@conference {20155,
	title = {Interactive exploration of microstructural features in gigapixel microscopy images},
	booktitle = {IEEE International Conference on Image Processing},
	year = {2017},
	month = {09/2017},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Beijing, China},
	abstract = {Modern imaging technologies enable the study of microstructural features, which require capturing the finest details in high-resolution gigapixel images. Nevertheless, the resolution disparity between gigapixel images and megapixel displays presents a challenge to effective visual analysis because subtle texture differences are hardly perceivable at coarser resolutions. In this paper we present a hierarchical segmentation technique based on joint distribution of intensity and noise-resistant local binary patterns to differentiate subtle microstructural textures across various scales.
The coarse-to-fine segmentation procedure subdivides each parent segment into texturally-distinct child segments at progressively higher resolutions. The hierarchical structure of segments allows creating intermediate segmentation results interactively. Based on the intermediate results, we highlight regions with texture differences using distinct colors, which provide salient visual hints to users despite of the current viewing resolution. Our new technique has been validated on large microscopy images and shows promising results.},
	keywords = {gigapixel images, Image segmentation},
	author = {Hsueh-Chien Cheng and Cardone, Antonio and Varshney, Amitabh}
}
@conference {12450,
	title = {Entropy rate superpixel segmentation},
	booktitle = {2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2011},
	month = {2011/06/20/25},
	pages = {2097 - 2104},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We propose a new objective function for superpixel segmentation. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with similar sizes. We present a novel graph construction for images and show that this construction induces a matroid - a combinatorial structure that generalizes the concept of linear independence in vector spaces. The segmentation is then given by the graph topology that maximizes the objective function under the matroid constraint. By exploiting submodular and mono-tonic properties of the objective function, we develop an efficient greedy algorithm. Furthermore, we prove an approximation bound of {\textonehalf} for the optimality of the solution. Extensive experiments on the Berkeley segmentation benchmark show that the proposed algorithm outperforms the state of the art in all the standard evaluation metrics.},
	keywords = {balancing function, Berkeley segmentation benchmark, Complexity theory, Entropy, entropy rate, graph construction, graph theory, graph topology, greedy algorithm, Greedy algorithms, homogeneous clusters, Image edge detection, Image segmentation, matrix algebra, matroid constraint, measurement, pattern clustering, Random variables, standard evaluation metrics, superpixel segmentation, vector spaces},
	isbn = {978-1-4577-0394-2},
	doi = {10.1109/CVPR.2011.5995323},
	author = {Ming-Yu Liu and Tuzel, O. and Ramalingam, S. and Chellapa, Rama}
}
@article {12464,
	title = {Video Pr{\'e}cis: Highlighting Diverse Aspects of Videos},
	journal = {IEEE Transactions on Multimedia},
	volume = {12},
	year = {2010},
	month = {2010/12//},
	pages = {853 - 868},
	abstract = {Summarizing long unconstrained videos is gaining importance in surveillance, web-based video browsing, and video-archival applications. Summarizing a video requires one to identify key aspects that contain the essence of the video. In this paper, we propose an approach that optimizes two criteria that a video summary should embody. The first criterion, {\textquotedblleft}coverage,{\textquotedblright} requires that the summary be able to represent the original video well. The second criterion, {\textquotedblleft}diversity,{\textquotedblright} requires that the elements of the summary be as distinct from each other as possible. Given a user-specified summary length, we propose a cost function to measure the quality of a summary. The problem of generating a précis is then reduced to a combinatorial optimization problem of minimizing the proposed cost function. We propose an efficient method to solve the optimization problem. We demonstrate through experiments (on KTH data, unconstrained skating video, a surveillance video, and a YouTube home video) that optimizing the proposed criterion results in meaningful video summaries over a wide range of scenarios. Summaries thus generated are then evaluated using both quantitative measures and user studies.},
