An Approach to Robust ICP Initialization.

In this note, we propose an approach to initialize the Iterative Closest Point (ICP) algorithm to match unlabelled point clouds related by rigid transformations. The method is based on matching the ellipsoids defined by the points' covariance matrices and then testing the various principal half-axes matchings that differ by elements of a finite reflection group. We derive bounds on the robustness of our approach to noise and numerical experiments confirm our theoretical findings.

Asymmetric correlation: a noise robust similarity measure for template matching.

We present an efficient and noise robust template matching method based on asymmetric correlation (ASC). The ASC similarity function is invariant to affine illumination changes and robust to extreme noise. It correlates the given non-normalized template with a normalized version of each image window in the frequency domain.

A probabilistic approach to pattern matching in the continuous domain.

The goal of this paper is to solve the following basic problem: Given discrete noisy samples from a continuous signal, compute the probability distribution of its distance from a fixed template. As opposed to the typical restoration problem, which considers a single optimal signal, the computation of the entire probability distribution necessitates integrating over the entire signal space. To achieve this, we apply path integration techniques.

Noniterative exact solution to the phase problem in optical imaging implemented with scanning probe microscopy.

An object is characterized by its amplitude and phase. However, when acquiring optical data about such an object, using a recording medium such as a camera, phase information is lost. Crystallography experienced a breakthrough in phase retrieval for large molecular entities by Max Perutz's introduction of "heavy atoms" using the method of isomorphous replacement.

Robust real-time pattern matching using bayesian sequential hypothesis testing.

This paper describes a method for robust real time pattern matching. We first introduce a family of image distance measures, the "Image Hamming Distance Family". Members of this family are robust to occlusion, small geometrical transforms, light changes and non-rigid deformations.

How to put probabilities on homographies.

We present a family of "normal" distributions over a matrix group together with a simple method for estimating its parameters. In particular, the mean of a set of elements can be calculated. The approach is applied to planar projective homographies, showing that using priors defined in this way improves object recognition.