scispace - formally typeset
Search or ask a question
Author

Loris Nanni

Bio: Loris Nanni is an academic researcher from University of Padua. The author has contributed to research in topics: Support vector machine & Convolutional neural network. The author has an hindex of 49, co-authored 288 publications receiving 8004 citations. Previous affiliations of Loris Nanni include Missouri State University & University of Bologna.


Papers
More filters
Journal ArticleDOI
TL;DR: The results show that the novel variant named elongated quinary patterns (EQP) is a very performing method among those proposed in this work for extracting information from a texture in all the tested datasets.

490 citations

Journal ArticleDOI
TL;DR: A generic computer vision system designed for exploiting trained deep Convolutional Neural Networks as a generic feature extractor and mixing these features with more traditional hand-crafted features is presented, demonstrating the generalizability of the proposed approach.

376 citations

Book ChapterDOI
TL;DR: It is shown experimentally that the machine expert based on local information outperforms the system based on global analysis when enough training data is available and it is found that global analysis is more appropriate in the case of small training set size.
Abstract: An on-line signature verification system exploiting both local and global information through decision-level fusion is presented. Global information is extracted with a feature-based representation and recognized by using Parzen Windows Classifiers. Local information is extracted as time functions of various dynamic properties and recognized by using Hidden Markov Models. Experimental results are given on the large MCYT signature database (330 signers, 16500 signatures) for random and skilled forgeries. Feature selection experiments based on feature ranking are carried out. It is shown experimentally that the machine expert based on local information outperforms the system based on global analysis when enough training data is available. Conversely, it is found that global analysis is more appropriate in the case of small training set size. The two proposed systems are also shown to give complementary recognition information which is successfully exploited using decision-level score fusion.

355 citations

Journal ArticleDOI
TL;DR: The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach and to compare several texture descriptors, it is shown that the proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches.
Abstract: The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors).Our experiments show that a fusion approach based on uniform local quinary pattern (LQP) and a rotation invariant local quinary pattern, where a bin selection based on variance is performed and Neighborhood Preserving Embedding (NPE) feature transform is applied, obtains a method that performs well on all tested datasets.As the classifier, we have tested a stand-alone support vector machine (SVM) and a random subspace ensemble of SVM. We compare several texture descriptors and show that our proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using six benchmark databases.

268 citations

Journal ArticleDOI
TL;DR: This paper introduces some ideas to improve the base BioHashing approach in order to maintain a very low equal error rate when nobody steals the Hash key, and to reach good performance also when an ''impostor'' steals theHash key.

266 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.
Abstract: The Pascal Visual Object Classes (VOC) challenge consists of two components: (i) a publicly available dataset of images together with ground truth annotation and standardised evaluation software; and (ii) an annual competition and workshop. There are five challenges: classification, detection, segmentation, action classification, and person layout. In this paper we provide a review of the challenge from 2008---2012. The paper is intended for two audiences: algorithm designers, researchers who want to see what the state of the art is, as measured by performance on the VOC datasets, along with the limitations and weak points of the current generation of algorithms; and, challenge designers, who want to see what we as organisers have learnt from the process and our recommendations for the organisation of future challenges. To analyse the performance of submitted algorithms on the VOC datasets we introduce a number of novel evaluation methods: a bootstrapping method for determining whether differences in the performance of two algorithms are significant or not; a normalised average precision so that performance can be compared across classes with different proportions of positive instances; a clustering method for visualising the performance across multiple algorithms so that the hard and easy images can be identified; and the use of a joint classifier over the submitted algorithms in order to measure their complementarity and combined performance. We also analyse the community's progress through time using the methods of Hoiem et al. (Proceedings of European Conference on Computer Vision, 2012) to identify the types of occurring errors. We conclude the paper with an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.

6,061 citations

01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

4,833 citations

01 Jan 2006

3,012 citations