Evangelos Skartados — accepted AI-conference papers

2023

LP-IOANet: Efficient High Resolution Document Shadow Removal

Konstantinos Georgiadis, Mehmet Kerim Yucel, Evangelos Skartados, Valia Dimaridou, Anastasios Drosou, Albert Saà-Garriga +1

ICASSP 2023accepted

Document shadow removal is an integral task in document enhancement pipelines, as it improves visibility, readability and thus the overall quality. Assuming that the majority of practical document shadow removal scenarios require real-time, accurate models that can produce high-resolution outputs in…

Cited by 0 Source ↗Scholar ↗

2018

3D Underground Mapping with a Mobile Robot and a GPR Antenna

Georgios Kouros, Ioannis Kotavelis, Evangelos Skartados, Dimitrios Giakoumis, Dimitrios Tzovaras, Alessandro Simi +1

IROS 2018poster

Automatic subsurface mapping is essential in the construction services, as it is anticipated to become the main operational environment of the future robots to be realized in the respective domain. Towards this direction, the paper at hand, introduces for the first time herein, an integrated framewo…

Cited by 37 Source ↗Scholar ↗

2018

RAMCIP - A Service Robot for MCI Patients at Home

Georgia Peleka, Andreas Kargakos, Evangelos Skartados, Ioannis Kostavelis, Dimitrios Giakoumis, Iason Sarantopoulos +11

IROS 2018poster

This video features RAMCIP, a new service robot developed to provide proactive and discreet assistance to elderly with Mild Cognitive Impairments (MCI), supporting their daily activities at home. Starting with a thorough analysis of needs and requirements of the target population, the RAMCIP robot w…

Cited by 22 Source ↗Scholar ↗

2016

Multi-sensorial and explorative recognition of garments and their material properties in unconstrained environment

Christos Kampouris, Ioannis Mariolis, Georgia Peleka, Evangelos Skartados, Andreas Kargakos, Dimitra Triantafyllou +1

ICRA 2016

Perception of garments is a challenging task for robots due to the large variety in shapes, fabric patterns, and materials. We investigate a multi-sensorial approach, making no assumptions about the garments' configuration or properties. We use a robot equipped with RGB-D, tactile, and photometric s

Cited by 27 Source ↗Scholar ↗