@article {13066,
	title = {Face Identification Using Large Feature Sets},
	journal = {Image Processing, IEEE Transactions on},
	volume = {21},
	year = {2012},
	month = {2012/04//},
	pages = {2245 - 2255},
	abstract = {With the goal of matching unknown faces against a gallery of known people, the face identification task has been studied for several decades. There are very accurate techniques to perform face identification in controlled environments, particularly when large numbers of samples are available for each face. However, face identification under uncontrolled environments or with a lack of training data is still an unsolved problem. We employ a large and rich set of feature descriptors (with more than 70 000 descriptors) for face identification using partial least squares to perform multichannel feature weighting. Then, we extend the method to a tree-based discriminative structure to reduce the time required to evaluate probe samples. The method is evaluated on Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. Experiments show that our identification method outperforms current state-of-the-art results, particularly for identifying faces acquired across varying conditions.},
	keywords = {(mathematics);, approximations;trees, challenge, data, data;tree-based, discriminative, environments;face, feature, FERET;FRGC, grand, identification, least, recognition, recognition;least, sets;face, sets;facial, squares, squares;training, structure;uncontrolled, task;face, technology;multichannel, weighting;partial},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2011.2176951},
	author = {Schwartz, W.R. and Guo,Huimin and Choi,Jonghyun and Davis, Larry S.}
}
@conference {13071,
	title = {Action recognition using Partial Least Squares and Support Vector Machines},
	booktitle = {Image Processing (ICIP), 2011 18th IEEE International Conference on},
	year = {2011},
	month = {2011/09//},
	pages = {533 - 536},
	abstract = {We introduce an action recognition approach based on Partial Least Squares (PLS) and Support Vector Machines (SVM). We extract very high dimensional feature vectors representing spatio-temporal properties of actions and use multiple PLS regressors to find relevant features that distinguish amongst action classes. Finally, we use a multi-class SVM to learn and classify those relevant features. We applied our approach to INRIA{\textquoteright}s IXMAS dataset. Experimental results show that our method is superior to other methods applied to the IXMAS dataset.},
	keywords = {analysis;support, approach;multiclass, approximations;regression, dataset;action, dimensional, extraction;feature, extraction;image, feature, high, INRIA, IXMAS, least, machines;, machines;very, partial, properties;support, recognition, recognition;least, regressors;spatiotemporal, squares, SVM;multiple, vector, vectors},
	doi = {10.1109/ICIP.2011.6116399},
	author = {Ramadan,S. and Davis, Larry S.}
}
@conference {13068,
	title = {Face verification using large feature sets and one shot similarity},
	booktitle = {Biometrics (IJCB), 2011 International Joint Conference on},
	year = {2011},
	month = {2011/10//},
	pages = {1 - 8},
	abstract = {We present a method for face verification that combines Partial Least Squares (PLS) and the One-Shot similarity model[28]. First, a large feature set combining shape, texture and color information is used to describe a face. Then PLS is applied to reduce the dimensionality of the feature set with multi-channel feature weighting. This provides a discriminative facial descriptor. PLS regression is used to compute the similarity score of an image pair by One-Shot learning. Given two feature vector representing face images, the One-Shot algorithm learns discriminative models exclusively for the vectors being compared. A small set of unlabeled images, not containing images belonging to the people being compared, is used as a reference (negative) set. The approach is evaluated on the Labeled Face in the Wild (LFW) benchmark and shows very comparable results to the state-of-the-art methods (achieving 86.12\% classification accuracy) while maintaining simplicity and good generalization ability.},
	keywords = {analysis;set, approximations;regression, descriptor;labeled, Face, feature, in, information;face, information;texture, least, LFW;PLS;PLS, recognition;least, regression;color, sets;one, shot, similarity;partial, squares, squares;shape, the, theory;, verification;facial, wild;large},
	doi = {10.1109/IJCB.2011.6117498},
	author = {Guo,Huimin and Robson Schwartz,W. and Davis, Larry S.}
}
@conference {12472,
	title = {Evaluation of state-of-the-art algorithms for remote face recognition},
	booktitle = {Image Processing (ICIP), 2010 17th IEEE International Conference on},
	year = {2010},
	month = {2010/09//},
	pages = {1581 - 1584},
	abstract = {In this paper, we describe a remote face database which has been acquired in an unconstrained outdoor environment. The face images in this database suffer from variations due to blur, poor illumination, pose, and occlusion. It is well known that many state-of-the-art still image-based face recognition algorithms work well, when constrained (frontal, well illuminated, high-resolution, sharp, and complete) face images are presented. In this paper, we evaluate the effectiveness of a subset of existing still image-based face recognition algorithms for the remote face data set. We demonstrate that in addition to applying a good classification algorithm, consistent detection of faces with fewer false alarms and finding features that are robust to variations mentioned above are very important for remote face recognition. Also setting up a comprehensive metric to evaluate the quality of face images is necessary in order to reject images that are of low quality.},
	keywords = {alarm;feature, algorithm;still, classification;image, classification;visual, database;remote, databases;, extraction;hidden, extraction;image, Face, feature, image-based, image;false, quality;occlusion;remote, recognition;face, recognition;feature, recognition;state-of-the-art, removal;image},
	doi = {10.1109/ICIP.2010.5652608},
	author = {Ni,Jie and Chellapa, Rama}
}
@conference {13095,
	title = {Combining multiple kernels for efficient image classification},
	booktitle = {Applications of Computer Vision (WACV), 2009 Workshop on},
	year = {2009},
	month = {2009/12//},
	pages = {1 - 8},
	abstract = {We investigate the problem of combining multiple feature channels for the purpose of efficient image classification. Discriminative kernel based methods, such as SVMs, have been shown to be quite effective for image classification. To use these methods with several feature channels, one needs to combine base kernels computed from them. Multiple kernel learning is an effective method for combining the base kernels. However, the cost of computing the kernel similarities of a test image with each of the support vectors for all feature channels is extremely high. We propose an alternate method, where training data instances are selected, using AdaBoost, for each of the base kernels. A composite decision function, which can be evaluated by computing kernel similarities with respect to only these chosen instances, is learnt. This method significantly reduces the number of kernel computations required during testing. Experimental results on the benchmark UCI datasets, as well as on a challenging painting dataset, are included to demonstrate the effectiveness of our method.},
