@article {18183,
	title = {Component forensics},
	journal = {Signal Processing Magazine, IEEE},
	volume = {26},
	year = {2009},
	month = {2009/03//},
	pages = {38 - 48},
	abstract = {Visual sensor technologies have experienced tremendous growth in recent decades, and digital devices are becoming ubiquitous. Digital images taken by various imaging devices have been used in a growing number of applications, from military and reconnaissance to medical diagnosis and consumer photography. Consequently, a series of new forensic issues arise amidst such rapid advancement and widespread adoption of imaging technologies. For example, one can readily ask what kinds of hardware and software components as well as their parameters have been employed inside these devices? Given a digital image, which imaging sensor or which brand of sensor was used to acquire the image? How was the image acquired? Was it captured using a digital camera, cell phone camera, image scanner, or was it created artificially using an imageediting software? Has the image undergone any manipulation after capture? Is it authentic, or has it been tampered in any way? Does it contain any hidden information or steganographic data? Many of these forensic questions are related to tracing the origin of the digital image to its creation process. Evidence obtained from such analysis would provide useful forensic information to law enforcement, security, and intelligence agencies. Knowledge of image acquisition techniques can also help answer further forensic questions regarding the nature of additional processing that the image might have undergone after capture.},
	keywords = {ACQUISITION, component, data;, forensics;digital, image, of, processing;image, sensor, sensors;security, technique;visual, technology;image},
	isbn = {1053-5888},
	doi = {10.1109/MSP.2008.931076},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
@conference {12512,
	title = {Recognition of quantized still face images},
	booktitle = {Biometrics: Theory, Applications, and Systems, 2009. BTAS {\textquoteright}09. IEEE 3rd International Conference on},
	year = {2009},
	month = {2009/09//},
	pages = {1 - 6},
	abstract = {In applications such as document understanding, only binary face images may be available as inputs to a face recognition (FR) algorithm. In this paper, we investigate the effects of the number of grey levels on PCA, multiple exemplar discriminant analysis (MEDA) and the elastic bunch graph matching (EBGM) FR algorithms. The inputs to these FR algorithms are quantized images (binary images or images with small number of grey levels) modified by distance and Box-Cox transforms. The performances of PCA and MEDA algorithms are at 87.66\% for images in FRGC version 1 experiment 1 after they are thresholded and transformed while the EBGM algorithm achieves only 37.5\%. In many document understanding applications, it is also required to verify a degraded low-quality image against a high-quality image, both of which are from the same source. For this problem, the performances of PCA and MEDA are stable when the images were degraded by noise, downsampling or different thresholding parameters.},
	keywords = {(signal);, algorithm;grey, algorithms;face, analysis;quantisation, analysis;quantized, Box-Cox, bunch, component, discriminant, exemplar, Face, FR, graph, images;distance, images;face, levels;multiple, MATCHING, recognition, recognition;principal, transforms;document, transforms;PCA;binary, understanding;elastic},
	doi = {10.1109/BTAS.2009.5339030},
	author = {Tao Wu and Chellapa, Rama}
}
@conference {13124,
	title = {Human detection using iterative feature selection and logistic principal component analysis},
	booktitle = {Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/05//},
	pages = {1691 - 1697},
	abstract = {We present a fast feature selection algorithm suitable for object detection applications where the image being tested must be scanned repeatedly to detected the object of interest at different locations and scales. The algorithm iteratively estimates the belongness probability of image pixels to foreground of the image. To prove the validity of the algorithm, we apply it to a human detection problem. The edge map is filtered using a feature selection algorithm. The filtered edge map is then projected onto an eigen space of human shapes to determine if the image contains a human. Since the edge maps are binary in nature, Logistic Principal Component Analysis is used to obtain the eigen human shape space. Experimental results illustrate the accuracy of the human detector.},
	keywords = {algorithm;logistic, analysis;edge, analysis;probability;, applications;principal, belongness, component, DETECTION, detection;feature, detection;iterative, detection;principal, extraction;filtering, feature, filtering;human, MAP, methods;object, PCA;object, probability;edge, selection, theory;iterative},
	doi = {10.1109/ROBOT.2008.4543444},
	author = {Abd-Almageed, Wael and Davis, Larry S.}
}
@conference {18155,
	title = {A pattern classification framework for theoretical analysis of component forensics},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {1665 - 1668},
