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Istituto di Scienza e Tecnologie dell'Informazione     
D'Acunto M., Colantonio S., Righi M., Salvetti O. SuperResolution-aided recognition of cytoskeletons in scanning probe microscopy images. In: ICPRAM 2014 - 3rd International Conference on Pattern Recognition Applications and Methods (Angers, France, 6-8 March 2014). Proceedings, pp. 703 - 709. Maria De Marsico, Antoine Tabbone, Ana Fred (eds.). SCITEPRESS, 2014.
 
 
Abstract
(English)
In this paper, we discuss the possibility to adopt SuperResolution (SR) methods as an important preparatory step to Pattern Recognition, so as to improve the accuracy of image content recognition and identification. Actually, SR mainly deals with the task of deriving a high-resolution image from one or multiple low resolution images of the same scene. The high-resolved image corresponds to a more precise image whose content is enriched with information hidden among the pixels of the original low resolution image(s), and corresponds to a more faithfully representation of the imaged scene. Such enriched content obviously represents a better sample of the scene which can be profitably used by Pattern Recognition algorithms. A real application scenario is discussed dealing with the recognition of cell skeletons in Scanning ProbeMicroscopy (SPM) single image SR. Results show that the SR allows us to detect and recognize important information barely visible in the original low-resolution image.
Abstract
(Italiano)
Nel lavoro si discute di come il pattern recognition puņ aiutare una strategia di superrisoluzione di immagini di cellule acquisite utilizzando un microscopio a forza atomica.
URL: http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0004830407030709
DOI: 10.5220/0004830407030709
Subject Superrisolution and image analysis
Pattern recognition
I.4.0 Image processing software
I.4.5 Reconstruction
I.4.6 Segmentation
I.6 SIMULATION AND MODELING
62H10 Distribution of statistics
62H30 Classification and discrimination; cluster analysis


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