There is a company that could apply the software-as-a-service model to biometric technologies in order to offer ...
Financial time series forecasting using independent ...
An example is used for illustrating the concept of the TnA method.Fig. 2 shows four financial time series data, each of size 1 × 794, which can be combined as a mixture matrix X of size 4 × 794. After using ICA method to the matrix X, a de-mixing matrix W of size 4 × 4 and four ICs, each of size 1 × 794, can be estimated. The profiles of those four ICs are shown in Fig. 3.
Real-Time Face Detection and Recognition for Video ...
The recognition stage is based on an improved independent components Analysis approach which has been modified to cope with the video surveillance application. In the recognition stage, the Hausdorff distance is used as a similarity measure between a general face model and possible instances of the object within the image....
Multilinear independent components analysis - IEEE ...
Abstract: Independent components analysis (ICA) maximizes the statistical independence of the representational components of a training image ensemble, but it cannot distinguish between the different factors, or modes, inherent to image formation, including scene structure, illumination, and imaging. We introduce a nonlinear, multifactor model that generalizes ICA.
 Independent Component Analysis: Algorithms and Applications
The statistical model in Eq. 4 is called independent component analysis, or ICA model. The ICA model is a generative model, which means that it describes how the observed data are generated by a process of mixing the components si. The independent components are latent variables, meaning that they cannot be directly observed....
Anorexia Nervosa and Body Dysmorphic Disorder are ...
We used independent component analysis of task-related fMRI data in combination with ERP data from a separate session, using the same stimuli and paradigm to perform joint independent component analysis (jICA). jICA combines data from these two modalities by joint estimation of the temporal ERP components and spatial fMRI components (Calhoun et ......
3D Reconstruction and Face Recognition Using Kernel-Based ...
Keywords: Independent component analysis, 3D human face reconstruction, 3D human face recognition, back-propagation algorithm, neural networks. 1. Introduction When we use a camera to capture 3D objects, we lose the depth information of the 3D objects and only obtain the 2D image information. However, the depth information of the 3D objects ......
Multilinear Independent Components Analysis
Multilinear Independent Components Analysis M. Alex O. Vasilescu1,2 and Demetri Terzopoulos2,1 1Department of Computer Science, University of Toronto, Toronto ON M5S 3G4, Canada 2Courant Institute of Mathematical Sciences, New York University, New York, NY 10003, USA Abstract IndependentComponentsAnalysis(ICA)maximizesthesta-tistical independence of the representational components of...
Appearance-based three-dimensional object recognition ...
Recognition was then performed by locating the closest point in the manifold using radial basis function network, which gave the identity and view (or pose) of the object. The use of ICA, in place of principle component analysis is expected to give a `natural' manifold with maximum significant information with least redundancy.
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