	keywords = {(artificial, AdaBoost;base, channels;multiple, classification;kernel, classification;learning, decision, feature, function;discriminative, intelligence);support, Kernel, kernel;image, kernels;composite, learning;support, machine;image, machines;, similarity;multiple, vector},
	doi = {10.1109/WACV.2009.5403040},
	author = {Siddiquie,B. and Vitaladevuni,S.N. and Davis, Larry S.}
}
@conference {18275,
	title = {Secure image retrieval through feature protection},
	booktitle = {Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on},
	year = {2009},
	month = {2009/04//},
	pages = {1533 - 1536},
	abstract = {This paper addresses the problem of image retrieval from an encrypted database, where data confidentiality is preserved both in the storage and retrieval process. The paper focuses on image feature protection techniques which enable similarity comparison among protected features. By utilizing both signal processing and cryptographic techniques, three schemes are investigated and compared, including bit-plane randomization, random projection, and randomized unary encoding. Experimental results show that secure image retrieval can achieve comparable retrieval performance to conventional image retrieval techniques without revealing information about image content. This work enriches the area of secure information retrieval and can find applications in secure online services for images and videos.},
	keywords = {bit-plane, confidentiality;data, database;image, databases;, encoding;signal, extraction;image, feature, processes;visual, processing;content-based, projection;randomized, protection;image, randomization;cryptographic, retrieval;cryptography;feature, retrieval;random, storage;encrypted, technique;data, unary},
	doi = {10.1109/ICASSP.2009.4959888},
	author = {Lu,Wenjun and Varna,A.L. and Swaminathan,A. and M. Wu}
}
@conference {13978,
	title = {Canny edge detection on NVIDIA CUDA},
	booktitle = {Computer Vision and Pattern Recognition Workshops, 2008. CVPRW {\textquoteright}08. IEEE Computer Society Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {The Canny edge detector is a very popular and effective edge feature detector that is used as a pre-processing step in many computer vision algorithms. It is a multi-step detector which performs smoothing and filtering, non-maxima suppression, followed by a connected-component analysis stage to detect ldquotruerdquo edges, while suppressing ldquofalserdquo non edge filter responses. While there have been previous (partial) implementations of the Canny and other edge detectors on GPUs, they have been focussed on the old style GPGPU computing with programming using graphical application layers. Using the more programmer friendly CUDA framework, we are able to implement the entire Canny algorithm. Details are presented along with a comparison with CPU implementations. We also integrate our detector in to MATLAB, a popular interactive simulation package often used by researchers. The source code will be made available as open source.},
	keywords = {algorithms;connected-component, analysis, application, Canny, CUDA;computer, detection;feature, detection;NVIDIA, detector;filtering;graphical, detector;non, edge, extraction;smoothing, feature, filter, layers;multistep, methods;, responses;nonmaxima, stage;edge, suppression;smoothing;computer, VISION, vision;edge},
	doi = {10.1109/CVPRW.2008.4563088},
	author = {Luo,Yuancheng and Duraiswami, Ramani}
}
@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 {13147,
	title = {Classifying Computer Generated Charts},
	booktitle = {Content-Based Multimedia Indexing, 2007. CBMI {\textquoteright}07. International Workshop on},
	year = {2007},
	month = {2007/06//},
	pages = {85 - 92},
	abstract = {We present an approach for classifying images of charts based on the shape and spatial relationships of their primitives. Five categories are considered: bar-charts, curve-plots, pie-charts, scatter-plots and surface-plots. We introduce two novel features to represent the structural information based on (a) region segmentation and (b) curve saliency. The local shape is characterized using the Histograms of Oriented Gradients (HOG) and the Scale Invariant Feature Transform (SIFT) descriptors. Each image is represented by sets of feature vectors of each modality. The similarity between two images is measured by the overlap in the distribution of the features -measured using the Pyramid Match algorithm. A test image is classified based on its similarity with training images from the categories. The approach is tested with a database of images collected from the Internet.},
	keywords = {algorithm;scale, analysis;visual, classification;image, database;image, databases;, feature, Internet;bar-chart;curve-plot;image, invariant, match, matching;image, relationship;surface-plot;Internet;image, representation;image, segmentation;pie-chart;pyramid, segmentation;statistical, transform;scatter-plot;spatial},
	doi = {10.1109/CBMI.2007.385396},
	author = {Prasad,V. S.N and Siddiquie,B. and Golbeck,J. and Davis, Larry S.}
}
@conference {13138,
	title = {An Interactive Approach to Pose-Assisted and Appearance-based Segmentation of Humans},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {An interactive human segmentation approach is described. Given regions of interest provided by users, the approach iteratively estimates segmentation via a generalized EM algorithm. Specifically, it encodes both spatial and color information in a nonparametric kernel density estimator, and incorporates local MRF constraints and global pose inferences to propagate beliefs over image space iteratively to determine a coherent segmentation. This ensures the segmented humans resemble the shapes of human poses. Additionally, a layered occlusion model and a probabilistic occlusion reasoning method are proposed to handle segmentation of multiple humans in occlusion. The approach is tested on a wide variety of images containing single or multiple occluded humans, and the segmentation performance is evaluated quantitatively.},
	keywords = {algorithm;appearance-based, algorithm;hidden, approach;layered, density, EM, Estimation, estimation;, estimator;pose-assisted, feature, human, Kernel, mechanisms;pose, method;expectation-maximisation, model;nonparametric, occlusion, reasoning, removal;image, segmentation;inference, segmentation;interactive, segmentation;probabilistic},
	doi = {10.1109/ICCV.2007.4409123},
	author = {Zhe Lin and Davis, Larry S. and David Doermann and DeMenthon,D.}
}
@conference {12581,
	title = {Kernel fully constrained least squares abundance estimates},
