Active Learning with Irrelevant Examples (2008)
Dominic Mazzoni, Kiri L. Wagstaff, Michael C. Burl
Abstract. Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled...
Active Learning with Irrelevant Examples (2008)
Dominic Mazzoni, Kiri L. Wagstaff, Michael C. Burl
Abstract. Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled...
Recognition of visual object classes (2008)
Humans can look at a scene or a photograph and easily recognize objects. Outside my window I can see cars, people walking a dog on a brick pathway, trees, buildings, etc. This perception is so...
Dennis Decoste, Michael C. Burl, Alan Hopkins, Nathan S. Lewis
Kernel methods provide a promising new family of algorithms for machine learning and data mining applications. In particular, kernel-based nonlinear classiers such as support vector machines (SVMs)...
Automated knowledge discovery from simulators (2007)
Burl, Michael C., DeCoste, D., Enke, B.L., Mazzoni, D., Merline, W.J., Scharenbroich, L.
In this paper, we explore one aspect of knowledge discovery from simulators, the landscape characterization problem, where the aim is to identify regions in the input/ parameter/model space that lead...
Algorithms for Optimal Processing of Polarimetric Radar Data (2005)
Novak, Leslie M., Sechtin, Michael B., Burl, Michael C.
This report describes algorithms that make optimal use of polarimetric radar information to detect and classify targets in a ground clutter background. The optimal polarimetric detector (OPD) is...
Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth (2004)
Smyth, Padhraic, Burl, Michael C., Fayyad, Usama M., Perona, Pietro
This paper discusses the problem of knowledge discovery in image databases with particular focus on the issues which arise when absolute ground truth is not available.
Distortion-invariant recognition via jittered queries (2000)
Dennis Decoste, Michael C. Burl
This paper presents a new approach for achieving distortion-invariant recognition and classification. A test example to be classified is viewed as a query intended to find similar examples in the...
Distortion-invariant recognition via jittered queries (2000)
Dennis Decoste, Michael C. Burl
This paper presents a new approach for achieving distortion-invariant recognition and classication. A test example to be classied is viewed as a query intended to nd similar examples in the training...
Diamond Eye: A distributed architecture for image data mining (1999)
Michael C. Burl, Charless Fowlkes, Joe Roden, Andre Stechert, Saleem Mukhtar
Diamond Eye is a distributed software architecture that enables users (scientists) to analyze large image collections by interacting with one or more custom data mining servers via a Java applet...
Mining for Image Content (1999)
Michael C. Burl, Charless Fowlkes, Joseph Roden
This paper provides an overview of our eorts to develop algorithms and systems that are able to \mine" useful information from large image collections. One of the core capabilities targeted is...
Probabilistic Affine Invariants for Recognition (1998)
Thomas K. Leung, Michael C. Burl, Pietro Perona
Under a weak perspective camera model, the image plane coordinates in different views of a planar object are related by an affine transformation. Because of this property, researchers have attempted...
Learning to Recognize Volcanoes on Venus (1998)
Michael C. Burl, Lars Asker, Padhraic Smyth, Usama Fayyad, Pietro Perona, Larry Crumpler
. Dramatic improvements in sensor and image acquisition technology have created a demand for automated tools that can aid in the analysis of large image databases. We describe the development of...
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry (1998)
Michael C. Burl, Markus Weber, Pietro Perona
. Many object classes, including human faces, can be modeled as a set of characteristic parts arranged in a variable spatial configuration. We introduce a simplified model of a deformable object...
Probabilistic Affine Invariants for Recognition (1998)
Thomas Leung Michael, Michael C. Burl, Pietro Perona
Under a weak perspective camera model, the image plane coordinates in different views of a planar object are related by an affine transformation. Because of this property, researchers have attempted...
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry (1998)
Michael C. Burl, Markus Weber, Pietro Perona
. Many object classes, including human faces, can be modeled as a set of characteristic parts arranged in a variable spatial configuration. We introduce a simplified model of a deformable object...
Probabilistic Affine Invariants for Recognition (1998)
Thomas Leung, Michael C. Burl, Pietro Perona
Under a weak perspective camera model, the image plane coordinates in different views of a planar object are related by an affine transformation. Because of this property, researchers have attempted...
A probabilistic approach to object recognition using local photometry and global geometry (1998)
Michael C. Burl, Markus Weber, Pietro Perona
Abstract. Many object classes, including human faces, can be modeled as a set of characteristic parts arranged in a variable spatial con guration. We introduce a simpli ed model of a deformable...
Recognition of visual object classes (1996)
Humans can look at a scene or a photograph and easily recognize objects. Outside my window I can see cars, people walking a dog on a brick pathway, trees, buildings, etc. This perception is so...