Michele Svanera

Michele Svanera
University of Glasgow | UofG · School of Psychology & Neuroscience

Ph.D.
Lecturer in Machine Learning for Neuroscience, University of Glasgow (UK)

About

20
Publications
9,401
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
214
Citations
Introduction
Wandering across Cognitive Neuroscience and Artificial Intelligence (specifically Deep Learning), I address the goal of understanding human vision conducting behavioural, f/MRI, and computational modelling studies. For more visit michelesvanera.org/research/
Additional affiliations
January 2013 - October 2016
Università degli Studi di Brescia
Position
  • PhD Student

Publications

Publications (20)
Conference Paper
Full-text available
Deep neural networks have been developed drawing inspiration from the brain visual pathway, implementing an end-to-end approach: from image data to video object classes. However building an fMRI decoder with the typical structure of Convolutional Neural Network (CNN), i.e. learning multiple level of representations, seems impractical due to lack of...
Article
Full-text available
The scale of shot, i.e. the apparent distance of the camera from the main subject of a scene, is one of the main stylistic and narrative functions of audiovisual products, conveying meaning and inducing the viewer’s emotional state. The statistical distribution of different shot scales in a film may be an important identifier of an individual film,...
Conference Paper
Hair is one of the elements that mostly characterize people appearance. Being able to detect hair in images can be useful in many applications, such as face recognition, gender classification, and video surveillance. To this purpose we propose a novel multi-class image database for hair detection in the wild, called Figaro. We tackle the problem of...
Article
Full-text available
Ultra-high-field magnetic resonance imaging (MRI) enables sub-millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso-scale. An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures...
Article
Full-text available
The promise of artificial intelligence in understanding biological vision relies on the comparison of computational models with brain data with the goal of capturing functional principles of visual information processing. Convolutional neural networks (CNN) have successfully matched the transformations in hierarchical processing occurring along the...
Preprint
A bstract Ultra high field MRI enables sub-millimetre resolution imaging of human brain, allowing to disentangle complex functional circuits across different cortical depths. The capability of using these innovative scanners at 7 Tesla (7T) poses new challenges, as for example those related to the current lack of standardised acquisition protocols,...
Preprint
A bstract The promise of artificial intelligence in understanding biological vision relies on the comparison of computational models with brain data that captures functional principles of visual information processing. Deep neural networks (DNN) have successfully matched the transformations in hierarchical processing occurring along the brain’s fee...
Article
Full-text available
Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan al...
Article
This article provides evidence for the existence of a robust “brainprint” of cinematic shot-scales that generalizes across movies, genres, and viewers. We applied a machine-learning method on a dataset of 234 fMRI scans taken during the viewing of a movie excerpt. Based on a manual annotation of shot-scales in five movies, we generated a computatio...
Article
Background: Deep neural networks have revolutionised machine learning, with unparalleled performance in object classification. However, in brain imaging (e.g., fMRI), the direct application of Convolutional Neural Networks (CNN) to decoding subject states or perception from imaging data seems impractical given the scarcity of available data. New...
Conference Paper
Functional and Structural MRI studies benefit from good segmentation of grey and white matter, for example to allow for cortex-based alignment. Automatic segmentation tools apply (multi-) atlas-based segmentation strategies that often lack the accuracy on difficult-to-segment brain structures and take several hours of processing. Moreover, these al...
Preprint
Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan al...
Article
We show how to perform automatic attribution of movie's authorship by the statistical analysis of two simple formal features: the length of camera takes (shot duration), and the distance between the camera and the subject (shot scale). Experiments include 143 films by 8 distinguishable directors over 70 years of historiography of author cinema.
Preprint
*** link: https://doi.org/10.1101/535377 *** Background: Deep neural networks have revolutionised machine learning, with unparalleled performance in object classification. However, in brain imaging (e.g. fMRI), the direct application of Convolutional Neural Networks (CNN) to decoding subject states or perception from imaging data seems impractica...
Preprint
Full-text available
We show how low-level formal features, such as shot duration, meant as length of camera takes, and shot scale, i.e. the distance between the camera and the subject, are distinctive of a director's style in art movies. So far such features were thought of not having enough varieties to become distinctive of an author. However our investigation on th...
Article
Hair highly characterises human appearance. Hair detection in images is useful for many applications, such as face and gender recognition, video surveillance, and hair modelling. We tackle the problem of hair analysis (detection, segmentation, and hairstyle classification) from unconstrained view by relying only on textures, without a-priori inform...
Article
Full-text available
Major methodological advancements have been recently made in the field of neural decoding, which is concerned with the reconstruction of mental content from neuroimaging measures. However, in the absence of a large-scale examination of the validity of the decoding models across subjects and content, the extent to which these models can be generaliz...
Conference Paper
The ability to characterize a film, in terms of its narrative and style, is becoming a necessity especially for developing personal video recommendation systems to better deliver on-demand Internet streaming media. Among the set of identifiable stylistic features which play an important role in the film's emotional effects, the use of Over-the-shou...

Network

Cited By

Projects