@conference {12484,
	title = {Group motion segmentation using a Spatio-Temporal Driving Force Model},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on},
	year = {2010},
	month = {2010/06//},
	pages = {2038 - 2045},
	abstract = {We consider the {\textquoteleft}group motion segmentation{\textquoteright} problem and provide a solution for it. The group motion segmentation problem aims at analyzing motion trajectories of multiple objects in video and finding among them the ones involved in a {\textquoteleft}group motion pattern{\textquoteright}. This problem is motivated by and serves as the basis for the {\textquoteleft}multi-object activity recognition{\textquoteright} problem, which is currently an active research topic in event analysis and activity recognition. Specifically, we learn a Spatio-Temporal Driving Force Model to characterize a group motion pattern and design an approach for segmenting the group motion. We illustrate the approach using videos of American football plays, where we identify the offensive players, who follow an offensive motion pattern, from motions of all players in the field. Experiments using GaTech Football Play Dataset validate the effectiveness of the segmentation algorithm.},
	keywords = {activity, American, analysis;image, dataset;group, driving, football, force, model;image, MOTION, pattern;group, play, plays;GaTech, problem;spatiotemporal, recognition, recognition;image, segmentation;, segmentation;motion, trajectories;multiobject},
	doi = {10.1109/CVPR.2010.5539880},
	author = {Ruonan Li and Chellapa, Rama}
}
@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}
}
@article {18230,
	title = {Intrinsic Sensor Noise Features for Forensic Analysis on Scanners and Scanned Images},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {4},
	year = {2009},
	month = {2009/09//},
	pages = {476 - 491},
	abstract = {A large portion of digital images available today are acquired using digital cameras or scanners. While cameras provide digital reproduction of natural scenes, scanners are often used to capture hard-copy art in a more controlled environment. In this paper, new techniques for nonintrusive scanner forensics that utilize intrinsic sensor noise features are proposed to verify the source and integrity of digital scanned images. Scanning noise is analyzed from several aspects using only scanned image samples, including through image denoising, wavelet analysis, and neighborhood prediction, and then obtain statistical features from each characterization. Based on the proposed statistical features of scanning noise, a robust scanner identifier is constructed to determine the model/brand of the scanner used to capture a scanned image. Utilizing these noise features, we extend the scope of acquisition forensics to differentiating scanned images from camera-taken photographs and computer-generated graphics. The proposed noise features also enable tampering forensics to detect postprocessing operations on scanned images. Experimental results are presented to demonstrate the effectiveness of employing the proposed noise features for performing various forensic analysis on scanners and scanned images.},
	keywords = {analysis;image, camera-taken, cameras;digital, denoising;intrinsic, denoising;wavelet, features;natural, forensics;scanned, graphics;digital, identifier;wavelet, images;digital, images;scanner, NOISE, photographs;computer-generated, prediction;nonintrusive, reproduction;forensic, scanner, scenes;neighborhood, sensor, transforms;},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2009.2026458},
	author = {Gou,Hongmei and Swaminathan,A. and M. Wu}
}
@conference {13121,
	title = {Action recognition using ballistic dynamics},
	booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {We present a Bayesian framework for action recognition through ballistic dynamics. Psycho-kinesiological studies indicate that ballistic movements form the natural units for human movement planning. The framework leads to an efficient and robust algorithm for temporally segmenting videos into atomic movements. Individual movements are annotated with person-centric morphological labels called ballistic verbs. This is tested on a dataset of interactive movements, achieving high recognition rates. The approach is also applied on a gesture recognition task, improving a previously reported recognition rate from 84\% to 92\%. Consideration of ballistic dynamics enhances the performance of the popular Motion History Image feature. We also illustrate the approachpsilas general utility on real-world videos. Experiments indicate that the method is robust to view, style and appearance variations.},
	keywords = {analysis;image, Bayesian, dynamics;gesture, feature;person-centric, framework;action, History, image, labels;psycho-kinesiological, morphological, MOTION, Movement, movements;motion, planning;interactive, processing;, recognition, recognition;ballistic, recognition;image, segmentation;video, signal, studies;image, task;human},
	doi = {10.1109/CVPR.2008.4587806},
	author = {Vitaladevuni,S.N. and Kellokumpu,V. and Davis, Larry S.}
}
@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}
}
@conference {13117,