	booktitle = {Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International},
	year = {2007},
	month = {2007/07//},
	pages = {4041 - 4044},
	abstract = {A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each end member within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become necessary. This paper presents such an algorithm that estimates the abundances in the kernel feature space while maintaining the non-negativity and sum-to-one constraints. The usefulness of the algorithm is shown using the AVIRIS Cuprite, Nevada image.},
	keywords = {abundance, algorithm;kernel, analysis;, AVIRIS, based, constrained, constraint;feature, constraint;spectral, estimates;linear, extraction;geophysical, feature, fully, image;hyperspectral, imagery;kernel, least, mixing, model;nonnegativity, processing;geophysical, processing;multidimensional, processing;spectral, signal, space;kernel, squares, techniques;image, unmixing;sum-to-one},
	doi = {10.1109/IGARSS.2007.4423736},
	author = {Broadwater, J. and Chellapa, Rama and Banerjee, A. and Burlina, P.}
}
@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.}
}
@article {12587,
	title = {Target Tracking Using a Joint Acoustic Video System},
	journal = {Multimedia, IEEE Transactions on},
	volume = {9},
	year = {2007},
	month = {2007/06//},
	pages = {715 - 727},
	abstract = {In this paper, a multitarget tracking system for collocated video and acoustic sensors is presented. We formulate the tracking problem using a particle filter based on a state-space approach. We first discuss the acoustic state-space formulation whose observations use a sliding window of direction-of-arrival estimates. We then present the video state space that tracks a target{\textquoteright}s position on the image plane based on online adaptive appearance models. For the joint operation of the filter, we combine the state vectors of the individual modalities and also introduce a time-delay variable to handle the acoustic-video data synchronization issue, caused by acoustic propagation delays. A novel particle filter proposal strategy for joint state-space tracking is introduced, which places the random support of the joint filter where the final posterior is likely to lie. By using the Kullback-Leibler divergence measure, it is shown that the joint operation of the filter decreases the worst case divergence of the individual modalities. The resulting joint tracking filter is quite robust against video and acoustic occlusions due to our proposal strategy. Computer simulations are presented with synthetic and field data to demonstrate the filter{\textquoteright}s performance},
	keywords = {(numerical, acoustic, adaptive, appearance, approach;synchronization;time-delay, data, delay;acoustic, divergence;acoustic, estimate;joint, estimation;hidden, feature, filter;sliding, Filtering, fusion;multitarget, fusion;synchronisation;target, highways;direction-of-arrival, Kullback-Leibler, methods);sensor, model;particle, processing;, processing;automated, propagation, removal;optical, signal, system;multimodal, tracking;acoustic, tracking;direction-of-arrival, tracking;occlusion;online, tracking;particle, tracking;video, variable;visual, video, window;state-space},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2007.893340},
	author = {Cevher, V. and Sankaranarayanan,A. C and McClellan, J.H. and Chellapa, Rama}
}
@conference {13196,
	title = {Efficient mean-shift tracking via a new similarity measure},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {176 - 183 vol. 1 - 176 - 183 vol. 1},
	abstract = {The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-to-compute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the mean shift algorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatial-feature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.},
	keywords = {algorithm;, analysis;, Bhattacharyya, coefficient;, Color, colour, density, divergence;, estimates;, extraction;, fast, feature, frame-rate, Gauss, Gaussian, histograms;, image, Kernel, Kullback-Leibler, matching;, Mean-shift, measures;, nonparametric, processes;, sample-based, sequences;, similarity, spaces;, spatial-feature, tracking, tracking;, transform;},
	doi = {10.1109/CVPR.2005.139},
	author = {Yang,Changjiang and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {13194,
	title = {Extracting regions of symmetry},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {3},
	year = {2005},
	month = {2005/09//},
	pages = {III - 133-6 - III - 133-6},
	abstract = {This paper presents an approach for extending the normalized-cut (n-cut) segmentation algorithm to find symmetric regions present in natural images. We use an existing algorithm to quickly detect possible symmetries present in an image. The detected symmetries are then individually verified using the modified n-cut algorithm to eliminate spurious detections. The weights of the n-cut algorithm are modified so as to include both symmetric and spatial affinities. A global parameter is defined to model the tradeoff between spatial coherence and symmetry. Experimental results indicate that symmetric quality measure for a region segmented by our algorithm is a good indicator for the significance of the principal axis of symmetry.},
	keywords = {algorithm;, coherence;, DETECTION, detection;, elimination;, extraction;, feature, image, images;, natural, normalized-cut, object, region, segmentation, segmentation;, spatial, spurious, symmetry},
	doi = {10.1109/ICIP.2005.1530346},
	author = {Gupta,A. and Prasad,V. S.N and Davis, Larry S.}
}
@conference {13197,
	title = {Fast illumination-invariant background subtraction using two views: error analysis, sensor placement and applications},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {1071 - 1078 vol. 1 - 1071 - 1078 vol. 1},
	abstract = {Background modeling and subtraction to detect new or moving objects in a scene is an important component of many intelligent video applications. Compared to a single camera, the use of multiple cameras leads to better handling of shadows, specularities and illumination changes due to the utilization of geometric information. Although the result of stereo matching can be used as the feature for detection, it has been shown that the detection process can be made much faster by a simple subtraction of the intensities observed at stereo-generated conjugate pairs in the two views. The methodology however, suffers from false and missed detections due to some geometric considerations. In this paper, we perform a detailed analysis of such errors. Then, we propose a sensor configuration that eliminates false detections. Algorithms are also proposed that effectively eliminate most detection errors due to missed detections, specular reflections and objects being geometrically close to the background. Experiments on several scenes illustrate the utility and enhanced performance of the proposed approach compared to existing techniques.},
	keywords = {analysis;, application;, background, cameras;, configuration;, DETECTION, detection;, error, error;, extraction;, false, feature, handling;, illumination-invariance;, image, intelligent, matching;, modeling;, object, placement;, processing;, sensor, sensors;, shadow, stereo, subtraction;, video},
