We address the problem of non-parametric multi-modal image matching. We propose a generic framework which relies on a global variational formulation and show its versatility through three different multi-modal registration methods : supervised registration by joint intensity learning, maximization of the mutual information and maximization of the correlation ratio. Regularization is performed by using a functional borrowed from linear elasticity theory. We also consider a geometry-driven regularization method. Experiments on synthetic images and preliminary results on the realignment of MRI datasets are presented.

We formulate a deformable template model for objects with a clearly defined mechanism for parameter estimation. A separate model is estimated for each class, and classification is likelihood based- no discrmination boundaries are learned. Nonethe-less high classification rates are achieved with small training samples. The data models are defined on binary oriented edge features that are highly robust to photometric vari-ation and small local deformations. The deformation of an object is defined in terms of locations of a moderate number reference points. Each reference point is associated with a part- a probability map assigning a probability for each edge type at each pixel in a window. The likelihood of the edge data on the entire image conditional on the deformation is described as a patchwork of parts (POP) model- the edges are assumed conditionally independent, and the marginal at each pixel is obtained by a patchwork operation: averaging the marginal probabilities contributed by each part covering the pixel. Object classes are modeled as mixtures of POP models that are discovered se-quentially as more class data is observed. Experiments are presented on the MNIST database, hundreds of deformed LATEX shapes, reading zipcodes, and face detection. 1

This paper describes an approach whereby comparametric analysis is used in jointly registering image pairs in their domain and range, i.e., in their spatial coordinates and pixel values, respectively. This is accomplished by approximating a camera's nonlinear comparametric function with a constrained piecewise linear one. The optimal fitting of this approximation to comparagram data is then used in a re-parameterized version of the camera's comparametric function to estimate the exposure difference between images. Doing this allows the inherently nonlinear problem of joint domain and range registration to be performed using a computationally attractive least squares formalism. The paper first presents the range registration process and then describes the strategy for performing the joint registration. The models used allow for the pair-wise registration of images taken from a camera that can automatically adjust its exposure as well as tilt, pan, rotate and zoom about its optical center. Results concerning the joint registration as well as range-only registration are provided to demonstrate the method's effectiveness.

Stereotactic neurosurgery for Parkinson's disease consists of stimulating deep nuclei of the brain. Although target coordinates are calculated with high precision on the pre-operative images, cerebrospinal fluid (CSF) leakage during the procedure can lead to a brain deformation and cause potential error with respect to the surgical planning.

Stereotactic neurosurgery for Parkinson's disease consists of stimulating deep nuclei of the brain. Although target coordinates are available with high accuracy on the pre-operative Magnetic Resonance Images (MRI), the leakage of cerebrospinal fluid (CSF) during the procedure may lead to a brain deformation and may cause localisation errors with respect to the surgical planning. In this paper, we propose a patient-specific biomechanical model of the brain able to recover its global deformation during this type of neurosurgical procedure. Such a model could be used to update the pre-operative planning and to predict the mechanical effects of the intra-operative brain shift.

Abstract—We consider the problem of registering two observations on an arbitrary object, where the two are related by a geometric affine transformation of their coordinate systems, and by a non-linear mapping of their intensities. More generally, the framework is that of jointly estimating the geometric and radiometric deformations relating two observations on the same object. We show that the original high-dimensional, non-linear, non-convex search problem of simultaneously recovering the geometric and radiometric deformations can be represented by an equivalent sequence of two linear systems. A solution of this sequence yields an exact, explicit, and efficient solution to the joint estimation problem. Index Terms—Affine transformations, image registration, linear estimation, parameter estimation, domain registration, nonlinear range registration. I.