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Jozef Dobos

dblp:54/9988 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorArtificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 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.

Artificial intelligence
1 paper
3D vision · 72% Trustworthy machine learning · 28%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning › uncertainty estimation
confidence estimation
0.112011
Capturing Time-of-Flight data with confidence · CVPR 2011
Computer vision › 3D vision › range sensing
depth sensing
0.112011
Capturing Time-of-Flight data with confidence · CVPR 2011
Computer vision › 3D vision › depth estimation
time-of-flight depth
0.112011
Capturing Time-of-Flight data with confidence · CVPR 2011
Computer vision › 3D vision
3d reconstruction
0.012011
Capturing Time-of-Flight data with confidence · CVPR 2011
Computer vision › 3D vision
point cloud processing
0.012011
Capturing Time-of-Flight data with confidence · CVPR 2011

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

random forest regressor · 0.1per-pixel confidence measure · 0.1
YearPublicationVenuePosition
2014 3D Timeline: Reverse engineering of a part-based provenance from consecutive 3D models
abstract
Abstract We present a novel tool for reverse engineering of modeling histories from consecutive 3D files based on a timeline abstraction. Although a timeline interface is commonly used in 3D modeling packages for animations, it has not been used on geometry manipulation before. Unlike previous visualization methods that require instrumentation of editing software, our approach does not rely on pre‐recorded editing instructions. Instead, each stand‐alone 3D file is treated as a keyframe of a construction flow from which the editing provenance is reverse engineered. We evaluate this tool on six complex 3D sequences created in a variety of modeling tools by different professional artists and conclude that it provides useful means of visualizing and understanding the editing history. A comparative user study suggests the tool is well suited for this purpose.
Jozef Dobos, Niloy J. Mitra, Anthony Steed
Comput. Graph. Forum1
2013 On the existence of equitable cake divisions
Katarína Cechlárová, Jozef Dobos, Eva Pillárová
Inf. Sci.2
2011 Capturing Time-of-Flight data with confidence
abstract
Time-of-Flight cameras provide high-frame-rate depth measurements within a limited range of distances. These readings can be extremely noisy and display unique errors, for instance, where scenes contain depth discontinuities or materials with low infrared reflectivity. Previous works have treated the amplitude of each Time-of-Flight sample as a measure of confidence. In this paper, we demonstrate the shortcomings of this common lone heuristic, and propose an improved per-pixel confidence measure using a Random Forest regressor trained with real-world data. Using an industrial laser scanner for ground truth acquisition, we evaluate our technique on data from two different Time-of-Flight cameras1. We argue that an improved confidence measure leads to superior reconstructions in subsequent steps of traditional scan processing pipelines. At the same time, data with confidence reduces the need for point cloud smoothing and median filtering.
Malcolm Reynolds, Jozef Dobos, Leto Peel, Tim Weyrich, Gabriel J. Brostow
CVPR2