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Mazlum Ferhat Arslan

dblp:260/6352 · DBLP profile ↗
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4ranked-venue papers
4as first author
3since 2021 · last 2025
0000-0003-0348-8227ORCID · corroborated

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

Graphics, computer vision, multimedia, augmented reality and games · 4 · 4 first-author · 3 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

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.

Artificial intelligence
1 paper
Trustworthy machine learning · 100%
Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%
Theoretical computer science
1 paper
Computational geometry · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning
fairness
0.912025
Neuromanifold-Regularized KANs for Shape-fair Feature Representations · ICCV 2025
Machine learning › Trustworthy machine learning › fairness
fair representation learning
0.912025
Neuromanifold-Regularized KANs for Shape-fair Feature Representations · ICCV 2025
Geometric modeling and processing
shape analysis
0.412020
Complexity of Shapes Embedded in Zn With a Bias Towards Squares · IEEE Trans. Image Process. 2020
Computational geometry
digital geometry
0.112020
Complexity of Shapes Embedded in Zn With a Bias Towards Squares · IEEE Trans. Image Process. 2020

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

neuromanifold regularization · 0.9kolmogorov-arnold network · 0.9multi-scale complexity measure · 0.9l-infinity norm · 0.9
YearPublicationVenuePosition
2025 Neuromanifold-Regularized KANs for Shape-fair Feature Representations
Mazlum Ferhat Arslan, Shuo Li 0001
ICCV1
2024 Single-source Domain Generalization in Deep Learning Segmentation via Lipschitz Regularization
Mazlum Ferhat Arslan, Shuo Li 0001
MICCAI (10)1
2022 SHREC'21: Quantifying shape complexity
Mazlum Ferhat Arslan, Alexandros Haridis, Paul L. Rosin, Sibel Tari, Charlotte Brassey, James D. Gardiner, Asli Gençtav, Murat Genctav
Comput. Graph.1
2020 Complexity of Shapes Embedded in Zn With a Bias Towards Squares
abstract
Shape complexity is a hard-to-quantify quality, mainly due to its relative nature. Biased by Euclidean thinking, circles are commonly considered as the simplest. However, their constructions as digital images are only approximations to the ideal form. Consequently, complexity orders computed in reference to circle are unstable. Unlike circles which lose their circleness in digital images, squares retain their qualities. Hence, we consider squares (hypercubes in Zn) to be the simplest shapes relative to which complexity orders are constructed. Using the connection between L∞ norm and squares we effectively encode squareness-adapted simplification through which we obtain multi-scale complexity measure, where scale determines the level of interest to the boundary. The emergent scale above which the effect of a boundary feature (appendage) disappears is related to the ratio of the contacting width of the appendage to that of the main body. We discuss what zero complexity implies in terms of information repetition and constructibility and what kind of shapes in addition to squares have zero complexity.
Mazlum Ferhat Arslan, Sibel Tari
IEEE Trans. Image Process.1