1 code implementation • 24 Jan 2022 • Loris Nanni, Michelangelo Paci, Sheryl Brahnam, Alessandra Lumini
These novel methods are based on the Fourier Transform (FT), the Radon Transform (RT) and the Discrete Cosine Transform (DCT).
no code implementations • 24 Dec 2021 • Loris Nanni, Daniela Cuza, Alessandra Lumini, Andrea Loreggia, Sheryl Brahnam
Semantic segmentation consists in classifying each pixel of an image by assigning it to a specific label chosen from a set of all the available ones.
no code implementations • 9 Oct 2021 • Loris Nanni, Alessandra Lumini, Alessandro Manfe, Riccardo Rampon, Sheryl Brahnam, Giorgio Venturin
Multilabel learning tackles the problem of associating a sample with multiple class labels.
no code implementations • 28 Aug 2021 • Loris Nanni, Alessandro Manfe, Gianluca Maguolo, Alessandra Lumini, Sheryl Brahnam
The best performing ensemble, which combined the CNNs using the different augmentation methods and the two new Adam variants proposed here, achieved state of the art on both insect data sets: 95. 52% on Deng and 73. 46% on IP102, a score on Deng that competed with human expert classifications.
no code implementations • 2 Apr 2021 • Alessandra Lumini, Loris Nanni, Gianluca Maguolo
The basic architecture in image segmentation consists of an encoder and a decoder: the first uses convolutional filters to extract features from the image, the second is responsible for generating the final output.
no code implementations • 26 Mar 2021 • Loris Nanni, Gianluca Maguolo, Alessandra Lumini
In this work, we compare Adam based variants based on the difference between the present and the past gradients, the step size is adjusted for each parameter.
no code implementations • 22 Jan 2021 • Loris Nanni, Alessandra Lumini, Sheryl Brahnam
Motivation: Automatic Anatomical Therapeutic Chemical (ATC) classification is a critical and highly competitive area of research in bioinformatics because of its potential for expediting drug develop-ment and research.
no code implementations • 24 Nov 2020 • Loris Nanni, Alessandra Lumini, Stefano Ghidoni, Gianluca Maguolo
In this paper we classify biomedical images using ensembles of neural networks.
no code implementations • 15 Aug 2019 • Alessandra Lumini, Loris Nanni, Gianluca Maguolo
We study how to create an ensemble based of different CNN models, fine tuned on several datasets with the aim of exploiting their diversity.
no code implementations • 9 Aug 2019 • Alessandra Lumini, Loris Nanni, Alice Codogno, Filippo Berno
In this work we propose a novel post processing approach for skin detectors based on trained morphological operators.
no code implementations • 11 Jun 2019 • Loris Nanni, Alessandra Lumini, Federica Pasquali, Sheryl Brahnam
In this paper we address the problem of protein classification starting from a multi-view 2D representation of proteins.
no code implementations • 20 Jul 2018 • Loris Nanni, Alessandra Lumini, Stefano Ghidoni
The aim of this work is to propose an ensemble of descriptors for Melanoma Classification, whose performance has been evaluated on validation and test datasets of the melanoma challenge 2018.
no code implementations • 7 Feb 2018 • Alessandra Lumini, Loris Nanni
Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection.