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Ravi K. Sharma

dblp:19/209 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2001
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 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
Image and video processing · 100%

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

TopicWeightPapersLastEvidence papers
Image and video processing › image fusion
multisensor image fusion
0.011998
Probabilistic Image Sensor Fusion · NIPS 1998
Image and video processing › image restoration
image denoising
0.011998
Probabilistic Image Sensor Fusion · NIPS 1998

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

probabilistic modeling · 0.0
YearPublicationVenuePosition
2001 Practical challenges for digital watermarking applications
abstract
The field of digital watermarking has recently seen numerous articles covering novel techniques, theoretical studies, attacks and analysis. We focus on practical challenges for digital watermarking applications. Challenges include design considerations, requirements analysis, choice of watermarking techniques, speed, robustness and the tradeoffs involved. We describe common attributes of watermarking systems and discuss the challenges in developing real world applications. We present, as a case study, a hypothetical application that captures important aspects of watermarking systems and illustrates some of the issues faced.
Ravi K. Sharma, Steve Decker
MMSP1
1998 Probabilistic Image Sensor Fusion
Ravi K. Sharma, Todd K. Leen, Misha Pavel
NIPS1