	title = {Multi-resolution Tracking in Space and Time},
	booktitle = {Computer Vision, Graphics Image Processing, 2008. ICVGIP {\textquoteright}08. Sixth Indian Conference on},
	year = {2008},
	month = {2008/12//},
	pages = {352 - 358},
	abstract = {This paper proposes efficient and robust methods for tracking a moving object at multiple spatial and temporal resolution levels. The efficiency comes from optimising the amounts of spatial and temporal data processed. The robustness results from multi-level coarse-to-fine state-space searching. Tracking across resolution levels incurs an accuracy-versus-speed trade-off. For example, tracking at higher resolutions incurs greater processing cost, while maintaining higher accuracy in estimating the position of the moving object. We propose a novel spatial multi-scale tracker that tracks at the optimal accuracy-versus-speed operating point. Next, we relax this requirement to propose a multi-resolution tracker that operates at a minimum acceptable performance level. Finally, we extend these ideas to a multi-resolution spatio-temporal tracker. We show results of extensive experimentation in support of the proposed approaches.},
	keywords = {analysis;image, coarse-to-fine, detection;, estimation;spatial, MOTION, moving, multiscale, object, resolution;object, searching;multiresolution, state-space, tracker;image, tracker;spatio-temporal, tracking;multilevel, tracking;position},
	doi = {10.1109/ICVGIP.2008.74},
	author = {Roy,S.D. and Tran,S.D. and Davis, Larry S. and Vikram,B.S.}
}
@article {12545,
	title = {Object Detection, Tracking and Recognition for Multiple Smart Cameras},
	journal = {Proceedings of the IEEE},
	volume = {96},
	year = {2008},
	month = {2008/10//},
	pages = {1606 - 1624},
	abstract = {Video cameras are among the most commonly used sensors in a large number of applications, ranging from surveillance to smart rooms for videoconferencing. There is a need to develop algorithms for tasks such as detection, tracking, and recognition of objects, specifically using distributed networks of cameras. The projective nature of imaging sensors provides ample challenges for data association across cameras. We first discuss the nature of these challenges in the context of visual sensor networks. Then, we show how real-world constraints can be favorably exploited in order to tackle these challenges. Examples of real-world constraints are (a) the presence of a world plane, (b) the presence of a three-dimiensional scene model, (c) consistency of motion across cameras, and (d) color and texture properties. In this regard, the main focus of this paper is towards highlighting the efficient use of the geometric constraints induced by the imaging devices to derive distributed algorithms for target detection, tracking, and recognition. Our discussions are supported by several examples drawn from real applications. Lastly, we also describe several potential research problems that remain to be addressed.},
	keywords = {algorithm;geometric, analysis;image, association;distributed, camera;visual, cameras;, cameras;object, colour, constraints;imaging, data, detection;object, detection;sensor, detection;three-dimiensional, fusion;target, model;video, network;distributed, recognition;object, scene, sensor, sensor;multiple, sensors;geometry;image, sensors;object, smart, texture;intelligent, tracking;target, tracking;video},
	isbn = {0018-9219},
	doi = {10.1109/JPROC.2008.928758},
	author = {Sankaranarayanan,A. C and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {12585,
	title = {Human Identification using Gait and Face},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 2},
	abstract = {In general the visual-hull approach for performing integrated face and gait recognition requires at least two cameras. In this paper we present experimental results for fusion of face and gait for the single camera case. We considered the NIST database which contains outdoor face and gait data for 30 subjects. In the NIST database, subjects walk along an inverted Sigma pattern. In (A. Kale, et al., 2003), we presented a view-invariant gait recognition algorithm for the single camera case along with some experimental evaluations. In this chapter we present the results of our view-invariant gait recognition algorithm in (A. Kale, et al., 2003) on the NIST database. The algorithm is based on the planar approximation of the person which is valid when the person walks far away from the camera. In (S. Zhou et al., 2003), an algorithm for probabilistic recognition of human faces from video was proposed and the results were demonstrated on the NIST database. Details of these methods can be found in the respective papers. We give an outline of the fusion strategy here.},
	keywords = {algorithm;visual-hull, analysis;image, approach;cameras;face, approximation;probabilistic, database;camera;face, databases;, fusion;face, fusion;human, fusion;probability;video, Gait, identification;planar, NIST, processing;visual, recognition, recognition;gait, recognition;view-invariant, signal},
	doi = {10.1109/CVPR.2007.383523},
	author = {Chellapa, Rama and Roy-Chowdhury, A.K. and Kale, A.}
}
@conference {12592,
