Ms. Jean E. Nonnemaker Profile

Jean E. Nonnemaker

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 19 January 2009 Paper

Using synthetic data safely in classification

Jean Nonnemaker, Henry Baird

Proceedings Volume 7247, 72470G (2009) https://doi.org/10.1117/12.805619

KEYWORDS: Cardiovascular magnetic resonance imaging, Image quality, Error analysis, Image processing, Feature extraction, Data acquisition, Systems modeling, Software, Prototyping, Image classification

Read Abstract +

When is it safe to use synthetic training data in supervised classification? Trainable classifier technologies require large representative training sets consisting of samples labeled with their true class. Acquiring such training sets is difficult and costly. One way to alleviate this problem is to enlarge training sets by generating artificial, synthetic samples. Of course this immediately raises many questions, perhaps the first being "Why should we trust artificially generated data to be an accurate representative of the real distributions?" Other questions include "When will training on synthetic data work as well as - or better than training on real data ?". We distinguish between sample space (the set of real samples), parameter space (all samples that can be generated synthetically), and finally, feature space (the set of samples in terms of finite numerical values). In this paper, we discuss a series of experiments, in which we produced synthetic data in parameter space, that is, by convex interpolation among the generating parameters for samples and showed we could amplify real data to produce a classifier that is as accurate as a classifier trained on real data. Specifically, we have explored the feasibility of varying the generating parameters for Knuth's Metafont system to see if previously unseen fonts could also be recognized. We also varied parameters for an image quality model. We have found that training on interpolated data is for the most part safe, that is to say never produced more errors. Furthermore, the classifier trained on interpolated data often improved class accuracy.

Proceedings Article | 16 January 2006 Paper

Versatile document image content extraction

Henry Baird, Michael Moll, Jean Nonnemaker, Matthew Casey, Don Delorenzo

Proceedings Volume 6067, 60670R (2006) https://doi.org/10.1117/12.650359

KEYWORDS: Feature extraction, Data modeling, Visualization, Image classification, Pattern recognition, Associative arrays, Photography, Mathematics, Image processing, Advanced distributed simulations

Read Abstract +

We offer a preliminary report on a research program to investigate versatile algorithms for document image content extraction, that is locating regions containing handwriting, machine-print text, graphics, line-art, logos, photographs, noise, etc. To solve this problem in its full generality requires coping with a vast diversity of document and image types. Automatically trainable methods are highly desirable, as well as extremely high speed in order to process large collections. Significant obstacles include the expense of preparing correctly labeled ("ground-truthed") samples, unresolved methodological questions in specifying the domain (e.g. what is a representative collection of document images?), and a lack of consensus among researchers on how to evaluate content-extraction performance. Our research strategy emphasizes versatility first: that is, we concentrate at the outset on designing methods that promise to work across the broadest possible range of cases. This strategy has several important implications: the classifiers must be trainable in reasonable time on vast data sets; and expensive ground-truthed data sets must be complemented by amplification using generative models. These and other design and architectural issues are discussed. We propose a trainable classification methodology that marries k-d trees and hash-driven table lookup and describe preliminary experiments.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years