	keywords = {$K$-means, CAMERAS, combinatorial mathematics, combinatorial optimization, Cost function, data compression, Exemplar selection, Image segmentation, Internet, Iron, Length measurement, multimedia systems, Ncut, optimisation, Optimization methods, original video, Permission, shot segmentation, Surveillance, user specified summary length, video précis, Video sharing, video signal processing, Video summarization},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2010.2058795},
	author = {Shroff, N. and Turaga,P. and Chellapa, Rama}
}
@conference {14193,
	title = {Active segmentation for robotics},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009},
	year = {2009},
	month = {2009/10/10/15},
	pages = {3133 - 3139},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The semantic robots of the immediate future are robots that will be able to find and recognize objects in any environment. They need the capability of segmenting objects in their visual field. In this paper, we propose a novel approach to segmentation based on the operation of fixation by an active observer. Our approach is different from current approaches: while existing works attempt to segment the whole scene at once into many areas, we segment only one image region, specifically the one containing the fixation point. Furthermore, our solution integrates monocular cues (color, texture) with binocular cues (stereo disparities and optical flow). Experiments with real imagery collected by our active robot and from the known databases demonstrate the promise of the approach.},
	keywords = {binocular cues, image colour analysis, Image segmentation, Image texture, Intelligent robots, Layout, Machine vision, monocular cues, Navigation, optical flow, Orbital robotics, Robot sensing systems, robot vision, Robot vision systems, robotics active segmentation, Robotics and automation, semantic robot, Simultaneous localization and mapping, stereo disparity, stereo image processing},
	isbn = {978-1-4244-3803-7},
	doi = {10.1109/IROS.2009.5354325},
	author = {Mishra,A. and Aloimonos, J. and Ferm{\"u}ller, Cornelia}
}
@article {12513,
	title = {Multicamera Tracking of Articulated Human Motion Using Shape and Motion Cues},
	journal = {Image Processing, IEEE Transactions on},
	volume = {18},
	year = {2009},
	month = {2009/09//},
	pages = {2114 - 2126},
	abstract = {We present a completely automatic algorithm for initializing and tracking the articulated motion of humans using image sequences obtained from multiple cameras. A detailed articulated human body model composed of sixteen rigid segments that allows both translation and rotation at joints is used. Voxel data of the subject obtained from the images is segmented into the different articulated chains using Laplacian eigenmaps. The segmented chains are registered in a subset of the frames using a single-frame registration technique and subsequently used to initialize the pose in the sequence. A temporal registration method is proposed to identify the partially segmented or unregistered articulated chains in the remaining frames in the sequence. The proposed tracker uses motion cues such as pixel displacement as well as 2-D and 3-D shape cues such as silhouettes, motion residue, and skeleton curves. The tracking algorithm consists of a predictor that uses motion cues and a corrector that uses shape cues. The use of complementary cues in the tracking alleviates the twin problems of drift and convergence to local minima. The use of multiple cameras also allows us to deal with the problems due to self-occlusion and kinematic singularity. We present tracking results on sequences with different kinds of motion to illustrate the effectiveness of our approach. The pose of the subject is correctly tracked for the duration of the sequence as can be verified by inspection.},
	keywords = {2D shape cues, 3D shape cues, algorithms, Anatomic;Models, articulated human motion, automatic algorithm, Biological;Movement;Posture;Skeleton;Video Recording;, Computer-Assisted;Models, Eigenvalues and eigenfunctions, human pose estimation, HUMANS, Image motion analysis, IMAGE PROCESSING, image registration, Image segmentation, Image sequences, kinematic singularity, Laplacian eigenmaps, multicamera tracking algorithm, pixel displacement, pose estimation, single-frame registration technique, temporal registration method, tracking},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2009.2022290},
	author = {Sundaresan, A. and Chellapa, Rama}
}
@conference {14251,
	title = {Real-time shape retrieval for robotics using skip Tri-Grams},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009. IROS 2009},
	year = {2009},
	month = {2009/10/10/15},
