Face recognition using nonparametric-weighted Fisherfaces

This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases. © 2012 Li et al

Lip segmentation using adaptive color space training

In audio-visual speech recognition (AVSR), it is beneficial to use lip boundary information in addition to texture-dependent features. In this paper, we propose an automatic lip segmentation method that can be used in AVSR systems. The algorithm consists of the following steps: face detection, lip corners extraction, adaptive color space training for lip and non-lip regions using Gaussian mixture models (GMMs), and curve evolution using level-set formulation based on region and image gradients fields. Region-based fields are obtained using adapted GMM likelihoods. We have tested the proposed algorithm on a database (SU-TAV) of 100 facial images and obtained objective performance results by comparing automatic lip segmentations with hand-marked ground truth segmentations. Experimental results are promising and much work has to be done to improve the robustness of the proposed method

Numerical Methods for Parasitic Extraction of Advanced Integrated Circuits

FFinFETs, also known as Fin Field Effect Transistors, are a type of non-planar transistors used in the modern integrated circuits. Fast and accurate parasitic capacitance and resistance extraction is crucial in the design and verification of Fin- FET integrated circuits. Though there are wide varieties of techniques available for parasitic extraction, FinFETs still pose tremendous challenges due to the complex geometries and user model of FinFETs. In this thesis, we propose three practical techniques for parasitic extraction of FinFET integrated circuits. The first technique we propose is to solve the dilemma that foundries and IP vendors face to protect the sensitive information which is prerequisite for accurate parasitic extraction. We propose an innovative solution to the challenge, by building a macro model around any region in 2D/3D on a circuit where foundries or IP vendors wish to hide information, yet the macro model allows accurate capacitance extraction inside and outside of the region. The second technique we present is to reduce the truncation error introduced by the traditional Neumann boundary condition. We make a fundamental contribution to the theory of field solvers by proposing a class of absorbing boundary conditions, which when placed on the boundary of the numerical region, will act as if the region extends to infinity. As a result, we can significantly reduce the size of the numerical region, which in turn reduces the run time without sacrificing accuracy. Finally, we improve the accuracy and efficiency of resistance extraction for Fin-FET with non-orthogonal resistivity interface through FVM and IFEM. The performance of FVM is comparable to FEM but with better stability since the conservation law is guaranteed. The IFEM is even better in both efficiency and mesh generation cost than other methods, including FDM, FEM and FVM. The proposed methods are based on rigorous mathematical derivations and verified through experimental results on practical example

Numerical Methods for Parasitic Extraction of Advanced Integrated Circuits

FFinFETs, also known as Fin Field Effect Transistors, are a type of non-planar transistors used in the modern integrated circuits. Fast and accurate parasitic capacitance and resistance extraction is crucial in the design and verification of Fin- FET integrated circuits. Though there are wide varieties of techniques available for parasitic extraction, FinFETs still pose tremendous challenges due to the complex geometries and user model of FinFETs. In this thesis, we propose three practical techniques for parasitic extraction of FinFET integrated circuits. The first technique we propose is to solve the dilemma that foundries and IP vendors face to protect the sensitive information which is prerequisite for accurate parasitic extraction. We propose an innovative solution to the challenge, by building a macro model around any region in 2D/3D on a circuit where foundries or IP vendors wish to hide information, yet the macro model allows accurate capacitance extraction inside and outside of the region. The second technique we present is to reduce the truncation error introduced by the traditional Neumann boundary condition. We make a fundamental contribution to the theory of field solvers by proposing a class of absorbing boundary conditions, which when placed on the boundary of the numerical region, will act as if the region extends to infinity. As a result, we can significantly reduce the size of the numerical region, which in turn reduces the run time without sacrificing accuracy. Finally, we improve the accuracy and efficiency of resistance extraction for Fin-FET with non-orthogonal resistivity interface through FVM and IFEM. The performance of FVM is comparable to FEM but with better stability since the conservation law is guaranteed. The IFEM is even better in both efficiency and mesh generation cost than other methods, including FDM, FEM and FVM. The proposed methods are based on rigorous mathematical derivations and verified through experimental results on practical example