	abstract = {Component forensics is an emerging methodology for forensic analysis that aims at estimating the algorithms and parameters in each component of a digital device. This paper proposes a theoretical foundation to examine the performance limits of component forensics. Using ideas from pattern classification theory, we define formal notions of identifiability of components in the information processing chain. We show that the parameters of certain device components can be accurately identified only in controlled settings through semi non-intrusive forensics, while the parameters of some others can be computed directly from the available sample data via complete non-intrusive analysis. We then extend the proposed theoretical framework to quantify and improve the accuracies and confidence in component parameter identification for several forensic applications.},
	keywords = {analysis;information, classification;, classification;parameter, component, estimation;pattern, forensics;component, forensics;pattern, identification;forensic, parameter, processing;nonintrusive},
	doi = {10.1109/ICASSP.2008.4517947},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
@conference {18150,
	title = {A Component Estimation Framework for Information Forensics},
	booktitle = {Multimedia Signal Processing, 2007. MMSP 2007. IEEE 9th Workshop on},
	year = {2007},
	month = {2007/10//},
	pages = {397 - 400},
	abstract = {With a rapid growth of imaging technologies and an increasingly widespread usage of digital images and videos for a large number of high security and forensic applications, there is a strong need for techniques to verify the source and integrity of digital data. Component forensics is new approach for forensic analysis that aims to estimate the algorithms and parameters in each component of the digital device. In this paper, we develop a novel theoretical foundation to understand the fundamental performance limits of component forensics. We define formal notions of identifiability of components in the information processing chain, and present methods to quantify the accuracies at which the component parameters can be estimated. Building upon the proposed theoretical framework, we devise methods to improve the accuracies of component parameter estimation for a wide range of forensic applications.},
	keywords = {component, data, data;, Estimation, estimation;image, forensics;parameter, framework;digital, integrity;imaging, of, processing;security, technologies;information},
	doi = {10.1109/MMSP.2007.4412900},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
@article {18257,
	title = {Nonintrusive component forensics of visual sensors using output images},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {2},
	year = {2007},
	month = {2007/03//},
	pages = {91 - 106},
	abstract = {Rapid technology development and the widespread use of visual sensors have led to a number of new problems related to protecting intellectual property rights, handling patent infringements, authenticating acquisition sources, and identifying content manipulations. This paper introduces nonintrusive component forensics as a new methodology for the forensic analysis of visual sensing information, aiming to identify the algorithms and parameters employed inside various processing modules of a digital device by only using the device output data without breaking the device apart. We propose techniques to estimate the algorithms and parameters employed by important camera components, such as color filter array and color interpolation modules. The estimated interpolation coefficients provide useful features to construct an efficient camera identifier to determine the brand and model from which an image was captured. The results obtained from such component analysis are also useful to examine the similarities between the technologies employed by different camera models to identify potential infringement/licensing and to facilitate studies on technology evolution},
	keywords = {ACQUISITION, array;color, authenticating, component, filter, forensics;patent, infringements;visual, Interpolation, manipulations;intellectual, modules;content, property, property;, protection;nonintrusive, rights, sensors;image, sensors;industrial, sources;color},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2006.890307},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
@article {12586,
	title = {Super-Resolution of Face Images Using Kernel PCA-Based Prior},
	journal = {Multimedia, IEEE Transactions on},
	volume = {9},
	year = {2007},
	month = {2007/06//},
	pages = {888 - 892},
	abstract = {We present a learning-based method to super-resolve face images using a kernel principal component analysis-based prior model. A prior probability is formulated based on the energy lying outside the span of principal components identified in a higher-dimensional feature space. This is used to regularize the reconstruction of the high-resolution image. We demonstrate with experiments that including higher-order correlations results in significant improvements},
	keywords = {analysis;learning-based, analysis;probability;, component, Face, image, method;prior, model;face, principal, probability, recognition;image, reconstruction;image, reconstruction;kernel, resolution;principal, super-resolution;high-resolution},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2007.893346},
	author = {Chakrabarti,Ayan and Rajagopalan, AN and Chellapa, Rama}
}
@conference {15600,
	title = {A new approach to image fusion based on cokriging},
