F
Francisco G. Bulnes
Researcher at University of Oviedo
Publications - 35
Citations - 1089
Francisco G. Bulnes is an academic researcher from University of Oviedo. The author has contributed to research in topics: Flatness (systems theory) & Machine vision. The author has an hindex of 12, co-authored 33 publications receiving 852 citations.
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Infrared thermography for temperature measurement and non-destructive testing.
TL;DR: A general introduction to infrared thermography and the common procedures for temperature measurement and non-destructive testing are presented and developments in these fields and recent advances are reviewed.
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Real-time flatness inspection of rolled products based on optical laser triangulation and three-dimensional surface reconstruction
TL;DR: A low-cost flatness inspection system based on optical triangulation by means of a laser stripe emitter and a CMOS matrix camera, designed to be part of an online flatness control system.
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An improved 3D imaging system for dimensional quality inspection of rolled products in the metal industry
Julio Molleda,Rubén Usamentiaga,Daniel F. Garcia,Francisco G. Bulnes,Adrián Espina,Bassiru Dieye,Lyndon N. Smith +6 more
TL;DR: Two approaches to improve the line detection and extraction method used in the original system are discussed, one intended for high-speed processing with lower accuracy, and the other providing high accuracy while incurring higher computational time expenses.
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An efficient method for defect detection during the manufacturing of web materials
TL;DR: A method to detect a specific type of defect that may occur during the production of web materials: periodical defects, which is very harmful, as it can generate many surface defects, greatly reducing the quality of the end product and, on occasions, making it unsuitable for sale.
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A Non-Invasive Technique for Online Defect Detection on Steel Strip Surfaces
TL;DR: In this paper, a new detection technique is proposed, based on the division of an image into a set of overlapping areas, and the optimum values for the configuration parameters of the detection technique are automatically determined using a genetic algorithm.