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Perry Lea

dblp:191/2437 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

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

Computer networks · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1

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.

Computer networks
1 paper
Wireless networking · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless networking › link adaptation
link rate adaptation
0.812024
ADR-X: ANN-Assisted Wireless Link Rate Adaptation for Compute-Constrained Embedded Gaming Devices · NSDI 2024

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

artificial neural network · 1.5
YearPublicationVenuePosition
2024 ADR-X: ANN-Assisted Wireless Link Rate Adaptation for Compute-Constrained Embedded Gaming Devices
Murali Ramanujam, Joe Schaefer, Stan Adermann, Srihari Narlanka, Perry Lea, Ravi Netravali, Krishna Chintalapudi
NSDI6
2016 Signature line detection in scanned documents
abstract
In this paper, we present an algorithm to determine the signature line in scanned documents through a fast and computationally efficient technique. To that effect, the algorithm consists of three modules, namely pre-processing, candidate line detection and classification. Preprocessing in this case consists of hard thresholding, which is essential to line detection in the next module. The Hough transform for a single angle (horizontal) is utilized in the line detection module. Connected component analysis is utilized to merge detected Hough lines belonging to the same line on the document. Finally, the classification module attempts to classify each line as a signature line or not. This is accomplished using Histogram of a Gradient (HoG) features and a Euclidean distance measure for comparison. Prior data for this similarity comparison consists of images of target text in different font sizes and styles. The proposed algorithm showed favorable results in terms of precision and accuracy.
Osborn de Lima, Shruty Janakiraman, Eli Saber, David C. Day, Peter Bauer, Mark Q. Shaw, Roger Twede, Perry Lea
ICIP8