Overview JHU/APL at TREC 2004: Robust and Terabyte Tracks (2008)
Christine Piatko, James Mayfield, Paul Mcnamee, Scott Cost
For initial ranked retrieval, we continue to use a statistical language model to compute query/document similarity values. Hiemstra and de Vries [3] describe such a linguistically motivated...
The Johns, James Mayfield, Paul Mcnamee, Christine Piatko
the TREC Category A evaluation. The focus of our information retrieval research this year has been on the relative value of and interaction among multiple term types and multiple similarity metrics....
JHU/APL at TREC 2002: Experiments in Filtering and Arabic Retrieval (2008)
Paul Mcnamee, Christine Piatko, James Mayfield
Laboratory (JHU/APL) participated in two tracks at this year’s conference. We participated in the filtering track, again addressing the batch and routing subtasks, as well as the adaptive task for...
Approach The Hopkins Automated Information Retriever for (2007)
James Mayfield, Paul Mcnamee, Christine Piatko
the TREC Category A evaluation. The focus of our information retrieval research this year has been on the relative value of and interaction among multiple term types and multiple similarity metrics....
The Analysis of a Simple (2007)
Means Clustering Algorithm, Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine Piatko, Angela Y. Wu
K-means clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points...
Means Clustering Algorithm, Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine Piatko, Angela Y. Wu
K-means clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points...
Localizing an Object With Finger Probes (2007)
Robert Freimer Samir, Samir Khuller, Christine Piatko, Kathleen Romanik, Diane Souvaine
We consider the problem of identifying one of a set of polygonal models in the plane using point probes and finger probes. In particular, we give strategies for using a minimum number of finger...
Paul Mcnamee, Christine Piatko, James Mayfield
this year's conference. We participated in the filtering track, again addressing the batch and routing subtasks, as well as the adaptive task for the first time. We 'also continued...
David M. Mount, Nathan S. Netanyahu, Christine Piatko, Angela Y. Wu
Given a set P of n points in R d, a fundamental problem in computational geometry is concerned with finding the smallest enclosing "range " of P. Well known instances of this...
Named Entity Recognition Using Hundreds of Thousands of Features (2003)
James Mayfield, Paul Mcnamee, Christine Piatko
We present an approach to named entity recognition that uses support vector machines to capture transition probabilities in a lattice. The support vector machines are trained with hundreds of...
Named Entity Recognition Using Hundreds of Thousands of Features (2003)
James Mayfield, Paul Mcnamee, Christine Piatko
We present an approach to named entity recognition that uses support vector machines to capture transition probabilities in a lattice. The support vector machines are trained with hundreds of...
The haircut system at trec-9 (2001)
Paul Mcnamee, James Mayfield, Christine Piatko
HAIRCUT benefits from a basic design decision to support flexibility throughout the system. One specific example of this is the way we represent documents and queries; words, stemmed words, character...
JHU/APL at TREC 2001: Experiments in filtering and in Arabic, video, and web retrieval (2001)
James Mayfield, Paul Mcnamee, Cash Costello, Christine Piatko, Amit Banerjee
The outsider might wonder whether, in its tenth year, the Text Retrieval Conference would be a moribund workshop encouraging little innovation and undertaking few new challenges, or whether fresh...
Approximating Large Convolutions in Digital Images (2001)
David M. Mount, Tapas Kanungo, Nathan S. Netanyahu, Christine Piatko, Ruth Silverman, Angela Y. Wu
Computing discrete two-dimensional convolutions is an important problem in image processing. In mathematical morphology, an important variant is that of computing binary convolutions, where the...
The HAIRCUT System at TREC-9 (2001)
Paul Mcnamee James, James Mayfield, Christine Piatko
ream. The text was lowercased, punctuation was removed, and diacritical marks were retained. Tokens containing digits were preserved; however only the first two of a sequence of digits were retained...
JHU/APL at TREC 2001: Experiments in Filtering and in Arabic, (2001)
Video And Web, James Mayfield, Paul Mcnamee, Cash Costello, Christine Piatko, Amit Banerjee
this paper mainly reports our initial findings
An Efficient k-Means Clustering Algorithm: Analysis and Implementation (2000)
Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine Piatko, Ruth Silverman, Angela Y. Wu
K-means clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points...
Computing Nearest Neighbors for Moving Points and Applications to Clustering (1999)
Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine Piatko, Ruth Silverman, Angela Y. Wu
Introduction Clustering is an important problem, with applications in areas such as data mining and knowledge discovery [6], data compression and vector quantiation [8], and pattern recognition and...
