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Barbara Caputo
Researcher at Polytechnic University of Turin
Publications - 257
Citations - 14179
Barbara Caputo is an academic researcher from Polytechnic University of Turin. The author has contributed to research in topics: Support vector machine & Domain (software engineering). The author has an hindex of 53, co-authored 257 publications receiving 11628 citations. Previous affiliations of Barbara Caputo include Smith-Kettlewell Institute & Sapienza University of Rome.
Papers
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Proceedings ArticleDOI
Recognizing human actions: a local SVM approach
TL;DR: This paper construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition and presents the presented results of action recognition.
Proceedings ArticleDOI
Domain Generalization by Solving Jigsaw Puzzles
TL;DR: This model learns the semantic labels in a supervised fashion, and broadens its understanding of the data by learning from self-supervised signals how to solve a jigsaw puzzle on the same images, which helps the network to learn the concepts of spatial correlation while acting as a regularizer for the classification task.
Journal ArticleDOI
Electromyography data for non-invasive naturally-controlled robotic hand prostheses
Manfredo Atzori,Arjan Gijsberts,Claudio Castellini,Barbara Caputo,Anne-Gabrielle Mittaz Hager,Simone Elsig,Giorgio Giatsidis,Franco Bassetto,Henning Müller +8 more
TL;DR: This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses.
Book ChapterDOI
On the Significance of Real‐World Conditions for Material Classification
TL;DR: A first contribution of this paper is to further advance the state-of-the-art by applying Support Vector Machines to this problem and record the best results to date on the CUReT database.
Proceedings ArticleDOI
Class-specific material categorisation
TL;DR: This paper adopts an appearance-based strategy, and conducts experiments on a new database which contains several samples of each of eleven material categories, imaged under a variety of pose, illumination and scale conditions, demonstrating that very significant gains can be achieved via different SVM-based classification techniques.