@conference {18210,
	title = {Exploring compression effects for improved source camera identification using strongly compressed video},
	booktitle = {Image Processing (ICIP), 2011 18th IEEE International Conference on},
	year = {2011},
	month = {2011/09//},
	pages = {1953 - 1956},
	abstract = {This paper presents a study of the video compression effect on source camera identification based on the Photo-Response Non-Uniformity (PRNU). Specifically, the reliability of different types of frames in a compressed video is first investigated, which shows quantitatively that I-frames are more reliable than P-frames for PRNU estimation. Motivated by this observation, a new mechanism for estimating the reference PRNU and two mechanisms for estimating the test-video PRNU are proposed to achieve higher accuracy with fewer frames used. Experiments are performed to validate the effectiveness of the proposed mechanisms.},
	keywords = {camera, cameras;video, coding;, compression;computer, compression;video, estimation;photoresponse, forensics;data, identification;video, non-uniformity;reliability;source, PRNU},
	doi = {10.1109/ICIP.2011.6115855},
	author = {Chuang,Wei-Hong and Su,Hui and M. Wu}
}
@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 {13119,
	title = {Decentralized discovery of camera network topology},
	booktitle = {Distributed Smart Cameras, 2008. ICDSC 2008. Second ACM/IEEE International Conference on},
	year = {2008},
	month = {2008/09//},
	pages = {1 - 10},
	abstract = {One of the primary uses of camera networks is the observation and tracking of objects within some domain. Substantial research has gone into tracking objects within single and multiple views. However, few such approaches scale to large numbers of sensors, and those that do require an understanding of the network topology. Camera network topology models camera adjacency in the context of tracking: when an object/entity leaves one camera, which cameras could it appear at next? This paper presents a decentralized approach for estimating a camera networkpsilas topology based on sequential Bayesian estimation using a modified multinomial distribution. Central to this method is an information-theoretic appearance model for observation weighting. The distributed nature of the approach utilizes all of the sensors as processing agents in collectively recovering the network topology. Experimental results are presented using camera networks varying in size from 10-100 nodes.},
	keywords = {adjacency;camera, Bayesian, camera, discovery;modified, distribution;sequential, estimation;Bayes, methods;distributed, multinomial, network, sensors;, sensors;image, topology;decentralized},
	doi = {10.1109/ICDSC.2008.4635696},
	author = {Farrell,R. and Davis, Larry S.}
}
@article {18197,
	title = {Digital image forensics via intrinsic fingerprints},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {3},
	year = {2008},
	month = {2008/03//},
	pages = {101 - 117},
	abstract = {Digital imaging has experienced tremendous growth in recent decades, and digital camera images have been used in a growing number of applications. With such increasing popularity and the availability of low-cost image editing software, the integrity of digital image content can no longer be taken for granted. This paper introduces a new methodology for the forensic analysis of digital camera images. The proposed method is based on the observation that many processing operations, both inside and outside acquisition devices, leave distinct intrinsic traces on digital images, and these intrinsic fingerprints can be identified and employed to verify the integrity of digital data. The intrinsic fingerprints of the various in-camera processing operations can be estimated through a detailed imaging model and its component analysis. Further processing applied to the camera captured image is modelled as a manipulation filter, for which a blind deconvolution technique is applied to obtain a linear time-invariant approximation and to estimate the intrinsic fingerprints associated with these postcamera operations. The absence of camera-imposed fingerprints from a test image indicates that the test image is not a camera output and is possibly generated by other image production processes. Any change or inconsistencies among the estimated camera-imposed fingerprints, or the presence of new types of fingerprints suggest that the image has undergone some kind of processing after the initial capture, such as tampering or steganographic embedding. Through analysis and extensive experimental studies, this paper demonstrates the effectiveness of the proposed framework for nonintrusive digital image forensics.},
	keywords = {ACQUISITION, analysis;intrinsic, approximation;cameras;digital, camera, deconvolution;digital, devices;blind, fingerprints;time, forensics;forensic, identification;image, image, images;digital, invariant, photography;fingerprint, sensors;},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2007.916010},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
@conference {12546,
	title = {Stochastic fusion of multi-view gradient fields},
	booktitle = {Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on},
	year = {2008},
	month = {2008/10//},
	pages = {1324 - 1327},
	abstract = {Image gradients form powerful cues in a host of vision and graphics applications. In this paper, we consider multiple views of a textured planar scene and consider the problem of estimating the scene texture map using these multi-view inputs. Modeling each camera view as a projective transformation of the scene, we show that the problem is equivalent to that of studying the effect of noise (and the projective imaging) on the gradient fields induced by this texture map. We show that these noisy gradient fields can be modeled as complete observers of the scene radiance. Further, the corrupting noise can be shown to be additive and linear, although spatially varying. However, the specific form of the noise term can be exploited to design linear estimators that fuse the gradient fields obtained from each of the individual views. The fused gradient field forms a robust estimate of the scene gradients and can be used for scene reconstruction.},
