Name
Abstract | ||
|---|---|---|
X-ray acquisitions are beneficial in food contaminant analysis as they can detect both metallic and non-metallic objects. This paper considers the scenario of single-pixel hyperspectral X-ray acquisitions applied to a series of materials with different characteristics. We propose a method that jointly applies a denoising operation and detects the analysed material in terms of a physical parameterisation. The proposed algorithm is based on a Convolutional Neural Network (CNN) trained with a multi-task learning strategy using a custom loss function tailored to the problem at hand. Experimental results on metals and polymers show that the proposed method can also generalise to materials never seen at training time. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.23919/EUSIPCO54536.2021.9616220 | 29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021) |
Keywords | DocType | ISSN |
X-ray imaging, CNN, polymer detection | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolò Bonettini | 1 | 37 | 3.47 |
| Carlo Andrea Gonano | 2 | 0 | 0.34 |
| Paolo Bestagini | 3 | 0 | 0.34 |
| Marco Marcon | 4 | 0 | 0.34 |
| Bruno Garavelli | 5 | 0 | 0.34 |
| Stefano Tubaro | 6 | 8 | 1.54 |