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Title: A Locally Adaptive Method for Boundary Detection

Abstract

Edge detection techniques predict boundaries between objects or regions in an image. Many classical methods rely on sharp changes in luminance, chromaticity, or texture, and images without these features are difficult to partition correctly. We present a supervised boundary detection method named Locally Adaptive Discriminant Analysis (LADA) that finds edges in images containing high noise and gradual changes between classes. The user trains the algorithm by providing locations of known materials within the image, but no assumptions are made on the material geometries in the image. The algorithm then uses statistics of anisotropic local data to place each pixel in the most probable class. The algorithm’s success is demonstrated on an optical image of a laser-induced shockwave.

Authors:
 [1];  [1];  [1];
  1. Mission Support and Test Services, LLC, Nevada National Security Site
Publication Date:
Research Org.:
Nevada National Security Site/Mission Support and Test Services LLC
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1471704
Report Number(s):
DOE/NV/03624-0071
DOE Contract Number:  
DE-NA0003624
Resource Type:
Conference
Resource Relation:
Conference: Conference on Data Analytics , Santa Fe, New Mexico, March 7, 2018
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; boundary identification, detection image analysis, edge detection, Locally Adaptive Discriminant Analysis, LADA, luminance, chromaticity, texture, anisotropic, pixel, laser-induced shockwave

Citation Formats

Hock, Margaret, Howard, Marylesa, Meehan, B. Timothy, and Dresselhaus-Cooper, Leora. A Locally Adaptive Method for Boundary Detection. United States: N. p., 2018. Web.
Hock, Margaret, Howard, Marylesa, Meehan, B. Timothy, & Dresselhaus-Cooper, Leora. A Locally Adaptive Method for Boundary Detection. United States.
Hock, Margaret, Howard, Marylesa, Meehan, B. Timothy, and Dresselhaus-Cooper, Leora. Wed . "A Locally Adaptive Method for Boundary Detection". United States. doi:. https://www.osti.gov/servlets/purl/1471704.
@article{osti_1471704,
title = {A Locally Adaptive Method for Boundary Detection},
author = {Hock, Margaret and Howard, Marylesa and Meehan, B. Timothy and Dresselhaus-Cooper, Leora},
abstractNote = {Edge detection techniques predict boundaries between objects or regions in an image. Many classical methods rely on sharp changes in luminance, chromaticity, or texture, and images without these features are difficult to partition correctly. We present a supervised boundary detection method named Locally Adaptive Discriminant Analysis (LADA) that finds edges in images containing high noise and gradual changes between classes. The user trains the algorithm by providing locations of known materials within the image, but no assumptions are made on the material geometries in the image. The algorithm then uses statistics of anisotropic local data to place each pixel in the most probable class. The algorithm’s success is demonstrated on an optical image of a laser-induced shockwave.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Mar 07 00:00:00 EST 2018},
month = {Wed Mar 07 00:00:00 EST 2018}
}

Conference:
Other availability
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