Results 1  10
of
14
Groupwise construction of appearance models using piecewise affine deformations
 in Proceedings of 16 th British Machine Vision Conference
, 2005
"... We describe an algorithm for obtaining correspondences across a group of images of deformable objects. The approach is to construct a statistical model of appearance which can encode the training images as compactly as possible (a Minimum Description Length framework). Correspondences are defined by ..."
Abstract

Cited by 26 (10 self)
 Add to MetaCart
We describe an algorithm for obtaining correspondences across a group of images of deformable objects. The approach is to construct a statistical model of appearance which can encode the training images as compactly as possible (a Minimum Description Length framework). Correspondences are defined by piecewise linear interpolation between a set of control points defined on each image. Given such points a model can be constructed, which can approximate every image in the set. The description length encodes the cost of the model, the parameters and most importantly, the residuals not explained by the model. By modifying the positions of the control points we can optimise the description length, leading to good correspondence. We describe the algorithm in detail and give examples of its application to MR brain images and to faces. We also describe experiments which use a recentlyintroduced specificity measure to evaluate the performance of different components of the algorithm. 1
C.J.: Computing accurate correspondences across groups of images
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2010
"... Abstract—Groupwise image registration algorithms seek to establish dense correspondences between sets of images. Typically they involve iteratively improving the registration between each image and an evolving mean. A variety of methods have been proposed, which differ in their choice of objective f ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
Abstract—Groupwise image registration algorithms seek to establish dense correspondences between sets of images. Typically they involve iteratively improving the registration between each image and an evolving mean. A variety of methods have been proposed, which differ in their choice of objective function, representation of deformation field and optimisation methods. Given the complexity of the task, the final accuracy is significantly affected by the choices made for each component. Here we present a groupwise registration algorithm which can take advantage of the statistics of both the image intensities and the range of shapes across the group to achieve accurate matching. By testing on large sets of images (in both 2D and 3D), we explore the effects of using different image representations and different statistical shape constraints. We demonstrate that careful choice of such representations can lead to significant improvements in overall performance. Index Terms—Nonrigid registration, correspondence problem, appearance models 1
N.: Computing minimal deformations: Application to construction of statistical shape models
 In: Computer Vision and Pattern Recognition
, 2008
"... Nonlinear registration is mostly performed after initialization by a global, linear transformation (in this work, we focus on similarity transformations), computed by a linear registration method. For the further processing of the results, it is mostly assumed that this preregistration step complete ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
Nonlinear registration is mostly performed after initialization by a global, linear transformation (in this work, we focus on similarity transformations), computed by a linear registration method. For the further processing of the results, it is mostly assumed that this preregistration step completely removes the respective linear transformation. However, we show that in deformable settings, this is not the case. As a consequence, a significant linear component is still existent in the deformation computed by the nonlinear registration algorithm. For construction of statistical shape models (SSM) from deformations, this is an unwanted property: SSMs should not contain similarity transformations, since these do not capture information about shape. We propose a method which performs an a posteriori extraction of a similarity transformation from a given nonlinear deformation field, and we use the processed fields as input for SSM construction. For computation of minimal displacements, a closedform solution minimizing the squared Euclidean norm of the displacement field subject to similarity parameters is used. Experiments on real intersubject data and on a synthetic example show that the theoretically justified removal of the similarity component by the proposed method has a large influence on the shape model and significantly improves the results. 1.
The Morphlet Transform: A Multiscale Representation for Diffeomorphisms
"... We describe a multiscale representation for diffeomorphisms. Our representation allows synthesis – e.g. generate random diffeomorphisms – and analysis – e.g. identify the scales and locations where the diffeomorphism has behavior that would be unpredictable based on its coarsescale behavior. Our re ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
We describe a multiscale representation for diffeomorphisms. Our representation allows synthesis – e.g. generate random diffeomorphisms – and analysis – e.g. identify the scales and locations where the diffeomorphism has behavior that would be unpredictable based on its coarsescale behavior. Our representation has a forward transform with coefficients that are organized dyadically, in a way that is familiar from wavelet analysis, and an inverse transform that is nonlinear, and generates true diffeomorphisms when the underlying object satisfies a certain sampling condition. Although both the forward and inverse transforms are nonlinear, it is possible to operate on the coefficients in the same way that one operates on wavelet coefficients; they can be shrunk towards zero, quantized, and can be randomized; such procedures are useful for denoising, compressing, and stochastic simulation. Observations include: (a) if a template image with edges is morphed by a complex but known transform, compressing the morphism is far more effective than compressing the morphed image. (b) One can create random morphisms with and desired selfsimilarity exponents by inverse transforming scaled Gaussian noise. (c) Denoising morpishms in a sense smooths the underlying level sets of the object. 1
Expression Compensation for Face Recognition Using a Polar Geodesic Representation
 In Proc. 3rd International Symposium on 3D Data Processing, Visualization, Transmission
"... This paper addresses the problem of face recognition in the presence of deformation due to expressions. Facial motion is considered as an isometric mapping between surfaces, which allows preservation of geodesic distance between facial points. Subject to this assumption, we propose a novel technique ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
This paper addresses the problem of face recognition in the presence of deformation due to expressions. Facial motion is considered as an isometric mapping between surfaces, which allows preservation of geodesic distance between facial points. Subject to this assumption, we propose a novel technique which integrates 2D and 3D information to establish a new deformation invariant representation of the face. The 3D surface is used to define a polar geodesic coordinate space which is independent of the intrinsic geometry of the face. The corresponding color image is embedded in this space and serves as the recognition data. A preceding stage of hierarchical diffeomorphic gridbased image warping before the recognition process combined with a lip segmentation technique are also employed to increase the overall performance of the algorithm.
