activity
20172020
most citedRGB-D-based Framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments

19 citations · 23 across the 5 of their papers we have counts for

collaborators

12 papers

cs.CV202019 cited

RGB-D-based Framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments

Andrés Fuster-Guilló, Jorge Azorín-López, Marcelo Saval-Calvo +3

This research aims to improve dietetic-nutritional treatment using state-of-the-art RGB-D sensors and virtual reality (VR) technology. Recent studies show that adherence to treatme…

cs.CV2020

When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)

Victor Villena-Martinez, Sergiu Oprea, Marcelo Saval-Calvo +3

Registration is the process that computes the transformation that aligns sets of data. Commonly, a registration process can be divided into four main steps: target selection, featu…

cs.CV2019

Automatic Hierarchical Classification of Kelps using Deep Residual Features

Ammar Mahmood, Ana Giraldo Ospina, Mohammed Bennamoun +6

Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal…

cs.CV20191 cited

UDFNet: Unsupervised Disparity Fusion with Adversarial Networks

Can Pu, Robert B. Fisher

Existing disparity fusion methods based on deep learning achieve state-of-the-art performance, but they require ground truth disparity data to train. As far as I know, this is the…

cs.RO2018

TrimBot2020: an outdoor robot for automatic gardening

Nicola Strisciuglio, Radim Tylecek, Michael Blaich +9

Robots are increasingly present in modern industry and also in everyday life. Their applications range from health-related situations, for assistance to elderly people or in surgic…

cs.CV2018

DUGMA: Dynamic Uncertainty-Based Gaussian Mixture Alignment

Can Pu, Nanbo Li, Radim Tylecek +1

Registering accurately point clouds from a cheap low-resolution sensor is a challenging task. Existing rigid registration methods failed to use the physical 3D uncertainty distribu…