We discuss how to automatically obtain the metric calibration of an ad-hoc network of cameras with no centralized processor. We model the set of uncalibrated cameras as nodes in a communication network, and propose a distributed algorithm in which each camera performs a local, robust bundle adjustment over the camera parameters and scene points of its neighbors in an overlay “vision graph”. We analyze the performance of the algorithm on both simulated and real data, and show that the distributed algorithm results in a fairer allocation of messages per node while achieving comparable calibration accuracy to centralized bundle adjustment.

Wedding is one of the most important ceremonies in our lives. It symbolizes the birth and creation of a new family. In this paper, we present a system for automatically segmenting a wedding ceremony video into a sequence of recognized wedding events, e.g., the couple’s wedding kiss. Our goal is to develop an automatic tool for users to efficiently organize, search, and retrieve his/her treasured wedding memories. Furthermore, the event descriptions could benefit and complement the current research in semantic video understanding. Technically, three kinds of event features, i.e., the speech/music discriminator, flashlight detector, and bride indicator, are exploited to build statistical models for each wedding event. Events are then recognized by a hidden Markov model, which takes into account both the fitness of observed features and the temporal rationality of event ordering to improve the segmentation accuracy. We conducted experiments on a rich set of wedding videos, and the results demonstrate the effectiveness of our approach.

A metamorphic computer virus generates copies of itself using code morphing techniques. A new virus has the same functionality as the parent but it has a different internal structure. The goal of the metamorphic virus writer is to produce viral copies that have no common signature. If the viral copies are sufficiently different, they can evade signature detection, which is the most widely-used anti-virus technique. In previous research, hidden Markov models (HMMs) have been used to detect some metamorphic viruses. However, recent research has shown that it is possible for carefully designed metamorphic viruses to evade HMM-based detection. In this project, we analyze similarity-based techniques for detecting metamorphic viruses. We first consider a similarity index technique that was previously studied. We then consider new similarity techniques based on edit distance and pairwise sequence alignment. We test these similarity measures on the challenging problem of metamorphic virus detection. We compare our detection results with those obtained using an HMM-based detection method. iv ACKNOWLEDGEMENTS I would like to express my deep and sincere gratitude to my project advisor, Dr. Mark Stamp for