Sandra Zancajo-Blazquez

dblp:162/8550 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none

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

Security and privacy · 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
Geometric modeling and processing · 100%
Network and information security
1 paper
Digital forensics and information hiding · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing
3d reconstruction
0.212015
Segmentation of Indoor Mapping Point Clouds Applied to Crime Scenes Reconstruction · IEEE Trans. Inf. Forensics Secur. 2015
Geometric modeling and processing › point cloud processing
point cloud segmentation
0.212015
Segmentation of Indoor Mapping Point Clouds Applied to Crime Scenes Reconstruction · IEEE Trans. Inf. Forensics Secur. 2015

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

parameterized geometric fitting · 0.4indoor mapping · 0.4
YearPublicationVenuePosition
2015 Segmentation of Indoor Mapping Point Clouds Applied to Crime Scenes Reconstruction
abstract
Data acquisition in forensics science must be performed in a fast and an efficient way, so that the data acquired is maximized at the same time that disturbance and time on the scene are minimized. For this reason, the use of indoor mapping systems appears as a key solution, in contrast with static systems, either laser or photogrammetry based, in which representing big and complex scenes requires acquisition from a high number of positions, and long-time dedication for data processing. This paper presents a methodology for the segmentation of point clouds acquired with a mobile indoor mapping system, and their conversion to 3-D models in CAD format, based on parameterized geometric elements from the scene. This way, all the information required in forensic sciences is stored in an adequate digital format, enabling its availability in the future, and minimizing time dedication in both data acquisition and processing steps.
Sandra Zancajo-Blazquez, Susana Lagüela-Lopez, Diego González-Aguilera, Joaquin Martinez-Sanchez
IEEE Trans. Inf. Forensics Secur.1