Demo Data from EDBT 2026 / reviewers in the wild / expert
Sajad Farokhi
dblp:148/0406
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2Databases, data management, data science and information retrieval · 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 |
Multimedia analysis and retrieval · 50% Image and video processing · 50% |
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 representation
blur invariants |
0.2 | 1 | 2016 | Recognition of Images Degraded by Gaussian Blur · IEEE Trans. Image Process. 2016 |
Multimedia analysis and retrieval › object recognition
blur-invariant recognition |
0.2 | 1 | 2016 | Recognition of Images Degraded by Gaussian Blur · IEEE Trans. Image Process. 2016 |
Image and video processing › image representation
moment computation |
0.2 | 1 | 2016 | Recognition of Images Degraded by Gaussian Blur · IEEE Trans. Image Process. 2016 |
Multimedia analysis and retrieval
object recognition |
0.2 | 1 | 2016 | Recognition of Images Degraded by Gaussian Blur · IEEE Trans. Image Process. 2016 |
Methods — techniques the papers use, named apart from their topics
spectral domain projection · 0.2recursive moment computation · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Recognition of Images Degraded by Gaussian BlurabstractIn this paper, we propose a new theory of invariants to Gaussian blur. We introduce a notion of a primordial image as a canonical form of all Gaussian blur-equivalent images. The primordial image is defined in spectral domain by means of projection operators. We prove that the moments of the primordial image are invariant to Gaussian blur and we derive recursive formulas for their direct computation without actually constructing the primordial image itself. We show how to extend their invariance also to image rotation. The application of these invariants is in blur-invariant image comparison and recognition. In the experimental part, we perform an exhaustive comparison with two main competitors: 1) the Zhang distance and 2) the local phase quantization. Jan Flusser, Sajad Farokhi, Cyril Höschl IV, Tomás Suk, Barbara Zitová, Matteo Pedone |
IEEE Trans. Image Process. | 2 |
| 2015 | Recognition of Images Degraded by Gaussian Blur
Jan Flusser, Tomás Suk, Sajad Farokhi, Cyril Höschl IV |
CAIP (1) | 3 |
| 2015 | Near infrared face recognition using Zernike moments and Hermite kernels
Sajad Farokhi, Usman Ullah Sheikh, Jan Flusser |
Inf. Sci. | 1 |