Results 1  10
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447
Active Appearance Models
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and graylevel variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations i ..."
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Cited by 1488 (50 self)
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AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and graylevel variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors. Index TermsÐAppearance models, deformable templates, model matching. 1
Articulated body motion capture by annealed particle filtering
 In IEEE Conf. on Computer Vision and Pattern Recognition
, 2000
"... The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30) to be recovered. Search algorithms, either deterministic or stochastic, that search such a space without constraint, fall foul of exponential computational complexity. One approach is to intr ..."
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Cited by 408 (3 self)
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The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30) to be recovered. Search algorithms, either deterministic or stochastic, that search such a space without constraint, fall foul of exponential computational complexity. One approach is to introduce constraints — either labelling using markers or colour coding, prior assumptions about motion trajectories or view restrictions. Another is to relax constraints arising from articulation, and track limbs as if their motions were independent. In contrast, here we aim for general tracking without special preparation of subjects or restrictive assumptions. The principal contribution of this paper is the development of a modified particle filter for search in high dimensional configuration spaces. It uses a continuation principle, based on annealing, to introduce the influence of narrow peaks in the fitness function, gradually. The new algorithm, termed annealed particle filtering, is shown to be capable of recovering full articulated body motion efficiently. 1.
A Survey of Computer VisionBased Human Motion Capture
 Computer Vision and Image Understanding
, 2001
"... A comprehensive survey of computer visionbased human motion capture literature from the past two decades is presented. The focus is on a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition. Each ..."
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Cited by 394 (14 self)
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A comprehensive survey of computer visionbased human motion capture literature from the past two decades is presented. The focus is on a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition. Each process is discussed and divided into subprocesses and/or categories of methods to provide a reference to describe and compare the more than 130 publications covered by the survey. References are included throughout the paper to exemplify important issues and their relations to the various methods. A number of general assumptions used in this research field are identified and the character of these assumptions indicates that the research field is still in an early stage of development. To evaluate the state of the art, the major application areas are identified and performances are analyzed in light of the methods
Image analogies
, 2001
"... Figure 1 An image analogy. Our problem is to compute a new “analogous ” image B ′ that relates to B in “the same way ” as A ′ relates to A. Here, A, A ′ , and B are inputs to our algorithm, and B ′ is the output. The fullsize images are shown in Figures 10 and 11. This paper describes a new framewo ..."
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Cited by 353 (8 self)
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Figure 1 An image analogy. Our problem is to compute a new “analogous ” image B ′ that relates to B in “the same way ” as A ′ relates to A. Here, A, A ′ , and B are inputs to our algorithm, and B ′ is the output. The fullsize images are shown in Figures 10 and 11. This paper describes a new framework for processing images by example, called “image analogies. ” The framework involves two stages: a design phase, in which a pair of images, with one image purported to be a “filtered ” version of the other, is presented as “training data”; and an application phase, in which the learned filter is applied to some new target image in order to create an “analogous” filtered result. Image analogies are based on a simple multiscale autoregression, inspired primarily by recent results in texture synthesis. By choosing different types of source image pairs as input, the framework supports a wide variety of “image filter ” effects, including traditional image filters, such as blurring or embossing; improved texture synthesis, in which some textures are synthesized with higher quality than by previous approaches; superresolution, in which a higherresolution image is inferred from a lowresolution source; texture transfer, in which images are “texturized ” with some arbitrary source texture; artistic filters, in which various drawing and painting styles are synthesized based on scanned realworld examples; and texturebynumbers, in which realistic scenes, composed of a variety of textures, are created using a simple painting interface.
Dynamic Textures
, 2002
"... Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include seawaves, smoke, foliage, whirlwind etc. We present a novel characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing ..."
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Cited by 286 (15 self)
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Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include seawaves, smoke, foliage, whirlwind etc. We present a novel characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing dynamic textures on a firm analytical footing. We borrow tools from system identification to capture the "essence" of dynamic textures; we do so by learning (i.e. identifying) models that are optimal in the sense of maximum likelihood or minimum prediction error variance. For the special case of secondorder stationary processes, we identify the model suboptimally in closedform. Once learned, a model has predictive power and can be used for extrapolating synthetic sequences to infinite length with negligible computational cost. We present experimental evidence that, within our framework, even lowdimensional models can capture very complex visual phenomena.
