R
Reid B. Porter
Researcher at Los Alamos National Laboratory
Publications - 66
Citations - 2821
Reid B. Porter is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Image processing & Feature extraction. The author has an hindex of 18, co-authored 66 publications receiving 2570 citations. Previous affiliations of Reid B. Porter include Queensland University of Technology & University of Cambridge.
Papers
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Proceedings ArticleDOI
Parallel evolution of image processing tools for multispectral imagery
Neal R. Harvey,Steven P. Brumby,Simon Perkins,Reid B. Porter,James Theiler,Aaron Cody Young,John J. Szymanski,Jeffrey J. Bloch +7 more
TL;DR: In this article, the authors describe the implementation and performance of a parallel, hybrid evolutionary-algorithm-based system, which optimizes image processing tools for feature-finding tasks in multi-spectral imagery (MSI) data sets.
Proceedings Article
Weighted order statistic classifiers with large rank-order margin
TL;DR: A rank-based measure of margin is presented that can robustly combine large numbers of base hypothesis and has similar performance to other types of regularization.
Proceedings ArticleDOI
A Change Detection Approach to Moving Object Detection in Low Fame-Rate Video
TL;DR: A change detection approach to the pixel-level classification problem and its impact on moving object detection is investigated and applied to lowframe rate (1-2 frames per second) video datasets.
Proceedings ArticleDOI
Ship detection in satellite imagery using rank-order grayscale hit-or-miss transforms
TL;DR: This work describes approaches taken in trying to build ship detection algorithms that have reduced false alarms, and uses a version of the grayscale morphological Hit-or-Miss transform that uses a rank-order selection for the dilation and erosion parts of the transform.
Proceedings ArticleDOI
Interactive image quantification tools in nuclear material forensics
Reid B. Porter,Christy E. Ruggiero,Don Hush,Neal R. Harvey,Patrick M. Kelly,Wayne Scoggins,Lav Tandon +6 more
TL;DR: A user-in-the-loop approach which attempts to both improve the efficiency of domain experts during image quantification as well as capture their domain knowledge over time is described through a sophisticated user-monitoring system that accumulates user-computer interactions as users exploit their imagery.