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99
Kernel-Based Object Tracking
, 2003
"... A new approach toward target representation and localization, the central component in visual tracking of non-rigid objects, is proposed. The feature histogram based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity fu ..."
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Cited by 900 (4 self)
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A new approach toward target representation and localization, the central component in visual tracking of non-rigid objects, is proposed. The feature histogram based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking. Keywords: non-rigid object tracking; target localization and representation; spatially-smooth similarity function; Bhattacharyya coefficient; face tracking. 1
Active Appearance Models Revisited
- International Journal of Computer Vision
, 2003
"... Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to ..."
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Cited by 462 (39 self)
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Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to an image consists of minimizing the error between the input image and the closest model instance; i.e. solving a nonlinear optimization problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show how the appearance variation can be "projected out" using this algorithm and how the algorithm can be extended to include a "shape normalizing" warp, typically a 2D similarity transformation. We evaluate our algorithm to determine which of its novel aspects improve AAM fitting performance.
Image Segmentation by Data Driven Markov Chain Monte Carlo
, 2001
"... This paper presents a computational paradigm called Data Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contributes to image segmentation in three aspects. Firstly, it designs effective and well balanced Markov Chain dynamics to exp ..."
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Cited by 277 (32 self)
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This paper presents a computational paradigm called Data Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contributes to image segmentation in three aspects. Firstly, it designs effective and well balanced Markov Chain dynamics to explore the solution space and makes the split and merge process reversible at a middle level vision formulation. Thus it achieves globally optimal solution independent of initial segmentations. Secondly, instead of computing a single maximum a posteriori solution, it proposes a mathematical principle for computing multiple distinct solutions to incorporates intrinsic ambiguities in image segmentation. A k-adventurers algorithm is proposed for extracting distinct multiple solutions from the Markov chain sequence. Thirdly, it utilizes datadriven (bottom-up) techniques, such as clustering and edge detection, to compute importance proposal probabilities, which eectively drive the Markov chain dynamics and achieve tremendous speedup in comparison to traditional jump-diffusion method[4]. Thus DDMCMC paradigm provides a unifying framework where the role of existing segmentation algorithms, such as, edge detection, clustering, region growing, split-merge, SNAKEs, region competition, are revealed as either realizing Markov chain dynamics or computing importance proposal probabilities. We report some results on color and grey level image segmentation in this paper and refer to a detailed report and a web site for extensive discussion.
The Template Update Problem
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2003
"... Template tracking is a well studied problem in computer vision which dates back to the Lucas-Kanade algorithm of 1981. Since then the paradigm has been extended in a variety of ways including: arbitrary parametric transformations of the template, and linear appearance variation. These extensions ..."
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Cited by 201 (1 self)
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Template tracking is a well studied problem in computer vision which dates back to the Lucas-Kanade algorithm of 1981. Since then the paradigm has been extended in a variety of ways including: arbitrary parametric transformations of the template, and linear appearance variation. These extensions have been combined, culminating in non-rigid appearance models such as Active Appearance Models (AAMs) and Active Blobs. One question that has received very little attention is how to update the template over time so that it remains a good model of the object being tracked. This paper proposes an algorithm to update the template that avoids the "drifting" problem of the naive update algorithm. Our algorithm can be interpreted as a heuristic to avoid local minima. It can also be extended to templates with linear appearance variation. This extension can be used to convert (update) a generic, person-independent AAM into a person specific AAM.
Equivalence and Efficiency of Image Alignment Algorithms
- In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, 2001
"... There are two major formulations of image alignment using gradient descent. The first estimates an additive increment to the parameters (the additive approach), the second an incremental warp (the compositional approach). We first prove that these two formulations are equivalent. A very efficient al ..."
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Cited by 191 (11 self)
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There are two major formulations of image alignment using gradient descent. The first estimates an additive increment to the parameters (the additive approach), the second an incremental warp (the compositional approach). We first prove that these two formulations are equivalent. A very efficient algorithm was recently proposed by Hager and Belhumeur using the additive approach that unfortunately can only be applied to a very restricted class of warps. We show that using the compositional approach an equally efficient algorithm (the inverse compositional algorithm) can be derived that can be applied to any set of warps which form a group. While most warps used in computer vision form groups, there are a certain warps that do not. Perhaps most notable is the set of piecewise affine warps used in Flexible Appearance Models (FAMs). We end this paper by extending the inverse compositional algorithm to apply to FAMs. 1
Interpreting Face Images using Active Appearance Models
, 1998
"... We demonstrate a fast, robust method of interpreting face images using an Active Appearance Model (AAM). An AAM contains a statistical model of shape and grey-level appearance which can generalise to almost any face. Matching to an image involves finding model parameters which minimise the differenc ..."
