Demo Data from EDBT 2026 / reviewers in the wild / expert
Supriya Sathyanarayana
dblp:140/7814
· DBLP profile ↗
3ranked-venue papers
2as first author
0since 2021 · last 2020
0000-0002-4596-807XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-authorHuman-computer interaction and ubiquitous computing · 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 |
Virtual and augmented reality · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality › augmented reality
medical augmented reality |
0.4 | 1 | 2020 | Landmark-based mixed-reality perceptual alignment of medical imaging data and accuracy validation in living subjects · ISMAR 2020 |
Medical and health informatics
image-guided intervention |
0.1 | 1 | 2020 | Landmark-based mixed-reality perceptual alignment of medical imaging data and accuracy validation in living subjects · ISMAR 2020 |
Methods — techniques the papers use, named apart from their topics
fiducial-based registration · 0.9MRI · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Landmark-based mixed-reality perceptual alignment of medical imaging data and accuracy validation in living subjectsabstractMedical augmented reality (AR) applications where virtual renderings are aligned with the real world allow to visualize internal anatomy of the patient to a medical caregiver wearing an AR headset. Accurate alignment of virtual and real content is important for applications where the virtual rendering is used to guide the medical procedure such as a surgery. Compared to 2D AR applications, where the alignment accuracy can be directly measured on the 2D screen, 3D medical AR applications require alignment measurements using phantoms and external tracking systems. In this paper we present an approach for landmark-based alignment, validation and accuracy measurement of a 3D AR overlay of medical images on the real-world subject. This is done by performing an initial MRI of a subject’s head, an AR alignment task of the virtual rendering of the head MRI data to the subject’s real-world head using virtual fiducials, and a second MRI scan to test the accuracy of the AR alignment task. We have performed these 3D medical AR alignment measurements on seven volunteers using a MagicLeap AR head-mounted display. Across all seven volunteers we measured an alignment accuracy of $4.7 \pm 2.6$ mm. These results suggest that such an AR application can be a valuable tool for guiding non-invasive transcranial magnetic brain stimulation treatment. The presented MRI-based accuracy validation will furthermore be an important versatile tool to establish the safety of medical AR techniques. Christoph Leuze, Supriya Sathyanarayana, Bruce Lewis Daniel, Jennifer A. McNab |
ISMAR | 2 |
| 2015 | Identifying epileptic seizures based on a template-based eyeball detection techniqueabstractMany types of epileptic seizures are accompanied by characteristic eye movement, along with the movement of other parts of the body. Vision-based detection of eye movement in the context of seizures is less explored. The contribution of this paper is three-fold: (1) a compute-efficient algorithm for eyeball detection is proposed and evaluated on a standard database and yields an accuracy of 95%. (2) A new video dataset for absence seizure has been created. (3) The proposed algorithm for eyeball detection is applied on this dataset in order to detect the occurrence of absence seizure. Supriya Sathyanarayana, Ravi Kumar Satzoda, Suchitra Sathyanarayana, Thambipillai Srikanthan |
ICIP | 1 |
| 2013 | Block-Based Search Space Reduction Technique for Face Detection Using Shoulder and Head Curves
Supriya Sathyanarayana, Ravi Kumar Satzoda, Suchitra Sathyanarayana, Thambipillai Srikanthan |
PSIVT | 1 |