	keywords = {application;brightness;cameras;gradient, application;image, camera, estimators;multiview, fields;projective, fusion;image, fusion;textured, gradient, gradients;linear, gradients;scene, imaging;scene, map;stochastic, methods;image, noise;graphics, planar, radiance;scene, reconstruction;image, reconstruction;scene, scene;vision, TEXTURE, texture;, view;corrupting},
	doi = {10.1109/ICIP.2008.4712007},
	author = {Sankaranarayanan,A. C and Chellapa, Rama}
}
@conference {13142,
	title = {Learning Higher-order Transition Models in Medium-scale Camera Networks},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We present a Bayesian framework for learning higher- order transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the network and have a greater predictive power for multi-camera tracking than camera adjacency alone. These models also provide inherent resilience to camera failure, filling in gaps left by single or even multiple non-adjacent camera failures. Our approach to estimating higher-order transition models relies on the accurate assignment of camera observations to the underlying trajectories of objects moving through the network. We addresses this data association problem by gathering the observations and evaluating alternative partitions of the observation set into individual object trajectories. Searching the complete partition space is intractable, so an incremental approach is taken, iteratively adding observations and pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model. When the algorithm has considered all observations, the most likely (MAP) partition is taken as the true object trajectories. From these recovered trajectories, the higher-order statistics we seek can be derived and employed for tracking. The partitioning algorithm we present is parallel in nature and can be readily extended to distributed computation in medium-scale smart camera networks.},
	keywords = {(artificial, approach;medium-scale, association, Bayesian, camera, cameras;video, framework;data, fusion;iterative, graphical, intelligence);optical, learning;incremental, likelihood;multicamera, likely, methods;higher, methods;learning, model, model;video, movement;probabilistic, network;Bayes, network;most, order, partition, problem;higher-order, statistics;higher-order, statistics;image, Surveillance, surveillance;, tracking;object, tracking;probability;video, transition},
	doi = {10.1109/ICCV.2007.4409203},
	author = {Farrell,R. and David Doermann and Davis, Larry S.}
}
@conference {13556,
	title = {Flattening curved documents in images},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {2},
	year = {2005},
	month = {2005/06//},
	pages = {338 - 345 vol. 2 - 338 - 345 vol. 2},
	abstract = {Compared to scanned images, document pictures captured by camera can suffer from distortions due to perspective and page warping. It is necessary to restore a frontal planar view of the page before other OCR techniques can be applied. In this paper we describe a novel approach for flattening a curved document in a single picture captured by an uncalibrated camera. To our knowledge this is the first reported method able to process general curved documents in images without camera calibration. We propose to model the page surface by a developable surface, and exploit the properties (parallelism and equal line spacing) of the printed textual content on the page to recover the surface shape. Experiments show that the output images are much more OCR friendly than the original ones. While our method is designed to work with any general developable surfaces, it can be adapted for typical special cases including planar pages, scans of thick books, and opened books.},
	keywords = {calibration;, camera, character, content;, curved, distortion;, document, document;, image, images;, OCR, optical, page, pictures;, printed, processing;, recognition;, restoration;, scanned, techniques;, textual, warping;},
	doi = {10.1109/CVPR.2005.163},
	author = {Liang,Jian and DeMenthon,D. and David Doermann}
}
@conference {12706,
	title = {3D model refinement using surface-parallax},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 285-8 vol.3 - iii - 285-8 vol.3},
	abstract = {We present an approach to update and refine coarse 3D models of urban environments from a sequence of intensity images using surface parallax. This generalizes the plane-parallax recovery methods to surface-parallax using arbitrary surfaces. A coarse and potentially incomplete depth map of the scene obtained from a digital elevation map (DEM) is used as a reference surface which is refined and updated using this approach. The reference depth map is used to estimate the camera motion and the motion of the 3D points on the reference surface is compensated. The resulting parallax, which is an epipolar field, is estimated using an adaptive windowing technique and used to obtain the refined depth map.},
	keywords = {3D, adaptive, arbitrary, camera, coarse, compensation;, Computer, DEM;, depth, digital, ELEVATION, environments;, epipolar, estimation;, field;, image, incomplete, INTENSITY, map;, model, MOTION, parallax;, plane-parallax, reconstruction;, recovery;, refinement;, sequence;, sequences;, surface, surfaces;, urban, vision;, windowing;},
	doi = {10.1109/ICASSP.2004.1326537},
	author = {Agrawala, Ashok K. and Chellapa, Rama}
}
@conference {13244,
	title = {Image-based pan-tilt camera control in a multi-camera surveillance environment},