	pages = {4731 - 4738},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The real time requirement is an additional constraint on many intelligent applications in robotics, such as shape recognition and retrieval using a mobile robot platform. In this paper, we present a scalable approach for efficiently retrieving closed contour shapes. The contour of an object is represented by piecewise linear segments. A skip Tri-Gram is obtained by selecting three segments in the clockwise order while allowing a constant number of segments to be {\~A}{\^A}{\textquestiondown}skipped{\~A}{\^A}{\textquestiondown} in between. The main idea is to use skip Tri-Grams of the segments to implicitly encode the distant dependency of the shape. All skip Tri-Grams are used for efficiently retrieving closed contour shapes without pairwise matching feature points from two shapes. The retrieval is at least an order of magnitude faster than other state-of-the-art algorithms. We score 80\% in the Bullseye retrieval test on the whole MPEG 7 shape dataset. We further test the algorithm using a mobile robot platform in an indoor environment. 8 objects are used for testing from different viewing directions, and we achieve 82\% accuracy.},
	keywords = {Bullseye retrieval test, Clocks, closed contour shape retrieval, Image retrieval, Image segmentation, Indexing, Information retrieval, Intelligent robots, Jacobian matrices, mobile robot, Mobile robots, MPEG 7 shape dataset, piecewise linear segments, Piecewise linear techniques, Real time systems, real-time shape retrieval, robot vision, SHAPE, shape recognition, shape representation, skip Tri-Grams, Testing},
	isbn = {978-1-4244-3803-7},
	doi = {10.1109/IROS.2009.5354738},
	author = {Li,Yi and Bitsakos,K. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {15922,
	title = {Sparsity inspired selection and recognition of iris images},
	booktitle = {Biometrics: Theory, Applications, and Systems, 2009. BTAS {\textquoteright}09. IEEE 3rd International Conference on},
	year = {2009},
	month = {2009/09//},
	pages = {1 - 6},
	abstract = {Iris images acquired from a partially cooperating subject often suffer from blur, occlusion due to eyelids, and specular reflections. The performance of existing iris recognition systems degrade significantly on these images. Hence it is essential to select good images from the incoming iris video stream, before they are input to the recognition algorithm. In this paper, we propose a sparsity based algorithm for selection of good iris images and their subsequent recognition. Unlike most existing algorithms for iris image selection, our method can handle segmentation errors and a wider range of acquisition artifacts common in iris image capture. We perform selection and recognition in a single step which is more efficient than devising separate specialized algorithms for the two. Recognition from partially cooperating users is a significant step towards deploying iris systems in a wide variety of applications.},
	keywords = {biometrics (access control), image recognition, Image segmentation, iris image recognition, iris image selection, iris video stream, sparsity inspired selection, video signal processing, video streaming},
	doi = {10.1109/BTAS.2009.5339067},
	author = {Pillai,J.K. and Patel, Vishal M. and Chellapa, Rama}
}
@conference {18477,
	title = {Imaging concert hall acoustics using visual and audio cameras},
	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008},
	year = {2008},
	month = {2008///},
	pages = {5284 - 5287},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Using a developed real time audio camera, that uses the output of a spherical microphone array beamformer steered in all directions to create central projection to create acoustic intensity images, we present a technique to measure the acoustics of rooms and halls. A panoramic mosaiced visual image of the space is also create. Since both the visual and the audio camera images are central projection, registration of the acquired audio and video images can be performed using standard computer vision techniques. We describe the technique, and apply it to the examine the relation between acoustical features and architectural details of the Dekelbaum concert hall at the Clarice Smith Performing Arts Center in College Park, MD.},
	keywords = {Acoustic imaging, acoustic intensity images, acoustic measurement, Acoustic measurements, Acoustic scattering, acoustic signal processing, acoustical camera, acoustical scene analysis, acquired audio registration, audio cameras, audio signal processing, CAMERAS, central projection, Computer vision, Educational institutions, HUMANS, image registration, Image segmentation, imaging concert hall acoustics, Layout, microphone arrays, panoramic mosaiced visual image, Raman scattering, reverberation, room acoustics, spherical microphone array beamformer, spherical microphone arrays, video image registration, visual cameras},
	isbn = {978-1-4244-1483-3},