	doi = {10.1109/CVPR.2005.155},
	author = {Lim,Ser-Nam and Mittal,A. and Davis, Larry S. and Paragios,N.}
}
@conference {12659,
	title = {A method for converting a smiling face to a neutral face with applications to face recognition},
	booktitle = {Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP {\textquoteright}05). IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/03//},
	pages = {ii/977 - ii/980 Vol. 2 - ii/977 - ii/980 Vol. 2},
	abstract = {The human face displays a variety of expressions, like smile, sorrow, surprise, etc. All these expressions constitute nonrigid motions of various features of the face. These expressions lead to a significant change in the appearance of a facial image which leads to a drop in the recognition accuracy of a face-recognition system trained with neutral faces. There are other factors like pose and illumination which also lead to performance drops. Researchers have proposed methods to tackle the effects of pose and illumination; however, there has been little work on how to tackle expressions. We attempt to address the issue of expression invariant face-recognition. We present preprocessing steps for converting a smiling face to a neutral face. We expect that this would in turn make the vector in the feature space to be closer to the correct vector in the gallery, in an appearance-based face recognition. This conjecture is supported by our recognition results which demonstrate that the accuracy goes up if we include the expression-normalization block.},
	keywords = {appearance-based, Expression, Face, face;, feature, invariant, motion;, neutral, nonrigid, normalization;, recognition;, smiling},
	doi = {10.1109/ICASSP.2005.1415570},
	author = {Ramachandran, M. and Zhou,S. K and Jhalani, D. and Chellapa, Rama}
}
@conference {18280,
	title = {Security of feature extraction in image hashing},
	booktitle = {Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP {\textquoteright}05). IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/03//},
	pages = {ii/1041 - ii/1044 Vol. 2 - ii/1041 - ii/1044 Vol. 2},
	abstract = {Security and robustness are two important requirements for image hash functions. We introduce "differential entropy" as a metric to quantify the amount of randomness in image hash functions and to study their security. We present a mathematical framework and derive expressions for the proposed security metric for various common image hashing schemes. Using the proposed security metric, we discuss the trade-offs between security and robustness in image hashing.},
	keywords = {cryptography;, differential, digital, entropy;, extraction;, feature, functions;, hash, hashing;, image, metric;, processing;, randomness;, robustness;, Security, signature;, signatures;},
	doi = {10.1109/ICASSP.2005.1415586},
	author = {Swaminathan,A. and Mao,Yinian and M. Wu}
}
@article {18187,
	title = {Data hiding in binary image for authentication and annotation},
	journal = {Multimedia, IEEE Transactions on},
	volume = {6},
	year = {2004},
	month = {2004/08//},
	pages = {528 - 538},
	abstract = {This paper proposes a new method to embed data in binary images, including scanned text, figures, and signatures. The method manipulates "flippable" pixels to enforce specific block-based relationship in order to embed a significant amount of data without causing noticeable artifacts. Shuffling is applied before embedding to equalize the uneven embedding capacity from region to region. The hidden data can be extracted without using the original image, and can also be accurately extracted after high quality printing and scanning with the help of a few registration marks. The proposed data embedding method can be used to detect unauthorized use of a digitized signature, and annotate or authenticate binary documents. The paper also presents analysis and discussions on robustness and security issues.},
	keywords = {annotation;, authentication;, binary, coding;, data, digital, digitized, document, EMBEDDING, encapsulation;, extraction;, feature, hiding;, image, image;, method;, signature;, unauthorized, user;, watermarking;},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2004.830814},
	author = {M. Wu and Liu,Bede}
}
@conference {18190,
	title = {Data hiding in curves for collusion-resistant digital fingerprinting},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/10//},
	pages = {51 - 54 Vol. 1 - 51 - 54 Vol. 1},
	abstract = {This paper presents a new data hiding method for curves. The proposed algorithm parameterizes a curve using the B-spline model and adds a spread spectrum sequence in the coordinates of the B-spline control points. We demonstrate through experiments the robustness of the proposed data hiding algorithm against printing-and-scanning and collusions, and show its feasibility for collusion-resistant fingerprinting of topographic maps as well as writings/drawings from pen-based input devices.},
	keywords = {(mathematics);, B-spline, coding;, collusion-resistant, CONTROL, data, devices;, digital, document, encapsulation;, extraction;, feature, fingerprinting;, hiding;, image, INPUT, maps;, model;, pen-based, points;, printing-and-scanning, processing;, robustness;, sequence;, spectrum, splines, spread, topographic, watermarking;},
	doi = {10.1109/ICIP.2004.1418687},
	author = {Gou,Hongmei and M. Wu}
}
@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 {12686,
	title = {Robust Bayesian cameras motion estimation using random sampling},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {2},
	year = {2004},
	month = {2004/10//},
	pages = {1361 - 1364 Vol.2 - 1361 - 1364 Vol.2},
	abstract = {In this paper, we propose an algorithm for robust 3D motion estimation of wide baseline cameras from noisy feature correspondences. The posterior probability density function of the camera motion parameters is represented by weighted samples. The algorithm employs a hierarchy coarse-to-fine strategy. First, a coarse prior distribution of camera motion parameters is estimated using the random sample consensus scheme (RANSAC). Based on this estimate, a refined posterior distribution of camera motion parameters can then be obtained through importance sampling. Experimental results using both synthetic and real image sequences indicate the efficacy of the proposed algorithm.},
	keywords = {3D, baseline, Bayesian, CAMERAS, cameras;, coarse-to-fine, consensus, density, estimation;, feature, function;, hierarchy, image, images;, importance, matching;, MOTION, posterior, probability, probability;, processing;, random, RANSAC;, real, realistic, sample, sampling;, scheme;, sequences;, stereo, strategy;, synthetic, wide},
	doi = {10.1109/ICIP.2004.1419754},
	author = {Qian, G. and Chellapa, Rama and Qinfen Zheng}
}
@conference {12687,
	title = {Robust ego-motion estimation and 3D model refinement using depth based parallax model},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {4},
	year = {2004},
