Posada J.,Vicomtech IK4 Foundation |
Toro C.,Vicomtech IK4 Foundation |
Barandiaran I.,Vicomtech IK4 Foundation |
Oyarzun D.,Vicomtech IK4 Foundation |
And 5 more authors.
IEEE Computer Graphics and Applications | Year: 2015
A worldwide movement in advanced manufacturing countries is seeking to reinvigorate (and revolutionize) the industrial and manufacturing core competencies with the use of the latest advances in information and communications technology. Visual computing plays an important role as the 'glue factor' in complete solutions. This article positions visual computing in its intrinsic crucial role for Industrie 4.0 and provides a general, broad overview and points out specific directions and scenarios for future research. © 1981-2012 IEEE.
Barandiaran I.,Vicomtech Ik4 Foundation |
Barandiaran I.,Computational Intelligence Group UPV EHU |
Maiz O.,Vicomtech Ik4 Foundation |
Marques I.,Computational Intelligence Group UPV EHU |
And 2 more authors.
Adaptation, Learning, and Optimization | Year: 2014
Robust image segmentation can be achieved by pixel classification based on features extracted from the image. Retinal vessel quantification is an important component of retinal disease screening protocols. Some vessel parameters are potential biomarkers for the diagnosis of several diseases. Specifically, the arterio-venular ratio (AVR) has been proposed as a biomarker for Diabetic retinopathy and other diseases. Classification of retinal vessel pixels into arteries or veins is required for computing AVR. This chapter compares Extreme Learning Machines (ELM) with other state-of-the-art classifier building approaches for this tasks, finding that ELM approaches improve over most of them in classification accuracy and computational time load. Experiments are performed on a well known benchmark dataset of retinal images. © Springer International Publishing Switzerland 2014.