	doi = {10.1109/ICASSP.2008.4518852},
	author = {O{\textquoteright}Donovan,A. and Duraiswami, Ramani and Zotkin,Dmitry N}
}
@article {12544,
	title = {Model Driven Segmentation of Articulating Humans in Laplacian Eigenspace},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {30},
	year = {2008},
	month = {2008/10//},
	pages = {1771 - 1785},
	abstract = {We propose a general approach using Laplacian Eigenmaps and a graphical model of the human body to segment 3D voxel data of humans into different articulated chains. In the bottom-up stage, the voxels are transformed into a high-dimensional (6D or less) Laplacian Eigenspace (LE) of the voxel neighborhood graph. We show that LE is effective at mapping voxels on long articulated chains to nodes on smooth 1D curves that can be easily discriminated, and prove these properties using representative graphs. We fit 1D splines to voxels belonging to different articulated chains such as the limbs, head and trunk, and determine the boundary between splines using the spline fitting error. A top-down probabilistic approach is then used to register the segmented chains, utilizing their mutual connectivity and individual properties. Our approach enables us to deal with complex poses such as those where the limbs form loops. We use the segmentation results to automatically estimate the human body models. While we use human subjects in our experiments, the method is fairly general and can be applied to voxel-based segmentation of any articulated object composed of long chains. We present results on real and synthetic data that illustrate the usefulness of this approach.},
	keywords = {3D voxel data segmentation, algorithms, Anatomic;Pattern Recognition, Artificial intelligence, Automated;Reproducibility of Results;Sensitivity and Specificity;Whole Body Imaging;, Computer simulation, Computer-Assisted;Imaging, curve fitting, Eigenvalues and eigenfunctions, graph theory, human articulation, human body graphical model, human motion analysis, HUMANS, Image Enhancement, Image Interpretation, Image motion analysis, image registration, Image segmentation, Laplace transforms, Laplacian eigenspace transform, model driven segmentation, probability, representative graph, skeleton registration, Smoothing methods, solid modelling, spline fitting error, splines (mathematics), Three-Dimensional;Joints;Models, top-down probabilistic approach, voxel neighborhood graph},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2007.70823},
	author = {Sundaresan, A. and Chellapa, Rama}
}
@conference {15061,
	title = {A Time Saving Method for Human Detection in Wide Angle Camera Images},
	booktitle = {Machine Learning and Cybernetics, 2006 International Conference on},
	year = {2006},
	month = {2006/08//},
	pages = {4029 - 4034},
	abstract = {A time-saving method for human detection in an indoor environment in wide angle camera images is presented in this paper, without using time-consuming template operations such as edge detection and morphological operations. It excludes other objects in motion to detect humans including cases of the partial occlusion of humans, rotation of the head and variation of skin colors. This algorithm is applicable where the memory space is limited and the time requirements are strict, such as in an embedded system. The experimental results show that it took about 0.35 seconds to process the algorithm after the scene study time},
	keywords = {CAMERAS, Embedded system, human detection, image recognition, Image segmentation, indoor environment, Motion estimation, time-saving method, wide angle camera images},
	doi = {10.1109/ICMLC.2006.258804},
	author = {Zhuolin Jiang and Li,Shao-Fa and Gao,Dong-Fa}
}
@conference {11897,
	title = {Identifying and segmenting human-motion for mobile robot navigation using alignment errors},
	booktitle = {12th International Conference on Advanced Robotics, 2005. ICAR {\textquoteright}05. Proceedings},
	year = {2005},
	month = {2005/07//},
	pages = {398 - 403},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper presents a new human-motion identification and segmentation algorithm, for mobile robot platforms. The algorithm is based on computing the alignment error between pairs of object images acquired from a moving platform. Pairs of images generating relatively small alignment errors are used to estimate the fundamental frequency of the object{\textquoteright}s motion. A decision criterion is then used to test the significance of the estimated frequency and to classify the object{\textquoteright}s motion. To verify the validity of the proposed approach, experimental results are shown on different classes of objects},