	title = {Markerless Monocular Tracking of Articulated Human Motion},
	booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on},
	volume = {1},
	year = {2007},
	month = {2007/04//},
	pages = {I-693 -I-696 - I-693 -I-696},
	abstract = {This paper presents a method for tracking general 3D general articulated human motion using a single camera with unknown calibration data. No markers, special clothes, or devices are assumed to be attached to the subject. In addition, both the camera and the subject are allowed to move freely, so that long-term view-independent human motion tracking and recognition are possible. We exploit the fact that the anatomical structure of the human body can be approximated by an articulated blob model. The optical flow under scaled orthographic projection is used to relate the spatial-temporal intensity change of the image sequence to the human motion parameters. These motion parameters are obtained by solving a set of linear equations to achieve global optimization. The correctness and robustness of the proposed method are demonstrated using Tai Chi sequences},
	keywords = {analysis;image, blob, camera;spatial-temporal, Chi, equations;markerless, flow;scaled, human, intensity;cameras;gait, model;articulated, monocular, MOTION, motion;global, optimization;human, orthographic, projection;single, recognition;image, sequence;linear, sequences;anatomical, sequences;optimisation;, structure;articulated, Tai, tracking;optical},
	doi = {10.1109/ICASSP.2007.366002},
	author = {Liu,Haiying and Chellapa, Rama}
}
@conference {18255,
	title = {Noise Features for Image Tampering Detection and Steganalysis},
	booktitle = {Image Processing, 2007. ICIP 2007. IEEE International Conference on},
	volume = {6},
	year = {2007},
	month = {2007/10/16/19},
	pages = {VI -97 -VI -100 - VI -97 -VI -100},
	abstract = {With increasing availability of low-cost image editing softwares, the authenticity of digital images can no longer be taken for granted. Digital images have also been used as cover data for transmitting secret information in the field of steganography. In this paper, we introduce a new set of features for multimedia forensics to determine if a digital image is an authentic camera output or if it has been tampered or embedded with hidden data. We perform such image forensic analysis employing three sets of statistical noise features, including those from denoising operations, wavelet analysis, and neighborhood prediction. Our experimental results demonstrate that the proposed method can effectively distinguish digital images from their tampered or stego versions.},
	keywords = {analysis;cryptography;data, analysis;image, analysis;wavelet, authenticity;hidden, computing;statistical, data;image, denoising, denoising;multimedia, detection;low-cost, editing, encapsulation;feature, extraction;image, features;steganalysis;wavelet, forensic, forensics;neighborhood, image, NOISE, operations;digital, prediction;statistical, softwares;multimedia, tampering, transforms;},
	doi = {10.1109/ICIP.2007.4379530},
	author = {Gou,Hongmei and Swaminathan,A. and M. Wu}
}
@conference {13148,
	title = {Objects in Action: An Approach for Combining Action Understanding and Object Perception},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {Analysis of videos of human-object interactions involves understanding human movements, locating and recognizing objects and observing the effects of human movements on those objects. While each of these can be conducted independently, recognition improves when interactions between these elements are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which unifies the inference processes involved in object classification and localization, action understanding and perception of object reaction. Traditional approaches for object classification and action understanding have relied on shape features and movement analysis respectively. By placing object classification and localization in a video interpretation framework, we can localize and classify objects which are either hard to localize due to clutter or hard to recognize due to lack of discriminative features. Similarly, by applying context on human movements from the objects on which these movements impinge and the effects of these movements, we can segment and recognize actions which are either too subtle to perceive or too hard to recognize using motion features alone.},
	keywords = {analysis;image, approach;action, Bayesian, classification;image, classification;object, framework;Bayes, interactions;inference, interpretation, localization;object, methods;gesture, MOTION, movements;human, perception;, perception;human-object, perception;object, process;object, processing;visual, recognition;image, recognition;video, segmentation;action, segmentation;object, signal, understanding;human},
	doi = {10.1109/CVPR.2007.383331},
	author = {Gupta,A. and Davis, Larry S.}
}
@conference {13137,
