P
Padhraic Smyth
Researcher at University of California, Irvine
Publications - 359
Citations - 38795
Padhraic Smyth is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Inference & Topic model. The author has an hindex of 80, co-authored 342 publications receiving 36653 citations. Previous affiliations of Padhraic Smyth include University of California & Jet Propulsion Laboratory.
Papers
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Journal ArticleDOI
California wildfire spread derived using VIIRS satellite observations and an object-based tracking system
Yan Chen,Stijn Hantson,Niels Andela,Shane R. Coffield,Casey A. Graff,Douglas C. Morton,Lesley Ott,Efi Foufoula-Georgiou,Padhraic Smyth,Michael L. Goulden,James T. Randerson +10 more
TL;DR: In this article , an object-based system for tracking the progression of individual fires using 375 m VISible Infrared Imaging Radiometer Suite active fire detections was developed, where fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or assigned to a new object.
Proceedings Article
Learning to Classify Galaxy Shapes Using the EM Algorithm
TL;DR: This work describes the application of probabilistic model-based learning to the problem of automatically identifying classes of galaxies, based on both morphological and pixel intensity characteristics, and demonstrates howclasses of galaxies can be learned in an unsupervised manner using a two-level EM algorithm.
Journal Article
Trainable Cataloging for Digital Image Libraries with Applications to Volcano Detection
TL;DR: In this paper, the authors describe the development of a trainable cataloging system: the user indicates the location of the objects of interest for a number of training images and the system learns to detect and catalog these objects in the rest of the database.
Locating Small Volcanoes on Venus Using a Scientist-Trainable Analysis System
Jayne C. Aubele,Larry S. Crumpler,Usama M. Fayyad,Padhraic Smyth,Michael C. Burl,Pietro Perona +5 more
Journal ArticleDOI
Bayesian Detection of Changepoints in Finite-State Markov Chains for Multiple Sequences
TL;DR: A Bayesian framework is proposed for analyzing sets of categorical sequences consisting of piecewise homogenous Markov segments, placing priors on the locations of the changepoints and on the transition matrices and using Markov chain Monte Carlo techniques to obtain posterior samples given the data.