scholarly journals An imaging analysis protocol to trace, quantify, and model multi-signal neuron morphology

2021 ◽  
Vol 2 (2) ◽  
pp. 100567
Author(s):  
Sumit Nanda ◽  
Shatabdi Bhattacharjee ◽  
Daniel N. Cox ◽  
Giorgio A. Ascoli
1996 ◽  
Vol 34 (2) ◽  
pp. 239
Author(s):  
Choon Hyeong Lee ◽  
Ik Yang ◽  
Joo Won Lim ◽  
Dong Ho Lee ◽  
Yung Tae Ko

2021 ◽  
Vol 11 (4) ◽  
pp. 1446
Author(s):  
Jacopo Orsilli ◽  
Anna Galli ◽  
Letizia Bonizzoni ◽  
Michele Caccia

Among the possible variants of X-Ray Fluorescence (XRF), applications exploiting scanning Macro-XRF (MA-XRF) are lately widespread as they allow the visualization of the element distribution maintaining a non-destructive approach. The surface is scanned with a focused or collimated X-ray beam of millimeters or less: analyzing the emitted fluorescence radiation, also elements present below the surface contribute to the elemental distribution image obtained, due to the penetrative nature of X-rays. The importance of this method in the investigation of historical paintings is so obvious—as the elemental distribution obtained can reveal hidden sub-surface layers, including changes made by the artist, or restorations, without any damage to the object—that recently specific international conferences have been held. The present paper summarizes the advantages and limitations of using MA-XRF considering it as an imaging technique, in synergy with other hyperspectral methods, or combining it with spot investigations. The most recent applications in the cultural Heritage field are taken into account, demonstrating how obtained 2D-XRF maps can be of great help in the diagnostic applied on Cultural Heritage materials. Moreover, a pioneering analysis protocol based on the Spectral Angle Mapper (SAM) algorithm is presented, unifying the MA-XRF standard approach with punctual XRF, exploiting information from the mapped area as a database to extend the comprehension to data outside the scanned region, and working independently from the acquisition set-up. Experimental application on some reference pigment layers and a painting by Giotto are presented as validation of the proposed method.


Author(s):  
Annalisa Appice ◽  
Angelo Cannarile ◽  
Antonella Falini ◽  
Donato Malerba ◽  
Francesca Mazzia ◽  
...  

AbstractSaliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors.


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