	keywords = {Computer errors, Educational institutions, Frequency estimation, human-motion identification, human-motion segmentation, HUMANS, Image motion analysis, Image segmentation, mobile robot navigation, Mobile robots, Motion estimation, Navigation, Object detection, robot vision, SHAPE},
	isbn = {0-7803-9178-0},
	doi = {10.1109/ICAR.2005.1507441},
	author = {Abd-Almageed, Wael and Burns,B. J and Davis, Larry S.}
}
@conference {14224,
	title = {Integration of motion fields through shape},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005},
	volume = {2},
	year = {2005},
	month = {2005/06/20/25},
	pages = {663- 669 vol. 2 - 663- 669 vol. 2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Structure from motion from single flow fields has been studied intensively, but the integration of information from multiple flow fields has not received much attention. Here we address this problem by enforcing constraints on the shape (surface normals) of the scene in view, as opposed to constraints on the structure (depth). The advantage of integrating shape is two-fold. First, we do not need to estimate feature correspondences over multiple frames, but we only need to match patches. Second, the shape vectors in the different views are related only by rotation. This constraint on shape can be combined easily with motion estimation, thus formulating motion and structure estimation from multiple views as a practical constrained minimization problem using a rank-3 constraint. Based on this constraint, we develop a 3D motion technique, which locates through color and motion segmentation, planar patches in the scene, matches patches over multiple frames, and estimates the motion between multiple frames and the shape of the selected scene patches using the image gradients. Experiments evaluate the accuracy of the 3D motion estimation and demonstrate the motion and shape estimation of the technique by super-resolving an image sequence.},
	keywords = {3D motion estimation, Automation, CAMERAS, computational geometry, Computer vision, constrained minimization problem, decoupling translation from rotation, Educational institutions, image colour analysis, image gradients, image resolution, Image segmentation, image sequence, Image sequences, integration of motion fields, Layout, minimisation, Motion estimation, motion field integration, motion segmentation, parameter estimation, planar patches, rank-3 constraint, scene patches, SHAPE, shape and rotation, shape estimation, structure estimation},
	isbn = {0-7695-2372-2},
	doi = {10.1109/CVPR.2005.190},
	author = {Ji,H. and Ferm{\"u}ller, Cornelia}
}
@article {14230,
	title = {Motion segmentation using occlusions},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {27},
	year = {2005},
	month = {2005/06//},
	pages = {988 - 992},
	abstract = {We examine the key role of occlusions in finding independently moving objects instantaneously in a video obtained by a moving camera with a restricted field of view. In this problem, the image motion is caused by the combined effect of camera motion (egomotion), structure (depth), and the independent motion of scene entities. For a camera with a restricted field of view undergoing a small motion between frames, there exists, in general, a set of 3D camera motions compatible with the observed flow field even if only a small amount of noise is present, leading to ambiguous 3D motion estimates. If separable sets of solutions exist, motion-based clustering can detect one category of moving objects. Even if a single inseparable set of solutions is found, we show that occlusion information can be used to find ordinal depth, which is critical in identifying a new class of moving objects. In order to find ordinal depth, occlusions must not only be known, but they must also be filled (grouped) with optical flow from neighboring regions. We present a novel algorithm for filling occlusions and deducing ordinal depth under general circumstances. Finally, we describe another category of moving objects which is detected using cardinal comparisons between structure from motion and structure estimates from another source (e.g., stereo).},
	keywords = {3D motion estimation, algorithms, Artificial intelligence, CAMERAS, Computer vision, Filling, hidden feature removal, Image Enhancement, Image Interpretation, Computer-Assisted, image motion, Image motion analysis, Image segmentation, Layout, MOTION, Motion detection, Motion estimation, motion segmentation, Movement, Object detection, occlusion, occlusions, optical flow, ordinal depth, Pattern Recognition, Automated, Photography, Reproducibility of results, segmentation, Semiconductor device modeling, Sensitivity and Specificity, video analysis., Video Recording},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2005.123},
	author = {Ogale, A. S and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {12034,
	title = {The influence of shape on image correspondence},