	booktitle = {Multimedia and Expo, 2003. ICME {\textquoteright}03. Proceedings. 2003 International Conference on},
	volume = {1},
	year = {2003},
	month = {2003/07//},
	pages = {I - 645-8 vol.1 - I - 645-8 vol.1},
	abstract = {In automated surveillance systems with multiple cameras, the system must be able to position the cameras accurately. Each camera must be able to pan-tilt such that an object detected in the scene is in a vantage position in the camera{\textquoteright}s image plane and subsequently capture images of that object. Typically, camera calibration is required. We propose an approach that uses only image-based information. Each camera is assigned a pan-tilt zero-position. Position of an object detected in one camera is related to the other cameras by homographies between the zero-positions while different pan-tilt positions of the same camera are related in the form of projective rotations. We then derive that the trajectories in the image plane corresponding to these projective rotations are approximately circular for pan and linear for tilt. The camera control technique is subsequently tested in a working prototype.},
	keywords = {automated, camera, cameras;, control;, detection;, environment;, image, image-based, information;, multicamera, object, pan-tilt, position;, processing;, sensors;, Surveillance, surveillance;, systems;, vantage, zero-position;, zero-positions;},
	doi = {10.1109/ICME.2003.1221000},
	author = {Lim,Ser-Nam and Elgammal,A. and Davis, Larry S.}
}
@conference {12728,
	title = {Towards a view invariant gait recognition algorithm},
	booktitle = {Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.},
	year = {2003},
	month = {2003/07//},
	pages = {143 - 150},
	abstract = {Human gait is a spatio-temporal phenomenon and typifies the motion characteristics of an individual. The gait of a person is easily recognizable when extracted from a side-view of the person. Accordingly, gait-recognition algorithms work best when presented with images where the person walks parallel to the camera image plane. However, it is not realistic to expect this assumption to be valid in most real-life scenarios. Hence, it is important to develop methods whereby the side-view can be generated from any other arbitrary view in a simple, yet accurate, manner. This is the main theme of the paper. We show that if the person is far enough from the camera, it is possible to synthesize a side view (referred to as canonical view) from any other arbitrary view using a single camera. Two methods are proposed for doing this: (i) using the perspective projection model; (ii) using the optical flow based structure from motion equations. A simple camera calibration scheme for this method is also proposed. Examples of synthesized views are presented. Preliminary testing with gait recognition algorithms gives encouraging results. A by-product of this method is a simple algorithm for synthesizing novel views of a planar scene.},
	keywords = {(access, algorithm;, analysis;, Biometrics, biometrics;, Calibration, calibration;, camera, canonical, control);, equations;, flow;, Gait, gait;, human, image, invariant, model;, MOTION, optical, perspective, phenomenon;, projection, recognition, scheme;, sequences;, spatio-temporal, view, view;},
	doi = {10.1109/AVSS.2003.1217914},
	author = {Kale, A. and Chowdhury, A.K.R. and Chellapa, Rama}
}
@conference {12758,
	title = {Bayesian structure from motion using inertial information},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {III-425 - III-428 vol.3 - III-425 - III-428 vol.3},
	abstract = {A novel approach to Bayesian structure from motion (SfM) using inertial information and sequential importance sampling (SIS) is presented. The inertial information is obtained from camera-mounted inertial sensors and is used in the Bayesian SfM approach as prior knowledge of the camera motion in the sampling algorithm. Experimental results using both synthetic and real images show that, when inertial information is used, more accurate results can be obtained or the same estimation accuracy can be obtained at a lower cost.},
	keywords = {3D, analysis;, Bayes, Bayesian, camera, estimation;, image, images;, importance, inertial, information;, methods;, MOTION, motion;, parameter, processing;, real, reconstruction;, sampling;, scene, sensors;, sequence;, sequences;, sequential, signal, structure-from-motion;, synthetic, systems;, video},
	doi = {10.1109/ICIP.2002.1038996},
	author = {Qian,Gang and Chellapa, Rama and Qinfen Zheng}
}
@conference {16150,
	title = {SpaceTree: supporting exploration in large node link tree, design evolution and empirical evaluation},
	booktitle = {Information Visualization, 2002. INFOVIS 2002. IEEE Symposium on},
	year = {2002},
	month = {2002///},
	pages = {57 - 64},
	abstract = {We present a novel tree browser that builds on the conventional node link tree diagrams. It adds dynamic rescaling of branches of the tree to best fit the available screen space, optimized camera movement, and the use of preview icons summarizing the topology of the branches that cannot be expanded. In addition, it includes integrated search and filter functions. This paper reflects on the evolution of the design and highlights the principles that emerged from it. A controlled experiment showed benefits for navigation to already previously visited nodes and estimation of overall tree topology.},
	keywords = {browser;, camera, data, design, diagrams;, dynamic, evolution;, experiment;, exploration;, filter, functions;, graphical, icons;, integrated, interfaces;, large, link, movement;, node, novel, optimized, rescaling;, search;, SpaceTree;, structures;, topology;, tree, user, visualisation;, visualization;},
	doi = {10.1109/INFVIS.2002.1173148},
	author = {Plaisant, Catherine and Grosjean,J. and Bederson, Benjamin B.}
}
