Results 1  10
of
13
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 texture ..."
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Cited by 94 (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...
Active shape models
 In Proc. British Machine Vision Conf
, 1992
"... Biomedical images usually contain complex objects, which will vary in appearance significantly from one image to another. Attempting to measure or detect ..."
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Cited by 48 (4 self)
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Biomedical images usually contain complex objects, which will vary in appearance significantly from one image to another. Attempting to measure or detect
T.: Facial feature detection using AdaBoost with shape constraints
 In: Proc. British Machine Vision Conf. (2003
"... Recently a fast and efficient face detection method has been devised [11], which relies on the AdaBoost algorithm and a set of Haar Wavelet like features. A natural extension of this approach is to use the same technique to locate individual features within the face region. However, we find that the ..."
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Cited by 29 (2 self)
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Recently a fast and efficient face detection method has been devised [11], which relies on the AdaBoost algorithm and a set of Haar Wavelet like features. A natural extension of this approach is to use the same technique to locate individual features within the face region. However, we find that there is insufficient local structure to reliably locate each feature in every image, and thus local models can give many false positive responses. We demonstrate that the performance of such feature detectors can be significantly improved by using global shape constraints. We describe an algorithm capable of accurately and reliably detecting facial features and present quantitative results on both high and low resolution image sets. 1
A comparison of shape constrained facial feature detectors
 In 6 th International Conference on Automatic Face and Gesture Recognition 2004, Seoul, Korea
, 2004
"... We consider the problem of robustly and accurately locating facial features. The relative positions of different feature points are represented using a statistical shape model. We construct an individual detector for each feature point, which is used to generate a feature response image. The quality ..."
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Cited by 22 (3 self)
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We consider the problem of robustly and accurately locating facial features. The relative positions of different feature points are represented using a statistical shape model. We construct an individual detector for each feature point, which is used to generate a feature response image. The quality of a given hypothesised shape can be evaluated quickly by combining values from each response image. We use global search to predict the approximate position of the face, then refine the hypothesis using nonlinear optimisation. The result is an algorithm capable of robustly and accurately matching a face model to new images, which we refer to as Shape Optimised Search (SOS). We describe SOS in detail and compare the performance of the algorithm when three different classes of feature detectors are used. We demonstrate that the approach is capable of outperforming the well known Active Appearance Model method. 1.
Diffeomorphic statistical shape models
 In British Machine Vision Conference
, 2004
"... We describe a method of constructing parametric statistical models of shape variation which can generate continuous diffeomorphic (nonfolding) deformation £elds. Traditional statistical shape models are constructed by analysis of the positions of a set of landmark points. Here we analyse the parame ..."
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Cited by 15 (2 self)
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We describe a method of constructing parametric statistical models of shape variation which can generate continuous diffeomorphic (nonfolding) deformation £elds. Traditional statistical shape models are constructed by analysis of the positions of a set of landmark points. Here we analyse the parameters of continuous warp £elds, constructed by composing simple parametric diffeomorphic warps. The warps are composed in such a way that the deformations are always de£ned in a reference frame. This allows the parameters controlling the deformations to be meaningfully compared from one example to another. A linear model is learnt to represent the variations in the warp parameters across the training set. This model can then be used to generalise the deformations. Models can be built either from sets of annotated points, or from unlabelled images. In the latter case, we use techniques from nonrigid registration to construct the warp £elds deforming a reference image into each example. We describe the technique in detail and give examples of the resulting models. 1
General Shape and Registration Analysis
 In
, 1997
"... The paper reviews various topics in shape analysis. In particular, matching configurations using regression is emphasized. Connections with general shape spaces and shape distances are discussed. Kendall's shape space and the affine shape space are considered in particular detail. Matching two confi ..."
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Cited by 12 (1 self)
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The paper reviews various topics in shape analysis. In particular, matching configurations using regression is emphasized. Connections with general shape spaces and shape distances are discussed. Kendall's shape space and the affine shape space are considered in particular detail. Matching two configurations and the extension to generalized matching are illustrated with applications in electrophoresis and biology. Shape distributions are briefly discussed and inference in tangent spaces is considered. Finally, some robustness and smoothing issues are highlighted. 1 Introduction The geometrical description of an object can be decomposed into registration and shape information. For example, an object's location, rotation and size could be the registration information and the geometrical information that remains is the object's shape. An object's shape is invariant under registration transformations and two objects have the same shape if they can be registered to match exactly. Depending...
Highly Resistant Shape Analysis
, 1997
"... Typical aims of shape analysis are to match or register configurations, to estimate mean shapes and to investigate shape variability. Procrustes analysis is a popular method for the shape analysis of labeled point configurations, based on a least squares criterion. We consider alternative procedures ..."
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Cited by 1 (0 self)
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Typical aims of shape analysis are to match or register configurations, to estimate mean shapes and to investigate shape variability. Procrustes analysis is a popular method for the shape analysis of labeled point configurations, based on a least squares criterion. We consider alternative procedures which are highly resistant to outlier points. In particular we consider procedures based on Sestimators, Least Median of Squares and Least Quartile Difference estimators. Practical implementation issues and relative performances are discussed in a simulation study. A procedure for mean shape estimation is also considered. We demonstrate the methodology by comparing resistant mean estimation and principal component analysis with the Procrustes procedure on a dataset of mouse vertebral landmarks and a simulated dataset. In our examples the estimated mean configurations from the resistant and Procrustes procedures were very similar, and the principal components did not differ substantially. I...
Preorganizing Shape Instances for LandmarkBased Shape Correspondence
"... Abstract The major challenge in constructing a statistical shape model for a structure is shape correspondence, which identifies a set of corresponded landmarks across a population of shape instances to accurately estimate the underlying shape variation. Both global or pairwise shapecorrespondence m ..."
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Cited by 1 (0 self)
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Abstract The major challenge in constructing a statistical shape model for a structure is shape correspondence, which identifies a set of corresponded landmarks across a population of shape instances to accurately estimate the underlying shape variation. Both global or pairwise shapecorrespondence methods have been developed to automatically identify the corresponded landmarks. For global methods, landmarks are found by optimizing a comprehensive objective function that considers the entire population of shape instances. While global methods can produce very accurate shape correspondence, they tend to be very inefficient when the population size is large. For pairwise methods, all shape instances are corresponded to a given template independently. Therefore, pairwise methods are usually very efficient. However, if the population exhibits a large amount of shape variation, pairwise methods may produce very poor shape correspondence. In this paper, we develop a new method that attempts to address the limitations of global and pairwise methods. In particular, we first construct a shape tree to globally organize the population of shape instances by identifying similar shape instance pairs. We then perform pairwise shape correspondence between
MODELLING PROFILES WITH A MIXTURE OF GAUSSIANS
"... Point Distribution Models are useful tools for modelling the variability of particular classes of shapes. A common approach is to apply a Principle Component Analysis to the data, to reduce the dimensionality of the representation. However, a single multivariate Gaussian model of the probability den ..."
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Point Distribution Models are useful tools for modelling the variability of particular classes of shapes. A common approach is to apply a Principle Component Analysis to the data, to reduce the dimensionality of the representation. However, a single multivariate Gaussian model of the probability density, estimated from the principle covariances, can be substantially inaccurate. In this paper, we examine how the specificity of a model can be improved by using a mixture of Gaussians, trained with the ExpectationMaximization algorithm, with reference to hand and vehicle profiles. 1.