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Aaron Isaacs

dblp:40/1248 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › statistical genetics
association testing
0.112007
GenABEL: an R library for genome-wide association analysis · Bioinform. 2007
Bioinformatics and computational biology › genomics
genome-wide association study
0.112007
GenABEL: an R library for genome-wide association analysis · Bioinform. 2007

Methods — techniques the papers use, named apart from their topics

r library · 0.1
YearPublicationVenuePosition
2007 GenABEL: an R library for genome-wide association analysis
abstract
UNLABELLED: Here we describe an R library for genome-wide association (GWA) analysis. It implements effective storage and handling of GWA data, fast procedures for genetic data quality control, testing of association of single nucleotide polymorphisms with binary or quantitative traits, visualization of results and also provides easy interfaces to standard statistical and graphical procedures implemented in base R and special R libraries for genetic analysis. We evaluated GenABEL using one simulated and two real data sets. We conclude that GenABEL enables the analysis of GWA data on desktop computers. AVAILABILITY: http://cran.r-project.org.
Yurii S. Aulchenko, Stephan Ripke, Aaron Isaacs, Cornelia M. van Duijn
Bioinform.3
2005 Detection of loitering individuals in public transportation areas
abstract
This paper presents a vision-based method to automatically detect individuals loitering about inner-city bus stops. Using a stationary camera view of a bus stop, pedestrians are segmented and tracked throughout the scene. The system takes snapshots of individuals when a clean, nonobstructed view of a pedestrian is found. The snapshots are then used to classify the individual images into a database, using an appearance-based method. The features used to correlate individual images are based on short-term biometrics, which are changeable but stay valid for short periods of time; this system uses clothing color. A linear discriminant method is applied to the color information to enhance the differences and minimize similarities between the different individuals in the feature space. To determine if a given individual is loitering, time stamps collected with the snapshots in their corresponding database class can be used to judge how long an individual has been present. An experiment was performed using a 30-min video of a busy bus stop with six individuals loitering about it. Results show that the system successfully classifies images of all six individuals as loitering.
Nathaniel D. Bird, Osama Masoud, Nikolaos Papanikolopoulos, Aaron Isaacs
IEEE Trans. Intell. Transp. Syst.4