5 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.CV2026
AFID: A Unified Open Framework for Automated Fingermark Identification, Quality Assessment and Feature Extraction
Tim Oblak, Rudolf Haraksim, Peter Peer
Automated fingermark identification is the foundation of forensic investigation, yet progress in the field is held back by fragmented, closed-source solutions trained on private or…
cs.CV2020★ 5 cited
Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks
Jaka Šircelj, Tim Oblak, Klemen Grm +5
In this paper we address the problem of representing 3D visual data with parameterized volumetric shape primitives. Specifically, we present a (two-stage) approach built around con…
cs.CV2019★ 1 cited
Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study
Tim Oblak, Klemen Grm, Aleš Jaklič +3
It has been a longstanding goal in computer vision to describe the 3D physical space in terms of parameterized volumetric models that would allow autonomous machines to understand…