	month = {2004/10//},
	pages = {2483 - 2486 Vol. 4 - 2483 - 2486 Vol. 4},
	abstract = {We present an iterative algorithm for robustly estimating the ego-motion and refining and updating a coarse, noisy and partial depth map using a depth based parallax model and brightness derivatives extracted from an image pair. Given a coarse, noisy and partial depth map acquired by a range-finder or obtained from a Digital Elevation Map (DFM), we first estimate the ego-motion by combining a global ego-motion constraint and a local brightness constancy constraint. Using the estimated camera motion and the available depth map estimate, motion of the 3D points is compensated. We utilize the fact that the resulting surface parallax field is an epipolar field and knowing its direction from the previous motion estimates, estimate its magnitude and use it to refine the depth map estimate. Instead of assuming a smooth parallax field or locally smooth depth models, we locally model the parallax magnitude using the depth map, formulate the problem as a generalized eigen-value analysis and obtain better results. In addition, confidence measures for depth estimates are provided which can be used to remove regions with potentially incorrect (and outliers in) depth estimates for robustly estimating ego-motion in the next iteration. Results on both synthetic and real examples are presented.},
	keywords = {3D, algorithm;, analysis;, and, based, camera;, coarse, compensation;, DEM;, depth, digital, ego-motion, eigen-value, eigenfunctions;, eigenvalues, ELEVATION, epipolar, estimation;, extraction;, feature, field;, iteration, iterative, map;, method;, methods;, model, model;, MOTION, parallax, partial, range-finding;, refinement;, refining;, surface},
	doi = {10.1109/ICIP.2004.1421606},
	author = {Agrawala, Ashok K. and Chellapa, Rama}
}
@conference {12696,
	title = {Role of shape and kinematics in human movement analysis},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {1},
	year = {2004},
	month = {2004/07/02/june},
	pages = {I-730 - I-737 Vol.1 - I-730 - I-737 Vol.1},
	abstract = {Human gait and activity analysis from video is presently attracting a lot of attention in the computer vision community. In this paper we analyze the role of two of the most important cues in human motion-shape and kinematics. We present an experimental framework whereby it is possible to evaluate the relative importance of these two cues in computer vision based recognition algorithms. In the process, we propose a new gait recognition algorithm by computing the distance between two sequences of shapes that lie on a spherical manifold. In our experiments, shape is represented using Kendall{\textquoteright}s definition of shape. Kinematics is represented using a Linear Dynamical system We place particular emphasis on human gait. Our conclusions show that shape plays a role which is more significant than kinematics in current automated gait based human identification algorithms. As a natural extension we study the role of shape and kinematics in activity recognition. Our experiments indicate that we require models that contain both shape and kinematics in order to perform accurate activity classification. These conclusions also allow us to explain the relative performance of many existing methods in computer-based human activity modeling.},
	keywords = {activity, algorithm;, algorithms;, analysis;, autoregressive, average, based, classification;, community;, Computer, definition;, dynamical, extraction;, feature, Gait, hidden, human, identification, image, Kendall, linear, manifold;, Markov, modeling;, models;, MOTION, Movement, moving, processes;, recognition, sequences;, SHAPE, spherical, system;, VISION, vision;},
	doi = {10.1109/CVPR.2004.1315104},
	author = {Veeraraghavan,A. and Chowdhury, A.R. and Chellapa, Rama}
}
@article {12699,
	title = {Wide baseline image registration with application to 3-D face modeling},
	journal = {Multimedia, IEEE Transactions on},
	volume = {6},
	year = {2004},
	month = {2004/06//},
	pages = {423 - 434},
	abstract = {Establishing correspondence between features in two images of the same scene taken from different viewing angles is a challenging problem in image processing and computer vision. However, its solution is an important step in many applications like wide baseline stereo, three-dimensional (3-D) model alignment, creation of panoramic views, etc. In this paper, we propose a technique for registration of two images of a face obtained from different viewing angles. We show that prior information about the general characteristics of a face obtained from video sequences of different faces can be used to design a robust correspondence algorithm. The method works by matching two-dimensional (2-D) shapes of the different features of the face (e.g., eyes, nose etc.). A doubly stochastic matrix, representing the probability of match between the features, is derived using the Sinkhorn normalization procedure. The final correspondence is obtained by minimizing the probability of error of a match between the entire constellation of features in the two sets, thus taking into account the global spatial configuration of the features. The method is applied for creating holistic 3-D models of a face from partial representations. Although this paper focuses primarily on faces, the algorithm can also be used for other objects with small modifications.},
	keywords = {2D, 3D, algorithm;, baseline, biometrics;, Computer, configuration;, correspondence, doubly, error, extraction;, Face, feature, holistic, image, matching;, matrix;, minimization;, modeling;, models;, normalization, probability, probability;, procedure;, processes;, processing;, recognition;, registration;, representation;, sequences;, shapes;, Sinkhorn, spatial, statistics;, Stochastic, video, vision;, wide},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2004.827511},
	author = {Roy-Chowdhury, A.K. and Chellapa, Rama and Keaton, T.}
}
@conference {12734,
	title = {Combining multiple evidences for gait recognition},
	booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP {\textquoteright}03). 2003 IEEE International Conference on},
	volume = {3},
	year = {2003},
	month = {2003/04//},
	pages = {III - 33-6 vol.3 - III - 33-6 vol.3},
	abstract = {In this paper, we systematically analyze different components of human gait, for the purpose of human identification. We investigate dynamic features such as the swing of the hands/legs, the sway of the upper body and static features like height, in both frontal and side views. Both probabilistic and non-probabilistic techniques are used for matching the features. Various combination strategies may be used depending upon the gait features being combined. We discuss three simple rules: the Sum, Product and MIN rules that are relevant to our feature sets. Experiments using four different datasets demonstrate that fusion can be used as an effective strategy in recognition.},
