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Giulia Andreani

dblp:192/7026 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none

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

Artificial intelligence and machine learning · 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 · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
depth estimation		 0.012001
A Hierarchical Model of Complex Cells in Visual Cortex for the Binocular Perception of Motion-in-Depth · NIPS 2001
Computer vision › 3D vision › motion estimation › 3d motion estimation
motion-in-depth		 0.012001
A Hierarchical Model of Complex Cells in Visual Cortex for the Binocular Perception of Motion-in-Depth · NIPS 2001
Bioinformatics and computational biology
computational neuroscience		 0.012001
A Hierarchical Model of Complex Cells in Visual Cortex for the Binocular Perception of Motion-in-Depth · NIPS 2001
Bioinformatics and computational biology › computational neuroscience › visual cortex
visual cortex modeling		 0.012001
A Hierarchical Model of Complex Cells in Visual Cortex for the Binocular Perception of Motion-in-Depth · NIPS 2001

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

phase-based disparity estimation · 0.1disparity energy model · 0.1
YearPublicationVenuePosition
2001 A Hierarchical Model of Complex Cells in Visual Cortex for the Binocular Perception of Motion-in-Depth
abstract
A cortical model for motion-in-depth selectivity of complex cells in the visual cortex is proposed. The model is based on a time ex(cid:173) tension of the phase-based techniques for disparity estimation. We consider the computation of the total temporal derivative of the time-varying disparity through the combination of the responses of disparity energy units. To take into account the physiological plau(cid:173) sibility, the model is based on the combinations of binocular cells characterized by different ocular dominance indices. The resulting cortical units of the model show a sharp selectivity for motion-in(cid:173) depth that has been compared with that reported in the literature for real cortical cells.
Silvio P. Sabatini, Fabio Solari, Giulia Andreani, Chiara Bartolozzi, Giacomo M. Bisio
 NIPS3