	booktitle = {Information Fusion, 2005 8th International Conference on},
	volume = {1},
	year = {2005},
	month = {2005/07//},
	pages = {8 pp. - 8 pp.},
	abstract = {We consider the image fusion problem involving remotely sensed data. We introduce cokriging as a method to perform fusion. We investigate the advantages of fusing Hyperion with ALI. This evaluation is performed by comparing the classification of the fused data with that of input images and by calculating well-chosen quantitative fusion quality metrics. We consider the invasive species forecasting system (ISFS) project as our fusion application. The fusion of ALI with Hyperion data is studied using PCA and wavelet-based fusion. We then propose utilizing a geostatistical based interpolation method called cokriging as a new approach for image fusion.},
	keywords = {ALI;, analysis;, based, cokriging;, component, data;, forecasting, fusion, fusion;, geophysical, geostatistical, Hyperion, image, Interpolation, interpolation;, invasive, ISFS, method;, metrics;, PCA;, principal, processing;, project;, QUALITY, quantitative, remote, remotely, sensed, sensing;, sensor, sensors;, signal, species, system;, techniques;, transforms;, wavelet, wavelet-based},
	doi = {10.1109/ICIF.2005.1591912},
	author = {Memarsadeghi,N. and Le Moigne,J. and Mount, Dave and Morisette,J.}
}
@conference {13193,
	title = {Robust observations for object tracking},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/09//},
	pages = {II - 442-5 - II - 442-5},
	abstract = {It is a difficult task to find an observation model that will perform well for long-term visual tracking. In this paper, we propose an adaptive observation enhancement technique based on likelihood images, which are derived from multiple visual features. The most discriminative likelihood image is extracted by principal component analysis (PCA) and incrementally updated frame by frame to reduce temporal tracking error. In the particle filter framework, the feasibility of each sample is computed using this most discriminative likelihood image before the observation process. Integral image is employed for efficient computation of the feasibility of each sample. We illustrate how our enhancement technique contributes to more robust observations through demonstrations.},
	keywords = {(numerical, adaptive, analysis;, component, enhancement;, filter, Filtering, framework;, image, images;, likelihood, methods);, object, observation, particle, PCA;, principal, tracking;},
	doi = {10.1109/ICIP.2005.1530087},
	author = {Han,Bohyung and Davis, Larry S.}
}
@conference {12693,
	title = {Classification probability analysis of principal component space analysis},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {240 - 243 Vol.1 - 240 - 243 Vol.1},
	abstract = {In a previous paper, we have presented a new linear classification algorithm, principal component space analysis (PCNSA) which is designed for problems like object recognition where different classes have unequal and non-white noise covariance matrices. PCNSA first obtains a principal components space (PCA space) for the entire data and in this PCA space, it finds for each class "i", an M<sub>i</sub> dimensional subspace along which the class{\textquoteright}s intra-class variance is the smallest. We call this subspace an approximate space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class{\textquoteright}s mean in the class{\textquoteright}s ANS is a minimum. In this paper, we discuss the PCNSA algorithm more precisely and derive tight upper bounds on its classification error probability. We use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA).},
	keywords = {algorithm;, analysis;, approximate, classification, classification;, component, covariance, discriminant, error, intraclass, linear, matrices;, matrix;, noise;, nonwhite, object, pattern, PCA;, principal, probability;, recognition;, space, space;, statistics;, subspace, variance;},
	doi = {10.1109/ICPR.2004.1334068},
	author = {Vaswani, N. and Chellapa, Rama}
}
@conference {12700,
	title = {Intra-personal kernel space for face recognition},
	booktitle = {Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on},
	year = {2004},
	month = {2004/05//},
	pages = {235 - 240},
	abstract = {Intra-personal space modeling proposed by Moghaddam et al. has been successfully applied in face recognition. In their work the regular principal subspaces are derived from the intra-personal spacce using a principal componen analysis and embedded in a probabilistic formulation. In this paper, we derive the principal subspace from the intro-personal kernel space by developing a probabilistic analysis for kernel principal components for face recognition. We test this algorithm on a subset of the FERET database with illumination and facial expression variations. The recognition performance demonstrates its advantage over other traditional subspace approaches.},
	keywords = {analysis;, component, Expression, Face, facial, illumination, intra-personal, Kernel, lighting;, principal, probabilistic, probability;, recognition;, space;, variation;},
	doi = {10.1109/AFGR.2004.1301537},
	author = {Zhou,Shaohua and Chellapa, Rama and Moghaddam, B.}
}
@conference {13214,
	title = {Object tracking by adaptive feature extraction},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/10//},
