Now showing items 1-6 of 6
Statistical and geometric methods for visual tracking with occlusion handling and target reacquisition
(Georgia Institute of Technology, 2012-01-17)
Computer vision is the science that studies how machines understand scenes and automatically make decisions based on meaningful information extracted from an image or multi-dimensional data of the scene, like human vision. ...
Dynamic curve estimation for visual tracking
(Georgia Institute of Technology, 2010-08-03)
This thesis tackles the visual tracking problem as a target contour estimation problem in the face of corrupted measurements. The major aim is to design robust recursive curve filters for accurate contour-based tracking. ...
Localized statistical models in computer vision
(Georgia Institute of Technology, 2009-09-14)
Computer vision approximates human vision using computers. Two subsets are explored in this work: image segmentation and visual tracking. Segmentation involves partitioning an image into logical parts, and tracking analyzes ...
Target tracking using residual vector quantization
(Georgia Institute of Technology, 2011-11-18)
In this work, our goal is to track visual targets using residual vector quantization (RVQ). We compare our results with principal components analysis (PCA) and tree structured vector quantization (TSVQ) based tracking. This ...
Robust target localization and segmentation using statistical methods
(Georgia Institute of Technology, 2010-04-05)
This thesis aims to contribute to the area of visual tracking, which is the process of identifying an object of interest through a sequence of successive images. The thesis explores kernel-based statistical methods, which ...
Global Optimizing Flows for Active Contours
(Georgia Institute of Technology, 2007-07-09)
This thesis makes significant contributions to the object detection problem in computer vision. The object detection problem is, given a digital image of a scene, to detect the relevant object in the image. One technique ...