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Alex Torrents

dblp:153/5918 · DBLP profile ↗
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1ranked-venue papers
0as 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 · 1Human-computer interaction and ubiquitous computing · 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
Image recognition and object detection · 77% Efficient and distributed learning · 23%

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

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › image retrieval
large-scale image retrieval
0.212014
High volume offline image recognition · ISMAR 2014
Machine learning › Efficient and distributed learning
on-device inference
0.112014
High volume offline image recognition · ISMAR 2014

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

image recognition engine · 0.2
YearPublicationVenuePosition
2014 High volume offline image recognition
abstract
We show a prototype of an offline image recognition engine, running on a tablet with Intel®Atom™ processor, searching within less than 250ms through thousands (5000+) of images. Moreover, the prototype still offers the advanced capabilities of recognising real world 3D objects, until now reserved only for cloud solutions. Until now image search within large collections of images could be performed only in the cloud, requiring mobile devices to have Internet connectivity. However, for many use cases the connectivity requirement is impractical, e.g. many museums have no network coverage, or do not want their visitors incurring expensive roaming charges. Existing commercial solutions are very limited in terms of searched collections sizes, often imposing a maximum limit of <100 reference images. Moreover, adding images typically affects the recognition speed and increases RAM requirements.
Tomasz Adamek, Luis Martinell, Miquel Ferrarons, Alex Torrents, David Marimon
ISMAR4