	keywords = {analysis;, dynamic, evidences;, extraction;, feature, features;, frontal, Gait, height;, human, identification;, image, MIN, multiple, nonprobabilistic, probabilistic, Product, recognition;, rules;, sets;, side, static, Sum, sway;, swing;, techniques;, views;},
	doi = {10.1109/ICASSP.2003.1199100},
	author = {Cuntoor, N. and Kale, A. and Chellapa, Rama}
}
@conference {12720,
	title = {A hidden Markov model based framework for recognition of humans from gait sequences},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {II - 93-6 vol.3 - II - 93-6 vol.3},
	abstract = {In this paper we propose a generic framework based on hidden Markov models (HMMs) for recognition of individuals from their gait. The HMM framework is suitable, because the gait of an individual can be visualized as his adopting postures from a set, in a sequence which has an underlying structured probabilistic nature. The postures that the individual adopts can be regarded as the states of the HMM and are typical to that individual and provide a means of discrimination. The framework assumes that, during gait, the individual transitions between N discrete postures or states but it is not dependent on the particular feature vector used to represent the gait information contained in the postures. The framework, thus, provides flexibility in the selection of the feature vector. The statistical nature of the HMM lends robustness to the model. In this paper we use the binarized background-subtracted image as the feature vector and use different distance metrics, such as those based on the L<sub>1</sub> and L<sub>2</sub> norms of the vector difference, and the normalized inner product of the vectors, to measure the similarity between feature vectors. The results we obtain are better than the baseline recognition rates reported before.},
	keywords = {analysis;, background-subtracted, binarized, discrete, distance, feature, Gait, hidden, human, image, image;, Markov, metrics;, model;, models;, postures;, recognition;, sequences;, vector;},
	doi = {10.1109/ICIP.2003.1246624},
	author = {Sundaresan,Aravind and RoyChowdhury,Amit and Chellapa, Rama}
}
@conference {13243,
	title = {Human body pose estimation using silhouette shape analysis},
	booktitle = {Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.},
	year = {2003},
	month = {2003/07//},
	pages = {263 - 270},
	abstract = {We describe a system for human body pose estimation from multiple views that is fast and completely automatic. The algorithm works in the presence of multiple people by decoupling the problems of pose estimation of different people. The pose is estimated based on a likelihood function that integrates information from multiple views and thus obtains a globally optimal solution. Other characteristics that make our method more general than previous work include: (1) no manual initialization; (2) no specification of the dimensions of the 3D structure; (3) no reliance on some learned poses or patterns of activity; (4) insensitivity to edges and clutter in the background and within the foreground. The algorithm has applications in surveillance and promising results have been obtained.},
	keywords = {3D, analysis;, body, classification;, clutter;, detection;, estimation;, extraction;, feature, function;, human, image, likelihood, multiple, object, parameter, parameters;, Pixel, pose, probability;, segmentation;, segmentations;, SHAPE, silhouette, structure;, surveillance;, views;},
	doi = {10.1109/AVSS.2003.1217930},
	author = {Mittal,A. and Liang Zhao and Davis, Larry S.}
}
@conference {13247,
	title = {Learning dynamics for exemplar-based gesture recognition},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-571 - I-578 vol.1 - I-571 - I-578 vol.1},
	abstract = {This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states (decoupled from exemplars) to capture the dynamics over a large exemplar space where a nonparametric estimation approach is used to model the exemplar distribution. This reduces the need for lengthy and non-optimal training of the HMM observation model. We used the proposed approach for view-based recognition of gestures. The approach is based on representing each gesture as a sequence of learned body poses (exemplars). The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constraints between different poses using the proposed non-parametric HMM.},
	keywords = {arbitrary, body, by, Computer, constraint;, detection;, discrete, distribution, dynamics;, edge, estimation;, example;, exemplar, exemplar-based, extraction;, feature, framework;, gesture, gesture;, hidden, HMM;, human, image, learning, Markov, matching;, model;, models;, motion;, nonparametric, pose, probabilistic, recognition;, sequence;, space;, state;, statistics;, system, temporal, tool;, view-based, vision;},
	doi = {10.1109/CVPR.2003.1211405},
	author = {Elgammal,A. and Shet,V. and Yacoob,Yaser and Davis, Larry S.}
}
@conference {13248,
	title = {Probabilistic tracking in joint feature-spatial spaces},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-781 - I-788 vol.1 - I-781 - I-788 vol.1},
	abstract = {In this paper, we present a probabilistic framework for tracking regions based on their appearance. We exploit the feature-spatial distribution of a region representing an object as a probabilistic constraint to track that region over time. The tracking is achieved by maximizing a similarity-based objective function over transformation space given a nonparametric representation of the joint feature-spatial distribution. Such a representation imposes a probabilistic constraint on the region feature distribution coupled with the region structure, which yields an appearance tracker that is robust to small local deformations and partial occlusion. We present the approach for the general form of joint feature-spatial distributions and apply it to tracking with different types of image features including row intensity, color and image gradient.},
	keywords = {analysis;, appearance, appearance;, color;, colour, Computer, constraint;, deformation;, detection;, distribution;, edge, estimation;, extraction;, feature, feature-spatial, feature;, function, gradient;, image, intensity;, joint, likelihood, local, maximization;, maximum, nonparametric, object, objective, occlusion;, optical, partial, probabilistic, probability;, region, representation;, row, similarity-based, small, space;, structure;, target, tracker;, tracking;, transformation, vision;},
	doi = {10.1109/CVPR.2003.1211432},
	author = {Elgammal,A. and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {13245,
	title = {Scalable image-based multi-camera visual surveillance system},
	booktitle = {Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.},
	year = {2003},
	month = {2003/07//},
	pages = {205 - 212},