	booktitle = {2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004. Proceedings},
	year = {2004},
	month = {2004/09/06/9},
	pages = {945 - 952},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We examine the implications of shape on the process of finding dense correspondence and half-occlusions for a stereo pair of images. The desired property of the depth map is that it should be a piecewise continuous function which is consistent with the images and which has the minimum number of discontinuities. To zeroeth order, piecewise continuity becomes piecewise constancy. Using this approximation, we first discuss an approach for dealing with such a fronto-parallel shapeless world, and the problems involved therein. We then introduce horizontal and vertical slant to create a first order approximation to piecewise continuity. We highlight the fact that a horizontally slanted surface (ie. having depth variation in the direction of the separation of the two cameras) appears horizontally stretched in one image as compared to the other image. Thus, while corresponding two images, N pixels on a scanline in one image may correspond to a different number of pixels M in the other image, which has consequences with regard to sampling and occlusion detection. We also discuss the asymmetry between vertical and horizontal slant, and the central role of nonhorizontal edges in the context of vertical slant. Using experiments, we discuss cases where existing algorithms fail, and how the incorporation of new constraints provides correct results.},
	keywords = {Automation, CAMERAS, Computational modeling, first order approximation, Geometrical optics, hidden feature removal, image sampling, Image segmentation, Layout, occlusion detection, piecewise continuous function, Pixel, SHAPE, Simulated annealing, stereo image processing, surface fitting},
	isbn = {0-7695-2223-8},
	doi = {10.1109/TDPVT.2004.1335418},
	author = {Ogale, A. S and Aloimonos, J.}
}
@conference {11896,
	title = {Kernel snakes: non-parametric active contour models},
	booktitle = {IEEE International Conference on Systems, Man and Cybernetics, 2003},
	volume = {1},
	year = {2003},
	month = {2003/10//},
	pages = {240- 244 vol.1 - 240- 244 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the advantages of using the proposed non-parametric model compared to other parametric techniques. Multi-colored-target tracking is used to demonstrate the performance of the proposed approach. Experimental results show enhanced, real-time performance.},
	keywords = {Active contours, Artificial intelligence, Bayes methods, Bayesian decision theory, Bayesian methods, decision theory, Deformable models, Image edge detection, Image segmentation, Intelligent robots, Kernel, kernel snakes, Laboratories, multicolored target tracking, nonparametric active contour models, nonparametric generalized formulation, nonparametric model, nonparametric statistics, nonparametric techniques, real time performance, Robot vision systems, statistical pressure snakes, target tracking},
	isbn = {0-7803-7952-7},
	doi = {10.1109/ICSMC.2003.1243822},
	author = {Abd-Almageed, Wael and Smith,C.E. and Ramadan,S.}
}
@conference {11899,
	title = {Mixture models for dynamic statistical pressure snakes},
	booktitle = {16th International Conference on Pattern Recognition, 2002. Proceedings},
	volume = {2},
	year = {2002},
	month = {2002///},
	pages = {721- 724 vol.2 - 721- 724 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper introduces a new approach to statistical pressure snakes. It uses statistical modeling for both object and background to obtain a more robust pressure model. The Expectation Maximization (EM) algorithm is used to model the data into a Mixture of Gaussians (MoG). Bayesian theory is then employed as a decision making mechanism. Experimental results using the traditional pressure model and the new mixture pressure model demonstrate the effectiveness of the new models.},
	keywords = {active contour models, Active contours, Artificial intelligence, Bayes methods, Bayesian methods, Bayesian theory, complex colored object, Computer vision, decision making, decision making mechanism, dynamic statistical pressure snakes, Equations, expectation maximization algorithm, Gaussian distribution, image colour analysis, Image edge detection, Image segmentation, Intelligent robots, mixture models, mixture of Gaussians, mixture pressure model, Robot vision systems, robust pressure model, Robustness, segmentation results, statistical analysis, statistical modeling},
	isbn = {0-7695-1695-X},
	doi = {10.1109/ICPR.2002.1048404},
	author = {Abd-Almageed, Wael and Smith,C.E.}
}
@conference {14168,
	title = {Global rigidity constraints in image displacement fields},