Volume-Enclosing Surface Extraction

In this paper we present a new method, which allows for the construction of triangular isosurfaces from three-dimensional data sets, such as 3D image data and/or numerical simulation data that are based on regularly shaped, cubic lattices. This novel volume-enclosing surface extraction technique, which has been named VESTA, can produce up to six different results due to the nature of the discretized 3D space under consideration. VESTA is neither template-based nor it is necessarily required to operate on 2x2x2 voxel cell neighborhoods only. The surface tiles are determined with a very fast and robust construction technique while potential ambiguities are detected and resolved. Here, we provide an in-depth comparison between VESTA and various versions of the well-known and very popular Marching Cubes algorithm for the very first time. In an application section, we demonstrate the extraction of VESTA isosurfaces for various data sets ranging from computer tomographic scan data to simulation data of relativistic hydrodynamic fireball expansions.Comment: 24 pages, 33 figures, 4 tables, final versio

The TREC-2002 video track report

TREC-2002 saw the second running of the Video Track, the goal of which was to promote progress in content-based retrieval from digital video via open, metrics-based evaluation. The track used 73.3 hours of publicly available digital video (in MPEG-1/VCD format) downloaded by the participants directly from the Internet Archive (Prelinger Archives) (internetarchive, 2002) and some from the Open Video Project (Marchionini, 2001). The material comprised advertising, educational, industrial, and amateur films produced between the 1930's and the 1970's by corporations, nonprofit organizations, trade associations, community and interest groups, educational institutions, and individuals. 17 teams representing 5 companies and 12 universities - 4 from Asia, 9 from Europe, and 4 from the US - participated in one or more of three tasks in the 2001 video track: shot boundary determination, feature extraction, and search (manual or interactive). Results were scored by NIST using manually created truth data for shot boundary determination and manual assessment of feature extraction and search results. This paper is an introduction to, and an overview of, the track framework - the tasks, data, and measures - the approaches taken by the participating groups, the results, and issues regrading the evaluation. For detailed information about the approaches and results, the reader should see the various site reports in the final workshop proceedings

Incremental Art: A Neural Network System for Recognition by Incremental Feature Extraction

Abstract Incremental ART extends adaptive resonance theory (ART) by incorporating mechanisms for efficient recognition through incremental feature extraction. The system achieves efficient confident prediction through the controlled acquisition of only those features necessary to discriminate an input pattern. These capabilities are achieved through three modifications to the fuzzy ART system: (1) A partial feature vector complement coding rule extends fuzzy ART logic to allow recognition based on partial feature vectors. (2) The addition of a F2 decision criterion to measure ART predictive confidence. (3) An incremental feature extraction layer computes the next feature to extract based on a measure of predictive value. Our system is demonstrated on a face recognition problem but has general applicability as a machine vision solution and as model for studying scanning patterns.Office of Naval Research (N00014-92-J-4015, N00014-92-J-1309, N00014-91-4100); Air Force Office of Scientific Research (90-0083); National Science Foundation (IRI 90-00530

INFORMATION SECURITY RISK AND BOUNDARY CHANGING BEHAVIOR

The escalating information security threats and their impacts have made firms pay careful attention to potential risks they face and the actions they can take to mitigate such risks. We explore if and how the information security risk perceptions of firms shape their boundary-changing behaviors. We argue that organizations have risk transfer, risk avoidance, risk reduction, risk acceptance options, and combine these options in their attempts to reduce the perceived effects of information security risks. Organizations through risk transfer could transfer some effects of information security risks to third parties, while boundary changing behaviors could alter the potential vulnerabilities of a firm, and hence decisions to alter firm boundaries are likely to be shaped by risk perceptions. By fine-tuning 11 state-of-the-art NLP models with causal extraction, we find that organizations’ information security risk perception is positively associated with their information security risk transfer behavior, and less-risky boundary changing actions