	abstract = {We describe the design of a scalable and wide coverage visual surveillance system. Scalability (the ability to add and remove cameras easily during system operation with minimal overhead and system degradation) is achieved by utilizing only image-based information for camera control. We show that when a pan-tilt-zoom camera pans and tilts, a given image point moves in a circular and a linear trajectory, respectively. We create a scene model using a plan view of the scene. The scene model makes it easy for us to handle occlusion prediction and schedule video acquisition tasks subject to visibility constraints. We describe a maximum weight matching algorithm to assign cameras to tasks that meet the visibility constraints. The system is illustrated both through simulations and real video from a 6-camera configuration.},
	keywords = {ACQUISITION, algorithm;, camera;, constraints;, feature, hidden, image-based, MATCHING, maximum, multi-camera, occlusion, pan-tilt-zoom, PLAN, prediction;, processing;, removal;, scalable, scheduling;, signal, Surveillance, surveillance;, system;, task, video, view;, visibility, visual, weight},
	doi = {10.1109/AVSS.2003.1217923},
	author = {Lim,Ser-Nam and Davis, Larry S. and Elgammal,A.}
}
@conference {16896,
	title = {Simultaneous pose and correspondence determination using line features},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {2},
	year = {2003},
	month = {2003/06//},
	pages = {II-424 - II-431 vol.2 - II-424 - II-431 vol.2},
	abstract = {We present a new robust line matching algorithm for solving the model-to-image registration problem. Given a model consisting of 3D lines and a cluttered perspective image of this model, the algorithm simultaneously estimates the pose of the model and the correspondences of model lines to image lines. The algorithm combines softassign for determining correspondences and POSIT for determining pose. Integrating these algorithms into a deterministic annealing procedure allows the correspondence and pose to evolve from initially uncertain values to a joint local optimum. This research extends to line features the SoftPOSIT algorithm proposed recently for point features. Lines detected in images are typically more stable than points and are less likely to be produced by clutter and noise, especially in man-made environments. Experiments on synthetic and real imagery with high levels of clutter, occlusion, and noise demonstrate the robustness of the algorithm.},
	keywords = {algorithm;, algorithms;, annealing;, clutter;, cluttered, Computer, correspondence, detection;, determination;, deterministic, environment;, extraction;, feature, feature;, image, image;, imagery;, images;, joint, line, local, man-made, MATCHING, matching;, measurement;, model-to-image, noise;, occlusion;, optimum;, perspective, point, pose, position, problem;, processing;, real, realistic, registration, simulated, softassign;, SoftPOSIT, stereo, synthetic, vision;},
	doi = {10.1109/CVPR.2003.1211499},
	author = {David,P. and DeMenthon,D. and Duraiswami, Ramani and Samet, Hanan}
}
@article {12752,
	title = {A generic approach to simultaneous tracking and verification in video},
	journal = {Image Processing, IEEE Transactions on},
	volume = {11},
	year = {2002},
	month = {2002/05//},
	pages = {530 - 544},
	abstract = {A generic approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior density estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given a measurement vector Z and two hypotheses H<sub>1</sub> and H0, we first estimate posterior probabilities P(H<sub>0</sub>|Z) and P(H<sub>1</sub>|Z), and then choose the one with the larger posterior probability as the true hypothesis. Several applications of the approach are illustrated by experiments devised to evaluate its performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented, illustrating vehicle tracking and verification, human (face) tracking and verification, facial feature tracking, and image sequence stabilization.},
	keywords = {approach;, Carlo, configuration;, correspondence, data;, density, density;, estimated, estimation;, evaluation;, extraction;, Face, facial, feature, generic, human, hypothesis, image, measurement, methods;, Monte, object, performance, posterior, probability, probability;, problem;, processing;, propagation;, recognition;, road, sequence, sequences;, sequential, signal, space;, stabilization;, state, synthetic, temporal, testing;, tracking;, vector;, vehicle, vehicles;, verification;, video, visual},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2002.1006400},
	author = {Li,Baoxin and Chellapa, Rama}
}
@conference {12746,
	title = {A hierarchical approach for obtaining structure from two-frame optical flow},
	booktitle = {Motion and Video Computing, 2002. Proceedings. Workshop on},
	year = {2002},
	month = {2002/12//},
	pages = {214 - 219},
	abstract = {A hierarchical iterative algorithm is proposed for extracting structure from two-frame optical flow. The algorithm exploits two facts: one is that in many applications, such as face and gesture recognition, the depth variation of the visible surface of an object in a scene is small compared to the distance between the optical center and the object; the other is that the time aliasing problem is alleviated at the coarse level for any two-frame optical flow estimate so that the estimate tends to be more accurate. A hierarchical representation for the relationship between the optical flow, depth, and the motion parameters is derived, and the resulting non-linear system is iteratively solved through two linear subsystems. At the coarsest level, the surface of the object tends to be flat, so that the inverse depth tends to be a constant, which is used as the initial depth map. Inverse depth and motion parameters are estimated by the two linear subsystems at each level and the results are propagated to finer levels. Error analysis and experiments using both computer-rendered images and real images demonstrate the correctness and effectiveness of our algorithm.},
	keywords = {algorithm;, aliasing;, analysis;, computer-rendered, depth, depth;, error, estimation;, extraction;, Face, feature, flow;, gesture, hierarchical, image, images;, inverse, iterative, methods;, MOTION, nonlinear, optical, parameter, processing;, real, recognition;, sequences;, signal, structure-from-motion;, system;, systems;, TIME, two-frame, variation;, video},
	doi = {10.1109/MOTION.2002.1182239},
	author = {Liu,Haiying and Chellapa, Rama and Rosenfeld, A.}
}
@article {12748,
	title = {Optimal edge-based shape detection},
	journal = {Image Processing, IEEE Transactions on},
	volume = {11},
	year = {2002},
	month = {2002/11//},
	pages = {1209 - 1227},