Icondensation: Unifying lowlevel and highlevel tracking in a stochastic framework
, 1998
"... . Tracking research has diverged into two camps; lowlevel approaches which are typically fast and robust but provide little finescale information, and highlevel approaches which track complex deformations in highdimensional spaces but must trade off speed against robustness. Realtime highlevel ..."
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Cited by 259 (13 self)
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. Tracking research has diverged into two camps; lowlevel approaches which are typically fast and robust but provide little finescale information, and highlevel approaches which track complex deformations in highdimensional spaces but must trade off speed against robustness. Realtime highlevel systems perform poorly in clutter and initialisation for most highlevel systems is either performed manually or by a separate module. This paper presents a new technique to combine low and highlevel information in a consistent probabilistic framework, using the statistical technique of importance sampling combined with the Condensation algorithm. The general framework, which we term Icondensation, is described, and a hand tracker is demonstrated which combines colour blobtracking with a contour model. The resulting tracker is robust to rapid motion, heavy clutter and handcoloured distractors, and reinitialises automatically. The system runs comfortably in real time on an...
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
 INTERNATIONAL JOURNAL OF COMPUTER VISION
, 2002
"... This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and regionbased segmentation modules under a curvebased optimization objective function. The task of supervised texture segmentation is considered to demonst ..."
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Cited by 234 (8 self)
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This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and regionbased segmentation modules under a curvebased optimization objective function. The task of supervised texture segmentation is considered to demonstrate the potentials of the proposed framework. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multicomponent conditional probability density functions. The texture segmentation is obtained by unifying region and boundarybased information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradientdescent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and regionbased segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.
Computing geodesics and minimal surfaces via graph cuts
 in International Conference on Computer Vision
, 2003
"... Geodesic active contours and graph cuts are two standard image segmentation techniques. We introduce a new segmentation method combining some of their benefits. Our main intuition is that any cut on a graph embedded in some continuous space can be interpreted as a contour (in 2D) or a surface (in 3D ..."
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Cited by 180 (22 self)
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Geodesic active contours and graph cuts are two standard image segmentation techniques. We introduce a new segmentation method combining some of their benefits. Our main intuition is that any cut on a graph embedded in some continuous space can be interpreted as a contour (in 2D) or a surface (in 3D). We show how to build a grid graph and set its edge weights so that the cost of cuts is arbitrarily close to the length (area) of the corresponding contours (surfaces) for any anisotropic Riemannian metric. There are two interesting consequences of this technical result. First, graph cut algorithms can be used to find globally minimum geodesic contours (minimal surfaces in 3D) under arbitrary Riemannian metric for a given set of boundary conditions. Second, we show how to minimize metrication artifacts in existing graphcut based methods in vision. Theoretically speaking, our work provides an interesting link between several branches of mathematicsdifferential geometry, integral geometry, and combinatorial optimization. The main technical problem is solved using CauchyCrofton formula from integral geometry. 1.
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2000
"... 8.997> 1INTRODUCTION T HE problem of detecting and tracking moving objects has a wide variety of applications in computer vision such as coding, video surveillance, monitoring, augmented reality, and robotics. Additionally, it provides input to higher level vision tasks, such as 3D reconstruction ..."
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Cited by 180 (4 self)
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8.997> 1INTRODUCTION T HE problem of detecting and tracking moving objects has a wide variety of applications in computer vision such as coding, video surveillance, monitoring, augmented reality, and robotics. Additionally, it provides input to higher level vision tasks, such as 3D reconstruction and 3D representation. This paper addresses the problem using boundarybased information to detect and track several nonrigid moving objects over a sequence of frames acquired by a static observer. During the last decade, a large variety of motion detection algorithms have been proposed. Early approaches for motion detection rely on the detection of temporal changes. Such methods [1] employ a thresholding technique over the interframe difference, where pixelwise differences or block differences (to increase robustness) have been considered. The difference map is usually binarized using a predefined threshold value to obtain the motion/nomotion classi