@conference {18282,
	title = {Semi non-intrusive training for cell-phone camera model linkage},
	booktitle = {Information Forensics and Security (WIFS), 2010 IEEE International Workshop on},
	year = {2010},
	month = {2010/12//},
	pages = {1 - 6},
	abstract = {This paper presents a study of cell-phone camera model linkage that matches digital images against potential makes / models of cell-phone camera sources using camera color interpolation features. The matching performance is examined and the dependency on the content of training image collection is evaluated via variance analysis. Training content dependency can be dealt with under the framework of component forensics, where cell-phone camera model linkage is seen as a combination of semi non-intrusive training and completely non-intrusive testing. Such a viewpoint suggests explicitly the goodness criterion of testing accuracy for training data selection. It also motivates other possible alternative training procedures based on different criteria, such as the training complexity, for which preliminary but promising experiment designs and results have been obtained.},
	keywords = {accuracy;training, analysis;cameras;cellular, analysis;image, camera, Color, colour, complexity;training, content, dependency;variance, feature;cell, forensics;digital, forensics;image, image, Interpolation, linkage;component, matching;interpolation;, matching;semi, model, nonintrusive, phone, radio;computer, training;testing},
	doi = {10.1109/WIFS.2010.5711468},
	author = {Chuang,Wei-Hong and M. Wu}
}
@conference {15544,
	title = {Efficient Kriging via Fast Matrix-Vector Products},
	booktitle = {Aerospace Conference, 2008 IEEE},
	year = {2008},
	month = {2008/03//},
	pages = {1 - 7},
	abstract = {Interpolating scattered data points is a problem of wide ranging interest. Ordinary kriging is an optimal scattered data estimator, widely used in geosciences and remote sensing. A generalized version of this technique, called cokriging, can be used for image fusion of remotely sensed data. However, it is computationally very expensive for large data sets. We demonstrate the time efficiency and accuracy of approximating ordinary kriging through the use of fast matrix-vector products combined with iterative methods. We used methods based on the fast Multipole methods and nearest neighbor searching techniques for implementations of the fast matrix-vector products.},
	keywords = {cokriging technique, fast matrix-vector products, fast multipole methods, geophysical techniques, image fusion, Interpolation, iterative methods, nearest neighbor searching, optimal scattered data estimator, Remote sensing, remotely sensed data, scattered data points, sensor fusion, time efficiency},
	doi = {10.1109/AERO.2008.4526433},
	author = {Memarsadeghi,N. and Raykar,V.C. and Duraiswami, Ramani and Mount, Dave}
}
@conference {18224,
	title = {Image acquisition forensics: Forensic analysis to identify imaging source},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {1657 - 1660},
	abstract = {With widespread availability of digital images and easy-to-use image editing softwares, the origin and integrity of digital images has become a serious concern. This paper introduces the problem of image acquisition forensics and proposes a fusion of a set of signal processing features to identify the source of digital images. Our results show that the devices{\textquoteright} color interpolation coefficients and noise statistics can jointly serve as good forensic features to help accurately trace the origin of the input image to its production process and to differentiate between images produced by cameras, cell phone cameras, scanners, and computer graphics. Further, the proposed features can also be extended to determining the brand and model of the device. Thus, the techniques introduced in this work provide a unified framework for image acquisition forensics.},
	keywords = {ACQUISITION, acquisition;image, analysis;, analysis;image, analysis;interpolation;statistical, cameras;color, cell, coefficients;computer, colour, editing, forensics;image, graphics;digital, identification;noise, images;forensic, Interpolation, phone, processing;data, softwares;imaging, source, statistics;scanners;signal},
	doi = {10.1109/ICASSP.2008.4517945},
	author = {McKay,C. and Swaminathan,A. and Gou,Hongmei and M. Wu}
}
@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}
}
@conference {12611,
	title = {Invariant Geometric Representation of 3D Point Clouds for Registration and Matching},
	booktitle = {Image Processing, 2006 IEEE International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {1209 - 1212},
	abstract = {Though implicit representations of surfaces have often been used for various computer graphics tasks like modeling and morphing of objects, it has rarely been used for registration and matching of 3D point clouds. Unlike in graphics, where the goal is precise reconstruction, we use isosurfaces to derive a smooth and approximate representation of the underlying point cloud which helps in generalization. Implicit surfaces are generated using a variational interpolation technique. Implicit function values on a set of concentric spheres around the 3D point cloud of object are used as features for matching. Geometric-invariance is achieved by decomposing implicit values based feature set into various spherical harmonics. The decomposition provides a compact representation of 3D point clouds while achieving rotation invariance},
	keywords = {3D, cloud;computer, function, geometric, graphics;geophysical, graphics;image, Interpolation, matching;image, point, processing;image, reconstruction;image, registration;image, registration;implicit, representation;interpolation;, representation;variational, signal, technique;clouds;computer, value;invariant},
	doi = {10.1109/ICIP.2006.312542},
	author = {Biswas,S. and Aggarwal,G. 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.}
}
@article {18459,
	title = {Rendering localized spatial audio in a virtual auditory space},
	journal = {IEEE Transactions on Multimedia},
	volume = {6},
	year = {2004},
	month = {2004/08//},
	pages = {553 - 564},
	abstract = {High-quality virtual audio scene rendering is required for emerging virtual and augmented reality applications, perceptual user interfaces, and sonification of data. We describe algorithms for creation of virtual auditory spaces by rendering cues that arise from anatomical scattering, environmental scattering, and dynamical effects. We use a novel way of personalizing the head related transfer functions (HRTFs) from a database, based on anatomical measurements. Details of algorithms for HRTF interpolation, room impulse response creation, HRTF selection from a database, and audio scene presentation are presented. Our system runs in real time on an office PC without specialized DSP hardware.},
	keywords = {-D audio processing, 3-D audio processing, Audio databases, audio signal processing, audio user interfaces, augmented reality, data sonification, Digital signal processing, head related transfer functions, head-related transfer function, Interpolation, Layout, perceptual user interfaces, Real time systems, Rendering (computer graphics), Scattering, spatial audio, Transfer functions, User interfaces, virtual audio scene rendering, virtual auditory spaces, virtual environments, Virtual reality, virtual reality environments},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2004.827516},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani and Davis, Larry S.}
}
