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Alexandru Ichim

dblp:117/2286 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 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 · 56% Image recognition and object detection · 44%

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

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection
object recognition and localization
0.112012
The jacobs robotics approach to object recognition and localization in the context of the ICRA'11 Solutions in Perception Challenge · ICRA 2012
Computer vision › 3D vision
shape matching
0.112012
The jacobs robotics approach to object recognition and localization in the context of the ICRA'11 Solutions in Perception Challenge · ICRA 2012
Computer vision › 3D vision › range sensing
range image segmentation
0.012012
The jacobs robotics approach to object recognition and localization in the context of the ICRA'11 Solutions in Perception Challenge · ICRA 2012

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

visual feature matching · 0.1geometric hashing · 0.1back-projection · 0.1
YearPublicationVenuePosition
2012 The jacobs robotics approach to object recognition and localization in the context of the ICRA'11 Solutions in Perception Challenge
abstract
In this paper, we give an overview of the Jacobs Robotics entry to the ICRA'11 Solutions in Perception Challenge. We present our multi-pronged strategy for object recognition and localization based on the integrated geometric and visual information available from the Kinect sensor. Firstly, the range image is over-segmented using an edge-detection algorithm and regions of interest are extracted based on a simple shape-analysis per segment. Then, these selected regions of the scene are matched with known objects using visual features and their distribution in 3D space. Finally, generated hypotheses about the positions of the objects are tested by back-projecting learned 3D models to the scene using estimated transformations and sensor model. Our method won the second place among eight competing algorithms, only marginally losing to the winner.
Narunas Vaskevicius, Kaustubh Pathak, Alexandru Ichim, Andreas Birk 0002
ICRA3