Computing Nearest Neighbors for Moving Points and Applications to Clustering (1999)
Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine Piatko
Introduction Clustering is an important problem, with applications in areas such as data mining and knowledge discovery [6], data compression and vector quantization [8], and pattern recognition and...
Computing nearest neighbors for moving points and applications to clustering (1999)
Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine Piatko, Ruth Silverman, Angela Y. Wu
Clustering is an important problem, with applications in areas such as data mining and knowledge discovery [6], data compression and vector quantiation [8], and pattern recognition and pattern...
Approximating Large Convolutions in Digital Images (1998)
Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine Piatko, Ruth Silverman, Angela Y. Wu
Computing discrete two-dimensional convolutions is an important problem in image processing. In mathematical morphology, an important variant is that of computing binary convolutions, where the...
Approximating Large Convolutions in Digital Images (1998)
Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine Piatko, Angela Y. Wu
Computing discrete two-dimensional convolutions is an important problem in image processing. In mathematical morphology, an important variant is that of computing binary convolutions, where the...
Approximating large convolutions in digital images (1998)
David M. Mount, Tapas Kanungo, Nathan S. Netanyahu, Christine Piatko, Ruth Silverman, Angela Y. Wu, ...
Abstract—Computing discrete two-dimensional (2-D) convolutions is an important problem in image processing. In mathematical morphology, an important variant is that of computing binary...
Approximating large convolutions in digital images (1998)
David M. Mount, Tapas Kanungo, Nathan S. Netanyahu, Christine Piatko, Ruth Silverman, Angela Y. Wu
Computing discrete two-dimensional convolutions is an important problem in image processing. In mathematical morphology, an important variant is that of computing binary convolutions, where the...
A visibility matching tone reproduction operator for high dynamic range scenes (1997)
Gregory Ward Larson, Gregory Ward Larson, Holly Rushmeier, Holly Rushmeier, Christine Piatko, Christine Piatko
We present a tone reproduction operator that preserves visibility in high dynamic range scenes. Our method introduces a new histogram adjustment technique, based on the population of local adaptation...
A visibility matching tone reproduction operator for high dynamic range scenes (1997)
Gregory Ward Larson, Holly Rushmeier, Christine Piatko
Abstract—We present a tone reproduction operator that preserves visibility in high dynamic range scenes. Our method introduces a new histogram adjustment technique, based on the population of local...
A visibility matching tone reproduction operator for high dynamic range scenes (1997)
Holly Rushmeier, Christine Piatko
Human vision operates over about nine orders of magnitude, from starlight at 10-4 candelas/meter 2 to daylight at 10 5 cd/m 2. In any given scene, the eye can adapt comfortably over a smaller range...
Localizing an object with finger probes (1994)
Freimer, Robert, Khuller, Samir, Mitchell, Joe, Piatko, Christine, Romanik, Kathleen, Souvaine, Diane
We consider the problem of identifying one of a set of polygonal models in the plane using point probes and finger probes. In particular, we give strategies for using a minimum number of finger...
Localizing an object with finger probes (1994)
Freimer, Robert, Khuller, Samir, Mitchell, Joe, Piatko, Christine, Romanik, Kathleen, Souvaine, Diane
We consider the problem of identifying one of a set of polygonal models in the plane using point probes and finger probes. In particular, we give strategies for using a minimum number of finger...
Point Probe Decision Trees for Geometric Concept Classes (1993)
Esther M. Arkin, Michael T. Goodrich, David Mount, Christine Piatko, Steven S. Skiena
A fundamental problem in model-based computer vision is that of identifying to which of a given set of concept classes of geometric models an observed model belongs. Considering a "probe"...
On the Complexity of Shattering Using Arrangements (1991)
Freimer, Robert, Mitchell, Joseph S. B., Piatko, Christine
A subdivision $\cal S$ of $\Re^{d}$ is said to shatter a set of objects if each object is contained within the closure of its own cell of $\cal S$. In this paper, we examine the problem of shattering...
On the Complexity of Shattering Using Arrangements (1991)
Freimer, Robert, Mitchell, Joseph S. B., Piatko, Christine
A subdivision $\cal S$ of $\Re^{d}$ is said to shatter a set of objects if each object is contained within the closure of its own cell of $\cal S$. In this paper, we examine the problem of shattering...