This page contains selected research and class projects.

Vertical LiDAR Registration

lidar
The aim of this project is to reconstruct a virtual 3D environment by registering successive 2D laser range scans (LiDAR). Registration means computing the relative movement of the LiDAR equipment as we capture data. Currently we use an additional LiDAR/stereo camera for registration. Very soon we will extend the scenario to urban outdoor scenarios within the UCSC campus. (current work with Suresh Lodha)

Tracking GPS-Equipped Objects Using the Experts Framework

tracking
In this work, we apply an expert learning framework for predicting and learning the motion of an uncertain mobile object in real-time. We define a number of probabilistic experts, each of which predicts the future position of the object with some uncertainty and then combine the predictions of all the experts to produce an estimate of the object's location. Individual experts predictions are weighted adaptively depending on their performance. We show that this adaptive combination is powerful when there are changes in the pattern of the object's motion. We have tested our algorithm with synthetic data using uniform and non-uniform patterns as well as real data acquired using GPS equipment in presence of intermittent and highly erroneous data. (work with Suresh Lodha)

Collaborative filtering algorithms for the EachMovie dataset

I implemented a family of multiplicative algorithms which seem to be promising, in that they clearly out-perform recently developed collaborative filtering algorithms for the EachMovie dataset. The properties if these algorithms make them possible candidates for application in other domains. (research done during independent study with David Helmbold and Manfred Warmuth in Spring'02)

Dense Depth Recovery By Dynamic Programming

dense_depth
I implemented a dynamic depth recovery algorithm for stereo pairs with large occlusion. I tried to improve the results using the Mean Shift filter to smoothen images and increasing the number of ground control points by incorporating edge features. There was no significant improvement. (course project for Image Analysis and Computer Vision in Winter '03)

Sparse Disparity Computation via Plane Induced Homography

homography
When there is large camera motion, it is hard to recover robust point correspondences. However if there is a large plane in the image, we can use the homography induced by this plane to greatly improve the feature matching results. This method is particularly useful in urban environments where building faces can be used to induce homographies. (course project for Advanced Computer Graphics in Spring '03)

Classifying DNA Hairpin molecules by behaviour in a Nanopore

nanopore
DNA strands caught in an \(\alpha\)-hemolysin channel cause a blockade in the ionic current flowing through the channel. The current blockade waveform is characteristic of the type of DNA molecule caught in the channel. I applied adaBoost for classification and evaluated the ``goodness'' of the features I used. (course project for Machine Learning in Fall '01)