	booktitle = {, Fifth International Conference on Computer Vision, 1995. Proceedings},
	year = {1995},
	month = {1995/06/20/23},
	pages = {245 - 250},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Image displacement fields-optical flow fields, stereo disparity fields, normal flow fields-due to rigid motion possess a global geometric structure which is independent of the scene in view. Motion vectors of certain lengths and directions are constrained to lie on the imaging surface at particular loci whose location and form depends solely on the 3D motion parameters. If optical flow fields or stereo disparity fields are considered, then equal vectors are shown to lie on conic sections. Similarly, for normal motion fields, equal vectors lie within regions whose boundaries also constitute conics. By studying various properties of these curves and regions and their relationships, a characterization of the structure of rigid motion fields is given. The goal of this paper is to introduce a concept underlying the global structure of image displacement fields. This concept gives rise to various constraints that could form the basis of algorithms for the recovery of visual information from multiple views},
	keywords = {3D motion parameters, algorithms, Automation, Computer science, Computer vision, conic sections, curves, equal vectors, Fluid flow measurement, global geometric structure, global rigidity constraints, image displacement fields, Image motion analysis, Image segmentation, Image sequences, imaging surface, Laboratories, Layout, Motion estimation, Motion measurement, motion vectors, multiple views, normal flow fields, optical flow fields, regions, rigid motion, stereo disparity fields, Stereo vision, vectors, visual information recovery},
	isbn = {0-8186-7042-8},
	doi = {10.1109/ICCV.1995.466779},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {11982,
	title = {Global rigidity constraints in image displacement fields},
	booktitle = {Proceedings of Fifth International Conference on Computer Vision, 1995},
	year = {1995},
	month = {1995/06/20/23},
	pages = {245 - 250},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Image displacement fields-optical flow fields, stereo disparity fields, normal flow fields-due to rigid motion possess a global geometric structure which is independent of the scene in view. Motion vectors of certain lengths and directions are constrained to lie on the imaging surface at particular loci whose location and form depends solely on the 3D motion parameters. If optical flow fields or stereo disparity fields are considered, then equal vectors are shown to lie on conic sections. Similarly, for normal motion fields, equal vectors lie within regions whose boundaries also constitute conics. By studying various properties of these curves and regions and their relationships, a characterization of the structure of rigid motion fields is given. The goal of this paper is to introduce a concept underlying the global structure of image displacement fields. This concept gives rise to various constraints that could form the basis of algorithms for the recovery of visual information from multiple views},
	keywords = {3D motion parameters, algorithms, Automation, Computer science, Computer vision, conic sections, curves, equal vectors, Fluid flow measurement, global geometric structure, global rigidity constraints, image displacement fields, Image motion analysis, Image segmentation, Image sequences, imaging surface, Laboratories, Layout, Motion estimation, Motion measurement, motion vectors, multiple views, normal flow fields, optical flow fields, regions, rigid motion, stereo disparity fields, Stereo vision, vectors, visual information recovery},
	isbn = {0-8186-7042-8},
	doi = {10.1109/ICCV.1995.466779},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@article {14189,
	title = {A syntactic approach to scale-space-based corner description},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {16},
	year = {1994},
	month = {1994/07//},
	pages = {748 - 751},
	abstract = {Planar curves are described by information about corners integrated over various levels of resolution. The detection of corners takes place on a digital representation. To compensate for ambiguities arising from sampling problems due to the discreteness, results about the local behavior of curvature extrema in continuous scale-space are employed},
	keywords = {Computer vision, corner detection, curvature extrema, edge detection, IMAGE PROCESSING, image resolution, Image segmentation, Laboratories, Large-scale systems, PARALLEL PROCESSING, pattern recognition, planar curves, resolution, Sampling methods, sampling problems, scale space based corner description, SHAPE, Smoothing methods, syntactic approach},
	isbn = {0162-8828},
	doi = {10.1109/34.297957},
