Robot docking using mixtures of Gaussians

This paper applies the Mixture of Gaussians probabilistic model, combined with Expectation Maximization optimization to the task of summarizing three dimensionals range data for the mobile robot. This provides a flexible way of dealing with uncertainties in sensor information, and allows the introduction of prior knowledge into low-level perception modules. Problems with the basic approach were solved in several ways: the mixture of Gaussians was reparameterized to reflect the types of objects expected in the scene, and priors on model parameters were included in the optimization process. Both approaches force the optimization to find 'interesting' objects, given the sensor and object characteristics. A higher level classifier was used to interpret the results provided by the model, and to reject spurious solutions

Analysis of Three-Dimensional Protein Images

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.Comment: See http://www.jair.org/ for any accompanying file

Machine vision: a survey

This paper surveys the field of machine vision from a computer science perspective. It is written to act as an introduction to the field and presents the reader with references to specific implementations. Machine vision is a complex and developing field that can be broken into the three stages: stereo correspondence, scene reconstruction, and object recognition. We present the techniques and general approaches to each of these stages and summarize the future direction of research

Modeling architecture from photographs : an implementation of the Façade system

The Façade photometric modeling system, developed by Paul E Debevec at Berkley, is capable of transforming a sparse set of camera images of an architectural scene into a photorealistic 3D model. Users define a rough model out of primitive building blocks and mark where a portion of the edges of the building blocks exist in the images. Façade then optimizes the parameters of those building blocks to create a model that best fits the images and the user input. This paper details my own implementation of the photometric modeling portion of Façade