output
20192024
most citedre-OBJ: Jointly Learning the Foreground and Background for Object Instance Re-identification

5 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

5 papers

cs.CV2024★ 2 cited

Impact of LiDAR visualisations on semantic segmentation of archaeological objects

Raveerat Jaturapitpornchai, Giulio Poggi, Gregory Sech +3

Deep learning methods in LiDAR-based archaeological research often leverage visualisation techniques derived from Digital Elevation Models to enhance characteristics of archaeologi…

cs.CV2024★ 3 cited

Pansharpening of PRISMA products for archaeological prospection

Gregory Sech, Giulio Poggi, Marina Ljubenovic +2

Hyperspectral data recorded from satellite platforms are often ill-suited for geo-archaeological prospection due to low spatial resolution. The established potential of hyperspectr…

physics.optics2023★ 2 cited

Super-resolution of THz time-domain images based on low-rank representation

Marina Ljubenovic, Alessia Artesani, Stefano Bonetti +1

Terahertz time-domain spectroscopy (THz-TDS) employs sub-picosecond pulses to probe dielectric properties of materials giving as a result a 3-dimensional hyperspectral data cube. T…

cs.CV2023★ 3 cited

Optimal Transport for Change Detection on LiDAR Point Clouds

Marco Fiorucci, Peter Naylor, Makoto Yamada

Unsupervised change detection between airborne LiDAR data points, taken at separate times over the same location, can be difficult due to unmatching spatial support and noise from…

cs.CV2019★ 5 cited

re-OBJ: Jointly Learning the Foreground and Background for Object Instance Re-identification

Vaibhav Bansal, Stuart James, Alessio Del Bue

Conventional approaches to object instance re-identification rely on matching appearances of the target objects among a set of frames. However, learning appearances of the objects…