Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Kuang-Chern Ng

dblp:43/3074 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

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 graphics and multimedia
1 paper
Computational photography and imaging · 56% Image and video coding · 44%

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

TopicWeightPapersLastEvidence papers
Computational photography and imaging › image acquisition › imaging system design › camera design
autofocus
0.012001
Practical Issues in Pixel-Based Autofocusing for Machine Vision · ICRA 2001
Image and video coding › image quality assessment
sharpness metric
0.012001
Practical Issues in Pixel-Based Autofocusing for Machine Vision · ICRA 2001
Computational photography and imaging
focus measure
0.012001
Practical Issues in Pixel-Based Autofocusing for Machine Vision · ICRA 2001

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

sharpness measures · 0.0search algorithm · 0.0
YearPublicationVenuePosition
2001 Practical Issues in Pixel-Based Autofocusing for Machine Vision
abstract
Different autofocusing methods exist for many cameras today. While not ignoring commercially available methods requiring specialized hardware, this paper focuses mainly on pixel based autofocusing algorithms as applied to CCD camera systems. Different measures of image sharpness are compared. For each of these, different algorithms for searching the best lens setting are assessed in terms of performance as well as their applicability to various situations. In addition, several other factors potentially affecting camera focusing are also discussed. Based on the information obtained, this research attempts to formulate a robust autofocusing algorithm.
Kuang-Chern Ng, Aun Neow Poo, Marcelo H. Ang
ICRA1