	pages = {1501 - 1504 Vol. 3 - 1501 - 1504 Vol. 3},
	abstract = {Tracking objects in the high-dimensional feature space is not only computationally expensive but also functionally inefficient. Selecting a low-dimensional discriminative feature set is a critical step to improve tracker performance. A good feature set for tracking can differ from frame to frame due to the changes in the background against the tracked object, and due to an on-line algorithm that adaptively determines a advantageous distinctive feature set. In this paper, multiple heterogeneous features are assembled, and likelihood images are constructed for various subspaces of the combined feature space. Then, the most discriminative feature is extracted by principal component analysis (PCA) based on those likelihood images. This idea is applied to the mean-shift tracking algorithm [D. Comaniciu et al., June 2000], and we demonstrate its effectiveness through various experiments.},
	keywords = {adaptive, algorithm;, analysis;, colour, component, extraction;, feature, feature;, heterogeneous, image, image;, likelihood, Mean-shift, object, online, principal, tracking, tracking;},
	doi = {10.1109/ICIP.2004.1421349},
	author = {Han,Bohyung and Davis, Larry S.}
}
@conference {12763,
	title = {An experimental evaluation of linear and kernel-based methods for face recognition},
	booktitle = {Applications of Computer Vision, 2002. (WACV 2002). Proceedings. Sixth IEEE Workshop on},
	year = {2002},
	month = {2002///},
	pages = {13 - 18},
	abstract = {In this paper we present the results of a comparative study of linear and kernel-based methods for face recognition. The methods used for dimensionality reduction are Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Linear Discriminant Analysis (LDA) and Kernel Discriminant Analysis (KDA). The methods used for classification are Nearest Neighbor (NN) and Support Vector Machine (SVM). In addition, these classification methods are applied on raw images to gauge the performance of these dimensionality reduction techniques. All experiments have been performed on images from UMIST Face Database.},
	keywords = {analysis;, classification;, component, discriminant, Face, image, Kernel, linear, Machine;, nearest, neighbor;, principal, recognition;, Support, vector},
	doi = {10.1109/ACV.2002.1182137},
	author = {Gupta, H. and Agrawala, Ashok K. and Pruthi, T. and Shekhar, C. and Chellapa, Rama}
}
@conference {13681,
	title = {The role of compressed document images in transmission and retrieval},
	booktitle = {Multimedia Signal Processing, 1997., IEEE First Workshop on},
	year = {1997},
	month = {1997/06//},
	pages = {331 - 336},
	abstract = {Document images belong to a unique class of images where the information content is contained in the language represented by a series of symbols on the page, rather than in the visual objects themselves. For this reason, it is essential to preserve the fidelity of individual components when considering methods of compression. Likewise the component level structure should be a prime consideration when ordering information for lossy or progressive transmission. We refine our work on document image compression as it applies to transmission and retrieval. We first overview the basic compression scheme, then describe a structural hierarchy which provides desirable properties for transmission. We present the results of a rate distortion experiment and discuss the implications for network applications},
	keywords = {applications;progressive, coding;multimedia, component, compression;document, compression;image, computing;query, content;lossy, databases;, distortion, experiment;structural, hierarchy;symbols;visual, image, level, objects;data, processing;image, processing;visual, retrieval;image, structure;document, transmission;information, transmission;multimedia;network, transmission;rate},
	doi = {10.1109/MMSP.1997.602657},
	author = {Kia,O. and David Doermann}
}
@conference {13571,
	title = {Image based typographic analysis of documents},
	booktitle = {Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on},
	year = {1993},
	month = {1993/10//},
	pages = {769 - 773},
	abstract = {An approach to image based typographic analysis of documents is provided. The problem requires a spatial understanding of the document layout as well as knowledge of the proper syntax. The system performs a page synthesis from the stream of formatting commands defined in a DVI file. Since the two-dimensional relationships between document components are not explicit in the page language, the authors develop a representation which preserves the two-dimensional layout, the read-order and the attributes of document components. From this hierarchical representation of the page layout we extract and analyze relevant typographic features such as margins, line and character spacing, and figure placement},
	keywords = {2D, analysis;, attributes;, based, character, commands;, component, data, description, document, DVI, extraction;, feature, figure, file;, formatting, hierarchical, image, language;, languages;, layout;, line, margins;, page, placement;, processing;, read-order;, relationships;, representation;, spacing;, spatial, structures;, syntax;, synthesis;, typographic, understanding;},
	doi = {10.1109/ICDAR.1993.395624},
	author = {David Doermann and Furuta,R.}
}