	title = {Robust Object Tracking with Regional Affine Invariant Features},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We present a tracking algorithm based on motion analysis of regional affine invariant image features. The tracked object is represented with a probabilistic occupancy map. Using this map as support, regional features are detected and probabilistically matched across frames. The motion of pixels is then established based on the feature motion. The object occupancy map is in turn updated according to the pixel motion consistency. We describe experiments to measure the sensitivities of our approach to inaccuracy in initialization, and compare it with other approaches.},
	keywords = {affine, algorithm;feature, analysis;image, analysis;pixel, consistency;regional, detection;motion, detection;tracking;, extraction;image, feature, features;robust, invariant, matching;image, MOTION, object, resolution;object, tracking},
	doi = {10.1109/ICCV.2007.4408948},
	author = {Tran,Son and Davis, Larry S.}
}
@conference {13136,
	title = {Segmentation using Meta-texture Saliency},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We address segmentation of an image into patches that have an underlying salient surface-roughness. Three intrinsic images are derived: reflectance, shading and meta- texture images. A constructive approach is proposed for computing a meta-texture image by preserving, equalizing and enhancing the underlying surface-roughness across color, brightness and illumination variations. We evaluate the performance on sample images and illustrate quantitatively that different patches of the same material, in an image, are normalized in their statistics despite variations in color, brightness and illumination. Finally, segmentation by line-based boundary-detection is proposed and results are provided and compared to known algorithms.},
	keywords = {analysis;image, colour, enhancement;image, image, image;salient, patches;image, segmentation;image, segmentation;meta-texture, surface-roughness;image, texture;},
	doi = {10.1109/ICCV.2007.4408930},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {12591,
	title = {Simulation and Analysis of Human Walking Motion},
	booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on},
	volume = {1},
	year = {2007},
	month = {2007/04//},
	pages = {I-797 -I-800 - I-797 -I-800},
	abstract = {Simulation and analysis of human walking motion has applications in surveillance and healthcare. In this paper we discuss an approach for modeling human walking motion using a mechanical model in the form of a kinematic chain consisting of rigid links and revolute joints. Our goal is to discriminate different types of walking motions using information such as joint torque and angle sequences extracted from the model. The angle sequences are initially extracted using 3D geometry. From these angle sequences we extract the torque sequences using a recursive Newton Euler inverse dynamics algorithm. Time series models and dynamic time warping of the torque and angle sequences are used to characterize and discriminate different walking patterns. A forward dynamics algorithm is also presented for synthesizing different walking sequences like limping from a normal walking torque sequence},
	keywords = {3D, algorithm;revolute, analysis;image, chain;mechanical, DYNAMICS, Euler, extraction;image, geometry;angle, inverse, joints;rigid, links;surveillance;time, method;feature, model;recursive, models;torque, MOTION, motion;kinematic, Newton, patterns;Newton, problems;time, sequences;dynamic, sequences;inverse, sequences;walking, series, series;time, simulation;torque;, TIME, walking, warp, warping;healthcare;human},
	doi = {10.1109/ICASSP.2007.366028},
	author = {Nandy, K. and Chellapa, Rama}
}
@conference {18184,
	title = {Component Forensics of Digital Cameras: A Non-Intrusive Approach},
	booktitle = {Information Sciences and Systems, 2006 40th Annual Conference on},
	year = {2006},
	month = {2006/03//},
	pages = {1194 - 1199},
	abstract = {This paper considers the problem of component forensics and proposes a methodology to identify the algorithms and parameters employed by various processing modules inside a digital camera. The proposed analysis techniques are non-intrusive, using only sample output images collected from the camera to find the color filter array pattern; and the algorithm and parameters of color interpolation employed in cameras. As demonstrated by various case studies in the paper, the features obtained from component forensic analysis provide useful evidence for such applications as detecting technology infringement, protecting intellectual property rights, determining camera source, and identifying image tampering.},
	keywords = {analysis;image, approach;processing, array, camera;image, Color, colour, detection;optical, filter, filters;, forensics;detecting, identification;intellectual, infringement;digital, interpolation;component, intrusive, module;cameras;image, pattern;color, property, protection;non, right, sensors;interpolation;object, tampering, technology},
	doi = {10.1109/CISS.2006.286646},
	author = {Swaminathan,A. and Wu,M. and Liu,K. J.R}
}
@conference {18256,
	title = {Non-Intrusive Forensic Analysis of Visual Sensors Using Output Images},
	booktitle = {Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on},
	volume = {5},
	year = {2006},
	month = {2006/05//},
	pages = {V - V},
