Publications
“Inline nondestructive internal disorder detection in pear fruit using explainable deep anomaly detection on X-ray images”, Computers and Electronics in Agriculture, vol. 197, no. 106962, pp. 1-14, 2022.
, “Non-destructive internal disorder detection of Conference pears by semantic segmentation of X-ray CT scans using deep learning”, Expert Systems with Applications, vol. 176, no. 114925, pp. 1-12, 2021.
, “Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning”, Food Control, vol. 113, no. 107170, pp. 1-13, 2020.
, “Combination of shape and X-ray inspection for apple internal quality control: in silico analysis of the methodology based on X-ray computed tomography”, Postharvest Biology and Technology, vol. 148, pp. 218-227, 2019.
, “Neural network Hilbert transform based filtered backprojection for fast inline X-ray inspection”, Measurement Science and Technology, vol. 29, no. 3, 2018. Download paper (3.4 MB)
, “Building 3D Statistical Shape Models of Horticultural Products”, Food and Bioprocess Technology, vol. 10, no. 11, pp. 2100-2112, 2017. Download paper (1.73 MB)
, “Comparison of methods for online inspection of apple internal quality”, in 7th Conference on Industrial Computed Tomography, Leuven, Belgium, 2017. Download paper (815.39 KB)
, “Inline Discrete Tomography system: application to agricultural product inspection”, Computers and Electronics in Agriculture, vol. 138, pp. 117–126, 2017.
, “Multisensor X-ray inspection of internal defects in horticultural products”, Postharvest Biology and Technology, vol. 128, pp. 33–43, 2017.
, , “Automated quality control and selection ”, U.S. Patent PCT/EP2016/0557182016.
, “Building a statistical shape model of the interior and exterior of the bell pepper”, 30th Targeted Technologies for Sustainable Food Systems (EFFoST) International Conference, vol. 28-30 November. Vienna, Austria, 2016.
, “Combining 3D vision and X-ray radiography for internal quality inspection of foods”, 30th Targeted Technologies for Sustainable Food Systems (EFFoST) International Conference. Austrian Economic Chamber, Vienna, Austria, 2016.
, “Fast inline inspection by neural network based filtered backprojection: Application to apple inspection”, Case Studies in Nondestructive Testing and Evaluation, vol. 6, pp. 14–20, 2016. Download paper (726.63 KB)
, “Fast X-ray Computed Tomography via Image Completion”, in 6th Conference on Industrial Computed Tomography(iCT), Wels, Austria, 2016, pp. 1-5.
, “In-line NDT with X-Ray CT combining sample rotation and translation”, NDT & E International, vol. 89, pp. 89–98, 2016.
, “A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs”, Postharvest Biology and Technology, vol. 112, pp. 205-214, 2016.
, “Understanding microstructural deformation of apple tissue from 4D micro-CT imaging”, ISHS Symposium 2016: Sensing Plant Water Status - Methods and Applications in Horticultural Science. Potsdam, Germany, 5-7 October. 2016.
, “Building a Statistical Shape Model of the Apple from Corresponded Surfaces”, Chemical Engineering Transactions, vol. 44, pp. 49-54, 2015. Download paper (696.94 KB)
, “Fast Neural Network Based X-Ray Tomography of Fruit on a Conveyor Belt”, Chemical Engineering Transactions, vol. 44, pp. 181-186, 2015.
, “Neural Network Based X-Ray Tomography for Fast Inspection of Apples on a Conveyor Belt”, in IEEE International Conference on Image Processing, 2015, pp. 917-921.
, “Online Tomato Inspection Using X-Ray Radiographies and 3- Dimensional Shape Models”, Chemical Engineering Transactions, vol. 44, pp. 37-42, 2015.
, “Tomography for in-line product inspection”, U.S. Patent EP3106863A12015.
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