Student Talks
Multiple Prediction Learning: Learning to Optimize Sequences via Submodular Maximization
Event Location: NSH 1305Abstract: Increasingly, real world problems require multiple predictions. For instance in advertisement placement on the web, a list of advertisements is placed on a page with the objective of maximizing click- through rate. Traditionally machine learning has focused on producing a single best prediction. More generally, in this thesis, we build an [...]
Pose Inference Machines: Efficient and Accurate Human Pose Estimation from Images
Event Location: GHC 8102Abstract: Fast and accurate human pose estimation enables a wide spectrum of applications from interactive control, markerless motion capture and gesture recognition to providing rich semantic information for higher level vision tasks such as scene understanding. The goal of this work is to develop an accurate and real-time human pose estimation system [...]
Handheld Micromanipulator for Robot-Assisted Microsurgery
Event Location: NSH 3305Abstract: Robot-assisted surgery has been increasingly adopted in a wide variety of surgical applications because it offers fine manipulation with high precision and dexterity. Despite the commercial success of robotic platforms, practical use in microsurgery is still challenging due to a considerable level of accuracy required at sub-millimeter scales. Limited visualization and [...]
Perceptually Valid Dynamics for Smiles and Blinks
Event Location: GHC 8102Abstract: Human observers are adept at detecting anomalies in realistic computer-generated (CG) facial animations. With an increased demand for CG characters in education and entertainment applications, it is important to animate accurate, realistic facial expressions. In this thesis, we develop a framework to explore representations of two key facial expressions: blinks and [...]
Design and Control of a Flapping Flight Micro Aerial Vehicle
Event Location: NSH 3305Abstract: Miniature flapping flight systems hold great promise in matching the agility of their natural counterparts, bees, flies, and hummingbirds. Characterized by reciprocating wing motion, unsteady aerodynamics, and the ability to hover, insect-like flapping flight presents an interesting locomotion strategy capable of functioning at small size scales and is still a current [...]
Spatial, Temporal and Spatio-temporal Correspondence for Computer Vision Problems
Event Location: GHC 2109Abstract: Many computer vision problems, such as object classification, motion estimation or shape registration rely on solving the correspondence problem. Existing algorithms to solve spatial or temporal correspondence problems are usually NP-hard, difficult to approximate, lack flexible models and mechanism for feature weighting. This proposal addresses the correspondence problem in computer vision, [...]
State Estimation for Humanoid Robots
Event Location: GHC 8102Abstract: This proposal focuses on dynamic model based state estimation for hydraulic humanoid robots. The goal is to produce state estimates that are robust and achieve good performance when combined with the controller. Three issues are addressed in this proposal. 1. How to handle modelling error using state estimation? 2. How to [...]
Dynamic Model Formulation and Calibration for Wheeled Mobile Robots
Event Location: NSH 1507Abstract: Advances in hardware design have made wheeled mobile robot(WMRs) exceptionally mobile. To fully exploit this mobility, WMR planning, control,and estimation systems require motion models that are fast and accurate. Much of the published theory on WMR modeling is limited to 2D or kinematics, but 3D dynamic (or force-driven) models are required [...]
Bayesian Aggregation of Evidence for Detection and Characterization of Patterns in Multiple Noisy Observations
Event Location: NSH 3305Abstract: Effective use of Machine Learning to support extracting maximal information from limited sensor data is one of the important research challenges in robotic sensing. This thesis develops techniques for detecting and characterizing patterns in noisy sensor data. Our Bayesian Aggregation (BA) algorithmic framework can leverage data fusion from multiple low Signal-To-Noise [...]