	abstract = {This paper considers the problem of non-intrusive forensic analysis of the individual components in visual sensors and its implementation. As a new addition to the emerging area of forensic engineering, we present a framework for analyzing technologies employed inside digital cameras based on output images, and develop a set of forensic signal processing algorithms for visual sensors based on color array sensor and interpolation methods. We show through simulations that the proposed method is robust against compression and noise, and can help identify various processing components inside the camera. Such a non-intrusive forensic framework would provide useful evidence for analyzing technology infringement and evolution for visual sensors},
	keywords = {algorithms;interpolation, analysis;image, analysis;output, array, cameras;forensic, Color, colour, engineering;forensic, forensic, images;visual, methods;nonintrusive, PROCESSING, sensor;digital, sensors;cameras;image, sensors;interpolation;, signal},
	doi = {10.1109/ICASSP.2006.1661297},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
@conference {12609,
	title = {Stabilization and Mosaicing of Airborne Videos},
	booktitle = {Image Processing, 2006 IEEE International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {345 - 348},
	abstract = {We present an algorithm for stabilizing low-quality and low-resolution video sequences obtained from UAVs and MAVs flying over a predominantly planar terrain. The problem is important for the processing of videos captured from airborne platforms where most of the image region gives little information about the image-motion. The algorithm consists of approximately aligning the images using phase correlation, then refining the transformation parameters using available optical flow measurements and finally performing a minimization of the image difference by carefully selecting the image regions to make the iterations converge. In the second part of the paper, we describe an algorithm for camera ego-motion estimation by incorporating available metadata along with the video sequence. The availability of metadata such as IMU measurements provides a good initial solution for the camera orientation and helps the iterations converge faster by reducing the search space},
	keywords = {airborne, analysis;image, cameras;, convergence;low-resolution, ego-motion, estimation;iterations, flow, measurement;video, mosaicing;camera, MOTION, resolution;image, segmentation;image, sequence;optical, sequences;video, stabilization;image, video, Videos},
	doi = {10.1109/ICIP.2006.313164},
	author = {Ramachandran, M. and Chellapa, Rama}
}
@conference {18290,
	title = {Watermarking for image authentication},
	booktitle = {Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on},
	volume = {2},
	year = {1998},
	month = {1998/10//},
	pages = {437 -441 vol.2 - 437 -441 vol.2},
	abstract = {A data embedding method is proposed for image authentication based on table look-up in frequency domain. A visually meaningful watermark and a set of simple features are embedded invisibly in the marked image, which can be stored in the compressed form. The scheme can detect and localize alterations of the original image, such as the tampering of images exported from a digital camera},
	keywords = {alterations, analysis;image, analysis;message, authentication;image, authentication;table, camera;frequency, coding;image, colour, compression;frequency-domain, detection;alterations, domain;image, EMBEDDING, image, image;compressed, image;ownership, localisation;color, look-up;watermarking;data, lookup;, method;digital, protection;table, storage;data, tampering;marked},
	doi = {10.1109/ICIP.1998.723413},
	author = {M. Wu and Liu,Bede}
}
@article {15022,
	title = {Scalable data parallel algorithms for texture synthesis using Gibbs random fields},
	journal = {Image Processing, IEEE Transactions on},
	volume = {4},
	year = {1995},
	month = {1995/10//},
	pages = {1456 - 1460},
	abstract = {This article introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov random field model representation for textures, we present parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinking Machines CM-2 and CM-5. The use of fine-grained, data parallel processing techniques yields real-time algorithms for texture synthesis and compression that are substantially faster than the previously known sequential implementations. Although current implementations are on Connection Machines, the methodology presented enables machine-independent scalable algorithms for a number of problems in image processing and analysis},
	keywords = {algorithms;maximum, algorithms;parallel, algorithms;scalable, algorithms;texture, analysis;image, CM-2;Thinking, CM-5;fine-grained, compression;image, compression;texture, Connection, data, estimation;model, estimation;parallel, field;Thinking, fields;Markov, likelihood, machine, Machines;Gibbs, machines;random, Parallel, parameter, processes;, processes;data, processing;image, processing;machine-independent, random, representation;real-time, scalable, synthesis;Markov, texture;maximum},
	isbn = {1057-7149},
	doi = {10.1109/83.465111},
	author = {Bader, D.A. and JaJa, Joseph F. and Chellapa, Rama}
}