	author = {Ferm{\"u}ller, Cornelia and Kropatsch,W.}
}
@conference {18297,
	title = {Early vision processing using a multi-stage diffusion process},
	booktitle = {1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1993. Proceedings CVPR {\textquoteright}93},
	year = {1993},
	month = {1993/06//},
	pages = {41 - 46},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The use of a multistage diffusion process in the early processing of range data is examined. The input range data are interpreted as occupying a volume in 3-D space. Each diffusion stage simulates the process of diffusing part of the boundary of the volume into the volume. The outcome of the process can be used for both discontinuity detection and segmentation into shape homogeneous regions. The process is applied to synthetic noise-free and noisy step, roof, and valley edges as well as to real range images},
	keywords = {3-D space, Computational modeling, Computer vision, Diffusion processes, discontinuity detection, early vision processing, Educational institutions, Image edge detection, Image segmentation, Laboratories, multistage diffusion process, Noise shaping, noise-free edges, noisy edges, Performance analysis, roof edges, segmentation, SHAPE, shape homogeneous regions, step edges, valley edges},
	isbn = {0-8186-3880-X},
	doi = {10.1109/CVPR.1993.341003},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {14169,
	title = {Hierarchical curve representation},
	booktitle = {, 11th IAPR International Conference on Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings},
	year = {1992},
	month = {1992/09/30/Aug-3},
	pages = {143 - 146},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Presents a robust method for describing planar curves in multiple resolution using curvature information. The method is developed by taking into account the discrete nature of digital images as well as the discrete aspect of a multiresolution structure (pyramid). The authors deal with the robustness of the technique, which is due to the additional information that is extracted from observing the behavior of corners in the pyramid. Furthermore the resulting algorithm is conceptually simple and easily parallelizable. They develop theoretical results, analyzing the curvature of continuous curves in scale-space, which show the behavior of curvature extrema under varying scale. These results are used to eliminate any ambiguities that might arise from sampling problems due to the discreteness of the representation. Finally, experimental results demonstrate the potential of the method},
	keywords = {Automation, continuous curves, curvature, data mining, digital images, Educational institutions, Feature extraction, hierarchical curve representation, IMAGE PROCESSING, image recognition, image resolution, Image segmentation, multiresolution structure, Object recognition, planar curves, pyramid, Robustness, Sampling methods, Smoothing methods},
	isbn = {0-8186-2920-7},
	doi = {10.1109/ICPR.1992.201947},
	author = {Ferm{\"u}ller, Cornelia and Kropatsch,W.}
}
@conference {14233,
	title = {Multi-resolution shape description by corners},
	booktitle = {, 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1992. Proceedings CVPR {\textquoteright}92},
	year = {1992},
	month = {1992/06/15/18},
	pages = {271 - 276},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A robust method for describing planar curves in multiple resolution using curvature information is presented. The method is developed by taking into account the discrete nature of digital images as well as the discrete aspect of a multiresolution structure (pyramid). The main contribution lies in the robustness of the technique, which is due to the additional information that is extracted from observing the behavior of corners in the whole pyramid. Furthermore, the resulting algorithm is conceptually simple and easily parallelizable. Theoretical results are developed analyzing the curvature of continuous curves in scale-space and showing the behavior of curvature extrema under varying scale. The results are used to eliminate any ambiguities that might arise from sampling problems due to the discreteness of the representation. Experimental results demonstrate the potential of the method},
	keywords = {ambiguities, Automation, computational geometry, Computer vision, continuous curves, corners, curvature extrema, curvature information, curve fitting, digital images, Feature extraction, IMAGE PROCESSING, image resolution, Image segmentation, Laboratories, multiple resolution, multiresolution structure, parallelizable, planar curves, Robustness, Sampling methods, scale-space, SHAPE, Smoothing methods, varying scale},
	isbn = {0-8186-2855-3},
	doi = {10.1109/CVPR.1992.223264},
	author = {Ferm{\"u}ller, Cornelia and Kropatsch,W.}
}
