Building and Testing a Statistical Shape Model of the Human Ear Canal

Abstract. Today the design of custom in-the-ear hearing aids is based on personal experience and skills and not on a systematic description of the variation of the shape of the ear canal. In this paper it is described how a dense surface point distribution model of the human ear canal is built based on a training set of laser scanned ear impressions and a sparse set of anatomical landmarks placed by an expert. The landmarks are used to warp a template mesh onto all shapes in the training set. Using the vertices from the warped meshes, a 3D point distribution model is made. The model is used for testing for gender related differences in size and shape of the ear canal.

Free-Form Surface Signatures: A representation scheme for Object Registration and Recognition

This Paper Presents A New Concept For 3-D Free-Form Surface Registration And Object Recognition Using A Novel Surface Representation Scheme. This Representation Scheme Captures The 3-D Curvature Information Of Any Free-Form Surface And Encodes It Into A 2-D Image Corresponding To A Certain Point On The Surface. This Image Is Unique For This Point And Is Independent From The Object Translation Or Orientation In Space. For This Reason We Called This Image "Surface Point Signature" (Sps). This Scheme Can Be Used As A Global Representation Of The Surface As Well As A Local One And Also In A Scale Independent Surface Matching. It Performs Faster Registration Than Existing Registration Approaches. Applications Presented Include Object Registration, Multimodal Medical Image Registration And The Recognition Of Multiple Objects In A 3-D Scene

Deformable Registration of Cortical Structures Via Hybrid Volumetric and Surface Warping

This paper presents a method of deformable registration of cortical structures across individuals, using hybrid volumetric and surface warping. The proposed method uses two steps. In the first step, a HAMMER-based volumetric registration algorithm warps the model surface to the individual’s space. In the second step, an attribute-based surface registration method further refines the results of the volumetric warping. An attribute vector is defined for each vertex on the cortical surface, and used to capture the local and global geometric features of the surface patch. The attribute vector is designed to be as distinctive as possible, so that each vertex on the model surface can find its correspondence on the individual surface. Experimental results on synthesized and real brain data are provided to demonstrate the performance of the proposed method in registering cortical structures across individuals

A Scatter Search Algorithm for the 3D Image Registration Problem

Abstract. Image registration has been a very active research area in the computer vision community. In the last few years, there is an increasing interest on the application of Evolutionary Computation in this field and several evolutionary approaches have been proposed obtaining promising results. In this contribution we introduce the use of an advanced evolutionary algorithm, Scatter Search, to solve the 3D image registration problem. The new proposal will be validated using two different shapes (both synthetic and MRI), considering three different transformations for each of them, and testing its performance with a Basic Memetic Algorithm and the classical, problem-specific ICP algorithm.