Local Shape Modelling Using Warplets
, 2005
"... We develop a statistical shape model for the analysis of local shape variation. In particular, we consider models of shapes that exhibit selfsimilarity along their contours such as fractal and space filling curves. ..."
Abstract
 Add to MetaCart
We develop a statistical shape model for the analysis of local shape variation. In particular, we consider models of shapes that exhibit selfsimilarity along their contours such as fractal and space filling curves.
Analysis of Surfaces Using Constrained Regression Models
"... Abstract. We present a study of the relationship between the changes in the shape of the human ear due to jaw movement and acoustical feedback (AF) in hearing aids. In particular, we analyze the deformation field of the outer ear associated with the movement of the mandible (jaw bone) to understand ..."
Abstract
 Add to MetaCart
Abstract. We present a study of the relationship between the changes in the shape of the human ear due to jaw movement and acoustical feedback (AF) in hearing aids. In particular, we analyze the deformation field of the outer ear associated with the movement of the mandible (jaw bone) to understand its effect on AF and identify local regions that play a significant role. Our data contains ear impressions of 42 hearing aid users, in two different positions: open and closed mouth, and survey data including information about experienced discomfort due to AF. We use weighted support vector machines (WSVM) to investigate the separation between the presence and lack of AF and achieve classification accuracy of 80 % based on the deformation field. To robustly localize the regions of the deformation field that significantly contribute to AF we employ logistic regression penalized with elastic net (EN). By visualizing the selected variables on the mean surface, we provide clinical interpretations of the results. 1
Proof of Property 1 – Overlapping Property
"... This appendix introduces the proofs of Property 1 and 2 related to the discretization scheme; and a new compact kernel that we use throughout our method. Notation. Matrix are in upper case bold (e.g. A) and vector in lower case bold (e.g. a). We consider two cameras: a source S and a target T. □ (a, ..."
Abstract
 Add to MetaCart
This appendix introduces the proofs of Property 1 and 2 related to the discretization scheme; and a new compact kernel that we use throughout our method. Notation. Matrix are in upper case bold (e.g. A) and vector in lower case bold (e.g. a). We consider two cameras: a source S and a target T. □ (a, b) ⊂ R 2 is the quad formed by the two points a ∈ R 2 and b ∈ R 2. o □(a,b) ∈ R 2 is the center of the quad □ (a, b) and c □(a,b) ∈ R 2×4 its four corners. E is the set of Essential Matrices [2]; it is a variety of dimension N (N ≤ 5) in R 9 (the set of 3 × 3 matrices). A. Overlapping properties As a reminder we redefine the relative rotation R (u) and translation t (u) cos (π − θ − α) 0 sin (π − θ − α) R (u) = ⎣ 0 1 0 ⎦, ⎡ − sin (π⎤ − θ − α) 0 cos (π − θ − α) sin (θ) t (u) = ⎣ 0 ⎦. cos (θ) (1) Property 1 Overlapping Property – See supplementary material for Proof ψ (u) = 0 ⇔ π + γS + γT < θ + α < 3π − γS − γT with γS (resp. γT) half the field of view of the source camera (resp. target). ψ (u) = 0 means no overlap.
Construction of Statistical Shape Models from Minimal Deformations
"... Abstract. Statistical shape models (SSM) capture the variation of shape across a population, in order to allow further analysis. Previous work demonstrates that deformation fields contain global transformation components, even if global preregistration is performed. It is crucial to construction of ..."
Abstract
 Add to MetaCart
Abstract. Statistical shape models (SSM) capture the variation of shape across a population, in order to allow further analysis. Previous work demonstrates that deformation fields contain global transformation components, even if global preregistration is performed. It is crucial to construction of SSMs to remove these global transformation components from the local deformations thus obtaining minimal deformations prior to using these as input for SSM construction. In medical image processing, parameterized SSMs based on control points of freeform deformations (FFD) are a popular choice, since they offer several advantages compared to SSMs based on dense deformation fields. In this work, we extend the previous approach by presenting a framework for construction of both, unparameterized and FFDbased SSMs from minimal deformations. The core of the method is computation of minimal deformations by extraction of the linear part from the original dense deformations. For FFDbased SSMs, the FFDparameterization of the minimal deformations is performed by projection onto the space of FFDs. Both steps are computed by closeform solutions optimally in the leastsquare sense. The proposed method is evaluated on a data set of 62 MR images of the corpus callosum. The results show a significant improvement achieved by the proposed method for SSMs built on dense fields, as well as on FFDbased SSMs. 1