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Cited by 167 (10 self)
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We demonstrate a fast, robust method of interpreting face images using an Active Appearance Model (AAM). An AAM contains a statistical model of shape and grey-level appearance which can generalise to almost any face. Matching to an image involves finding model parameters which minimise the difference between the image and a synthesised face. We observe that displacing each model parameter from the correct value induces a particular pattern in the residuals. In a training phase, the AAM learns a linear model of the correlation between parameter displacements and the induced residuals. During search it measures the residuals and uses this model to correct the current parameters, leading to a better fit. A good overall match is obtained in a few iterations, even from poor starting estimates. We describe the technique in detail and show it matching to new face images. 1 Introduction There is currently a great deal of interest in model-based approaches to the interpretation of face images...
Statistical Models of Appearance for Medical Image Analysis and Computer Vision
- In Proc. SPIE Medical Imaging
, 2001
"... Statistical models of shape and appearance are powerful tools for interpreting medical images. We assume a training set of images in which corresponding `landmark' points have been marked on every image. From this data we can compute a statistical model of the shape variation, a model of the te ..."
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Cited by 114 (1 self)
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Statistical models of shape and appearance are powerful tools for interpreting medical images. We assume a training set of images in which corresponding `landmark' points have been marked on every image. From this data we can compute a statistical model of the shape variation, a model of the texture variation and a model of the correlations between shape and texture. With enough training examples such models should be able to synthesize any image of normal anatomy. By finding the parameters which optimize the match between a synthesized model image and a target image we can locate all the structures represented by the model. Two approaches to the matching will be described. The Active Shape Model essentially matches a model to boundaries in an image. The Active Appearance Model finds model parameters which synthesize a complete image which is as similar as possible to the target image. By using a `difference decomposition' approach the current difference between target image and synthesi...
Real-time image-based tracking of planes using efficient second-order minimization
- Proceedings of the International Conference on Intelligent Robots and Systems
, 2004
"... Abstract — The tracking algorithm presented in this paper is based on minimizing the sum-of-squared-difference between a given template and the current image. Theoretically, amongst all standard minimization algorithms, the Newton method has the highest local convergence rate since it is based on a ..."
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Cited by 102 (20 self)
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Abstract — The tracking algorithm presented in this paper is based on minimizing the sum-of-squared-difference between a given template and the current image. Theoretically, amongst all standard minimization algorithms, the Newton method has the highest local convergence rate since it is based on a second-order Taylor series of the sum-of-squareddifferences. However, the Newton method is time consuming since it needs the computation of the Hessian. In addition, if the Hessian is not positive definite, convergence problems can occur. That is why several methods use an approximation of the Hessian. The price to pay is the loss of the high convergence rate. The aim of this paper is to propose a tracking algorithm based on a second-order minimization method which does not need to compute the Hessian. I.
Face identification by fitting a 3D morphable model using linear shape and texture error functions
- in European Conference on Computer Vision
, 2002
"... Abstract This paper presents a novel algorithm aiming at analysis and identification of faces viewed from different poses and illumination conditions. Face analysis from a single image is performed by recovering the shape and textures parameters of a 3D Morphable Model in an analysis-by-synthesis fa ..."
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Cited by 77 (2 self)
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Abstract This paper presents a novel algorithm aiming at analysis and identification of faces viewed from different poses and illumination conditions. Face analysis from a single image is performed by recovering the shape and textures parameters of a 3D Morphable Model in an analysis-by-synthesis fashion. The shape parameters are computed from a shape error estimated by optical flow and the texture parameters are obtained from a texture error. The algorithm uses linear equations to recover the shape and texture parameters irrespective of pose and lighting conditions of the face image. Identification experiments are reported on more than 5000 images from the publicly available CMU-PIE database which includes faces viewed from 13 different poses and under 22 different illuminations. Extensive identification results are available on our web page for future comparison with novel algorithms. 1
