RT Journal Article
T1 Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts
A1 Merino, Ibon
A1 Azpiazu, Jon
A1 Remazeilles, Anthony
A1 Sierra, Basilio
AB This article deals with the 2D image-based recognition of industrial parts. Methods based on histograms are well known and widely used, but it is hard to find the best combination of histograms, most distinctive for instance, for each situation and without a high user expertise. We proposed a descriptor subset selection technique that automatically selects the most appropriate descriptor combination, and that outperforms approach involving single descriptors. We have considered both backward and forward mechanisms. Furthermore, to recognize the industrial parts a supervised classification is used with the global descriptors as predictors. Several class approaches are compared. Given our application, the best results are obtained with the Support Vector Machine with a combination of descriptors increasing the F1 by 0.031 with respect to the best descriptor alone.
SN 2076-3417
YR 2020
FD 2020-06-01
LA eng
NO Merino , I , Azpiazu , J , Remazeilles , A & Sierra , B 2020 , ' Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts ' , Applied Sciences , vol. 10 , no. 11 , 3701 , pp. 3701 . https://doi.org/10.3390/app10113701
NO Publisher Copyright: © 2020 by the authors.
DS TECNALIA Publications
RD 1 jul 2024