	abstract = {We propose an approach to accurately detecting two-dimensional (2-D) shapes. The cross section of the shape boundary is modeled as a step function. We first derive a one-dimensional (1-D) optimal step edge operator, which minimizes both the noise power and the mean squared error between the input and the filter output. This operator is found to be the derivative of the double exponential (DODE) function, originally derived by Ben-Arie and Rao (1994). We define an operator for shape detection by extending the DODE filter along the shape{\textquoteright}s boundary contour. The responses are accumulated at the centroid of the operator to estimate the likelihood of the presence of the given shape. This method of detecting a shape is in fact a natural extension of the task of edge detection at the pixel level to the problem of global contour detection. This simple filtering scheme also provides a tool for a systematic analysis of edge-based shape detection. We investigate how the error is propagated by the shape geometry. We have found that, under general assumptions, the operator is locally linear at the peak of the response. We compute the expected shape of the response and derive some of its statistical properties. This enables us to predict both its localization and detection performance and adjust its parameters according to imaging conditions and given performance specifications. Applications to the problem of vehicle detection in aerial images, human facial feature detection, and contour tracking in video are presented.},
	keywords = {1D, 2D, aerial, analysis;, boundary, conditions;, contour, cross, detection;, DODE, double, edge, edge-based, error, error;, exponential, extraction;, facial, feature, filter, filter;, Filtering, function;, geometry;, global, human, images;, imaging, localization, mean, methods;, NOISE, operator;, optimal, optimisation;, output;, performance;, pixel;, power;, propagation;, properties;, section;, SHAPE, square, squared, statistical, step, theory;, tracking;, two-dimensional, vehicle, video;},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2002.800896},
	author = {Moon, H. and Chellapa, Rama and Rosenfeld, A.}
}
@conference {12747,
	title = {Wide baseline image registration using prior information},
	booktitle = {Multimedia Signal Processing, 2002 IEEE Workshop on},
	year = {2002},
	month = {2002/12//},
	pages = {37 - 40},
	abstract = {Establishing correspondence between features in two images of the same scene taken from different viewing angles in a challenging problem in image processing and computer vision. However, its solution is an important step in many applications like wide baseline stereo, 3D model alignment, creation of panoramic views etc. In this paper, we propose a technique for registration of two images of a face obtained from different viewing angles. We show that prior information about the general characteristics of a face obtained from video sequences of different faces can be used to design a robust correspondence algorithm. The method works by matching 2D shapes of the different features of the face. A doubly stochastic matrix, representing the probability of match between the features, is derived using the Sinkhorn normalization procedure. The final correspondence is obtained by minimizing the probability of error of a match between the entire constellations of features in the two sets, thus taking into account the global spatial configuration of the features. The method is applied for creating holistic 3D models of a face from partial representations. Although this paper focuses primarily on faces, the algorithm can also be used for other objects with small modifications.},
	keywords = {2D, 3D, algorithm;, alignment;, angles;, baseline, Computer, configuration;, constellation;, correspondence, creation;, doubly, error, extraction;, Face, feature, global, holistic, image, images;, matching;, matrix;, model, models;, normalization, panoramic, probability;, procedure;, processes;, processing;, registration;, robust, sequences;, SHAPE, signal, Sinkhorn, spatial, statistics;, stereo;, Stochastic, video, view, viewing, vision;, wide},
	doi = {10.1109/MMSP.2002.1203242},
	author = {Chowdhury, AM and Chellapa, Rama and Keaton, T.}
}
@conference {14839,
	title = {Error propagation in full 3D-from-2D object recognition},
	booktitle = {Computer Vision and Pattern Recognition, 1994. Proceedings CVPR {\textquoteright}94., 1994 IEEE Computer Society Conference on},
	year = {1994},
	month = {1994/06//},
	pages = {892 - 898},
	abstract = {Robust recognition systems require a careful understanding of the effects of error in sensed features. Error in these image features results in uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty when model poses are based on matching three image and model points. This result applies to objects that are fully three-dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute this uncertainty when poses are based on any number of initial matches},
	keywords = {3D-from-2D, algorithm;, error, extraction;, feature, features;, handling;, image, initial, linear, matches;, object, Programming, programming;, propagation;, recognition, recognition;, robust, sequences;, systems;, Uncertainty, uncertainty;},
	doi = {10.1109/CVPR.1994.323920},
	author = {Alter,T. D. and Jacobs, David W.}
}
@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.}
}
@conference {13678,
	title = {The processing of form documents},
	booktitle = {Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on},
	year = {1993},
	month = {1993/10//},
	pages = {497 - 501},
	abstract = {An overview of an approach to the generic modeling and processing of known forms is presented. The system provides a methodology by which models are generated from regions in the document based on their usage. Automatic extraction of an optimal set of features to be used for registration is proposed, and it is shown how specialized detectors can be designed for each feature based on their position, orientation and width properties. Registration of the form with the model is accomplished using probing to establish correspondence. Form components which are corrupted by markings are detected and isolated, the intersections are interpreted and the properties of the non-form markings are used to reconstruct the strokes through the intersections. The feasibility of these ideas is demonstrated through an implementation of key components of the system},
	keywords = {AUTOMATIC, business, detectors;, document, documents;, extraction;, feature, form, forms;, generation;, generic, handling;, known, markings;, model, modeling;, non-form, optimal, properties;, reconstruction;, set;, specialized, stroke, width},
	doi = {10.1109/ICDAR.1993.395687},
	author = {David Doermann and Rosenfeld, A.}
}
@conference {13660,
	title = {Temporal clues in handwriting},
	booktitle = {Pattern Recognition, 1992. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on},
	year = {1992},
	month = {1992/09/03/aug},
	pages = {317 - 320},
	abstract = {Handwritten character recognition is typically classified as online or offline depending on the nature of the input data. Online data consists of a temporal sequence of instrument positions while offline data is in the form of a 2D image of the writing sample. Online recognition techniques have been relatively successful but have the disadvantage of requiring the data to be gathered during the writing process. This paper presents work on the extraction of temporal information from static images of handwriting and its implications for character recognition},
	keywords = {character, extraction;, feature, handwriting, information, offline, online, recognition;, techniques;, temporal},
	doi = {10.1109/ICPR.1992.201781},
	author = {David Doermann and Rosenfeld, A.}
}
