Demo Data from SIGIR 2026 / reviewers in the wild / expert
K. S. Pradeep
dblp:08/823
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, 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 · 87% Computational photography and imaging · 13% | |
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 4 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
depth estimation |
0.1 | 1 | 2007 | Improving Shape From Focus Using Defocus Cue · IEEE Trans. Image Process. 2007 |
Computer vision › 3D vision › depth estimation › focus-based depth estimation
depth from focus |
0.1 | 1 | 2007 | Improving Shape From Focus Using Defocus Cue · IEEE Trans. Image Process. 2007 |
Image and video processing
image restoration |
0.1 | 1 | 2007 | Improving Shape From Focus Using Defocus Cue · IEEE Trans. Image Process. 2007 |
Image and video processing › image restoration
space-variant restoration |
0.1 | 1 | 2007 | Improving Shape From Focus Using Defocus Cue · IEEE Trans. Image Process. 2007 |
Methods — techniques the papers use, named apart from their topics
space-variant restoration · 0.1defocus blur estimation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Improving Shape From Focus Using Defocus CueabstractThe shape-from-focus (SFF) method uses a sequence of frames to estimate the structure of a 3-D object. Its accuracy depends on the step size by which the translational table is moved while capturing the images. Existing SFF algorithms use an ad hoc interpolation strategy to account for the error due to the finite step size. We propose an improved SFF method that uses relative defocus blur derived from actual image data to arrive at the final estimates of the structure of the object. A space-variant image restoration scheme is also proposed to obtain a focused image of the 3-D object. The reconstructed 3-D structure as well as the quality of the restored image are superior for the proposed method in comparison to traditional SFF. K. S. Pradeep, A. N. Rajagopalan 0001 |
IEEE Trans. Image Process. | 1 |