Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yogesh Srinivas
dblp:87/560
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2001
0009-0001-2624-8384ORCID · corroborated
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
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% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Image and video processing › image restoration
image denoising |
0.0 | 1 | 2001 | Quantitative image quality analysis of a nonlinear spatio-temporal filter · IEEE Trans. Image Process. 2001 |
Image and video processing › image sequence processing
spatio-temporal filtering |
0.0 | 1 | 2001 | Quantitative image quality analysis of a nonlinear spatio-temporal filter · IEEE Trans. Image Process. 2001 |
Medical and health informatics › medical imaging › x-ray imaging
fluoroscopy |
0.0 | 1 | 2001 | Quantitative image quality analysis of a nonlinear spatio-temporal filter · IEEE Trans. Image Process. 2001 |
Medical and health informatics
medical imaging |
0.0 | 1 | 2001 | Quantitative image quality analysis of a nonlinear spatio-temporal filter · IEEE Trans. Image Process. 2001 |
Methods — techniques the papers use, named apart from their topics
median filter · 0.1forced-choice experiment · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | A comparative study of three expert systems for blood pressure control
Yogesh Srinivas, William D. Timmons, John Durkin |
Expert Syst. Appl. | 1 |
| 2001 | Quantitative image quality analysis of a nonlinear spatio-temporal filterabstractDigital temporal and spatial filtering of fluoroscopic image sequences can be used to improve the quality of images acquired at low X-ray exposure. In this study, we characterized a nonlinear edge preserving, spatio-temporal noise reduction filter, the bidirectional multistage (BMS) median filter of Arce (1991). To assess image quality, signal detection and discrimination experiments were performed on stationary targets using a four-alternative forced-choice paradigm. A measure of detectability, d', was obtained for filtered and unfiltered noisy image sequences at different signal amplitudes. Filtering gave statistically significant, average d' improvements of 20% (detection) and 31% (discrimination). A nonprewhitening detection model modified to include the human spatio-temporal visual system contrast-sensitivity underestimated enhancement, predicting an improvement of 6%. Pixel noise standard deviation, a commonly applied image quality measure, greatly overestimated effectiveness giving 67% improvement in d'. We conclude that human testing is required to evaluate the filter effectiveness and that human perception models must be improved to account for the spatio-temporal filtering of image sequences. Francisco J. Sánchez-Marín, Yogesh Srinivas, Kadri N. Jabri, David L. Wilson |
IEEE Trans. Image Process. | 2 |