Multi-scale cortical keypoints for realtime hand tracking and gesture recognition

Human-robot interaction is an interdisciplinary research area which aims at integrating human factors, cognitive psychology and robot technology. The ultimate goal is the development of social robots. These robots are expected to work in human environments, and to understand behavior of persons through gestures and body movements. In this paper we present a biological and realtime framework for detecting and tracking hands. This framework is based on keypoints extracted from cortical V1 end-stopped cells. Detected keypoints and the cells’ responses are used to classify the junction type. By combining annotated keypoints in a hierarchical, multi-scale tree structure, moving and deformable hands can be segregated, their movements can be obtained, and they can be tracked over time. By using hand templates with keypoints at only two scales, a hand’s gestures can be recognized.

Large-scale prediction of seagrass distribution: the case of Cymodocea nodosa (Mediterranean-Atlantic)

Understanding the factors that affect seagrass meadows encompassing their entire range of distribution is challenging yet important for their conservation. We model the environmental niche of Cymodocea nodosa using a combination of environmental variables and landscape metrics to examine factors defining its distribution and find suitable habitats for the species. The most relevant environmental variables defining the distribution of C. nodosa were sea surface temperature (SST) and salinity. We found suitable habitats at SST from 5.8 ºC to 26.4 ºC and salinity ranging from 17.5 to 39.3. Optimal values of mean winter wave height ranged between 1.2 m and 1.5 m, while waves higher than 2.5 m seemed to limit the presence of the species. The influence of nutrients and pH, despite having weight on the models, was not so clear in terms of ranges that confine the distribution of the species. Landscape metrics able to capture variation in the coastline enhanced significantly the accuracy of the models, despite the limitations caused by the scale of the study. By contrasting predictive approaches, we defined the variables affecting the distributional areas that seem unsuitable for C. nodosa as well as those suitable habitats not occupied by the species. These findings are encouraging for its use in future studies on climate-related marine range shifts and meadow restoration projects of these fragile ecosystems.