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Search Publications by: Jin Chu Wu (Assoc)

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Displaying 1 - 25 of 32

Monte Carlo studies of bootstrap variability in ROC analysis with data dependency

August 1, 2019
Author(s)
Jin Chu Wu, Alvin F. Martin, Raghu N. Kacker
ROC analysis involving two large datasets is an important method for analyzing statistics of interest for decision making of a classifier in many disciplines. And data dependency due to multiple use of the same subjects exists ubiquitously in order to

A novel measure and significance testing in data analysis of cell image segmentation

April 20, 2017
Author(s)
Jin Chu Wu, Michael W. Halter, Raghu N. Kacker, John T. Elliott, Anne L. Plant
Background: Cell image segmentation (CIS) is an essential part of quantitative imaging of biological cells. Designing a performance measure and conducting significance testing are critical for evaluating and comparing the CIS algorithms for image-based

The Impact of Data Dependency on Speaker Recognition Evaluation

February 8, 2017
Author(s)
Jin Chu Wu, Alvin F. Martin, Craig S. Greenberg, Raghu N. Kacker
The data dependency due to multiple use of the same subjects has impact on the standard error (SE) of the detection cost function (DCF) in speaker recognition evaluation. The DCF is defined as a weighted sum of the probabilities of type I and type II

Bootstrap Variability Studies in ROC Analysis on Large Datasets

March 19, 2014
Author(s)
Jin Chu Wu, Alvin F. Martin, Raghu N. Kacker
The nonparametric two-sample bootstrap is employed to compute uncertainties of measures in receiver operating characteristic (ROC) analysis on large datasets in areas such as biometrics, and so on. In this framework, the bootstrap variability was

Measurement Uncertainty in Cell Image Segmentation Data Analysis

August 13, 2013
Author(s)
Jin Chu Wu, Michael W. Halter, Raghu N. Kacker, John T. Elliott, Anne L. Plant
Cell image segmentation is a part of quantitative studies regarding cell movement and cell behavior, and it plays a critical role in molecular biology and cellular biochemistry. Therefore, it is fundamentally important to evaluate the performance levels of

Significance Test with Data Dependency in Speaker Recognition Evaluation

July 25, 2013
Author(s)
Jin Chu Wu, Alvin F. Martin, Craig S. Greenberg, Raghu N. Kacker, Vincent M. Stanford
To evaluate the performance of speaker recognition systems, a detection cost function defined as a weighted sum of the probabilities of type I and type II errors is employed. The speaker datasets may have data dependency due to multiple uses of the same

Significance Test in Speaker Recognition Data Analysis with Data Dependency

October 17, 2012
Author(s)
Jin Chu Wu, Alvin F. Martin, Craig S. Greenberg, Raghu N. Kacker, Vincent M. Stanford
To evaluate the performance of speaker recognition systems, a detection cost function defined as a weighted sum of the probabilities of type I and type II errors is employed. The speaker datasets may have data dependency due to multiple uses of the same

An Exploration of the Operational Ramifications of Lossless Compression of 1000 ppi Fingerprint Imagery

August 6, 2012
Author(s)
Shahram Orandi, John M. Libert, John D. Grantham, Kenneth Ko, Stephen S. Wood, Jin Chu Wu, Lindsay M. Petersen, Bruce Bandini
This paper presents the findings of a study initially conducted to measure the operational impact of JPEG 2000 lossy compression on 1000 ppi fingerprint imagery at various levels of compression, but later expanded to include lossless compression. Lossless

A New Measure in Cell Image Segmentation Data Analysis

July 24, 2012
Author(s)
Jin Chu Wu, Michael W. Halter, Raghu N. Kacker, John T. Elliott
Cell image segmentation (CIS) is critical for quantitative imaging in cytometric analyses. The data derived after segmentation can be used to infer cellular function. To evaluate CIS algorithms, first for dealing with comparisons of single cells treated as

Data Dependency on Measurement Uncertainties in Speaker Recognition Evaluation

July 11, 2012
Author(s)
Jin Chu Wu, Alvin F. Martin, Craig S. Greenberg, Raghu N. Kacker
The National Institute of Standards and Technology conducts an ongoing series of Speaker Recognition Evaluations (SRE). Speaker detection performance is measured using a detection cost function defined as a weighted sum of the probabilities of type I and

Uncertainties of Measures in Speaker Recognition Evaluation

November 10, 2011
Author(s)
Jin Chu Wu, Alvin F. Martin, Craig S. Greenberg, Raghu N. Kacker
The National Institute of Standards and Technology (NIST) Speaker Recognition Evaluations (SRE) are an ongoing series of projects conducted by NIST. In the NIST SRE, speaker detection performance is measured using a detection cost function, which is

Data Dependency on Measurement Uncertainties in Speaker Recognition Evaluation

November 3, 2011
Author(s)
Jin Chu Wu, Alvin F. Martin, Craig S. Greenberg, Raghu N. Kacker
The National Institute of Standards and Technology (NIST) has been conducting an ongoing series of Speaker Recognition Evaluations (SRE). Speaker detection performance is measured using a detection cost function defined as a weighted sum of the

Effects of JPEG 2000 Image Compression on 1000ppi Fingerprint Imagery

May 26, 2011
Author(s)
Shahram Orandi, John M. Libert, John Grantham, Kenneth Ko, Stephen S. Wood, Jin Chu Wu
This paper presents the findings of a study conducted to measure the impact of JPEG 2000 compression on fingerprint imagery at various levels of compression. The impact of compression is measured in terms of impact to both Galton and non-Galton based

Measures, Uncertainties, and Significance Test in Operational ROC Analysis

January 31, 2011
Author(s)
Jin Chu Wu, Alvin F. Martin, Raghu N. Kacker
In operational ROC (receiver operating characteristic) analysis of fingerprint-image matching algorithms on large datasets, the measures and their accuracies are investigated in the three scenarios: 1) the true accept rate (TAR) of genuine scores at a

Further Studies of Bootstrap Variability for ROC Analysis on Large Datasets

October 11, 2010
Author(s)
Jin Chu Wu, Alvin F. Martin, Raghu N. Kacker
The nonparametric two-sample bootstrap is successfully applied to computing the measurement uncertainties in receiver operating characteristic (ROC) analysis on large datasets in areas such as biometrics, speaker recognition system, etc. To determine the

Validation of Two-Sample Bootstrap in ROC Analysis on Large Datasets Using AURC

October 11, 2010
Author(s)
Jin Chu Wu, Alvin F. Martin, Raghu N. Kacker
Sampling variability can result in uncertainties of measures. The nonparametric two-sample bootstrap method has been used to compute uncertainties of measures in receiver operating characteristic (ROC) analysis on large datasets, such as the standard error

Measurement Uncertainties in Speaker Recognition Evaluation

September 15, 2010
Author(s)
Jin Chu Wu, Alvin F. Martin, Craig S. Greenberg, Raghu N. Kacker
The speaker recognition evaluation is an ongoing series of evaluations conducted by NIST. A detection cost function is computed over the sequence of trials provided and used for all speaker detection tests while measuring speaker detection performance. The

Significance Test in Operational ROC Analysis

April 5, 2010
Author(s)
Jin Chu Wu, Alvin F. Martin, Raghu N. Kacker, Robert C. Hagwood
To evaluate the performance of fingerprint-image matching algorithms on large datasets, a receiver operating characteristic (ROC) curve is applied. From the operational perspective, the true accept rate (TAR) of the genuine scores at a specified false