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358 results on '"Scotti, Fabio"'

1. Weakly Contrastive Learning via Batch Instance Discrimination and Feature Clustering for Small Sample SAR ATR

Author

Zhai, Yikui, Zhou, Wenlve, Sun, Bing, Li, Jingwen, Ke, Qirui, Ying, Zilu, Gan, Junying, Mai, Chaoyun, Labati, Ruggero Donida, Piuri, Vincenzo, and Scotti, Fabio

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In recent years, impressive performance of deep learning technology has been recognized in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). Since a large amount of annotated data is required in this technique, it poses a trenchant challenge to the issue of obtaining a high recognition rate through less labeled data. To overcome this problem, inspired by the contrastive learning, we proposed a novel framework named Batch Instance Discrimination and Feature Clustering (BIDFC). In this framework, different from that of the objective of general contrastive learning methods, embedding distance between samples should be moderate because of the high similarity between samples in the SAR images. Consequently, our flexible framework is equipped with adjustable distance between embedding, which we term as weakly contrastive learning. Technically, instance labels are assigned to the unlabeled data in per batch and random augmentation and training are performed few times on these augmented data. Meanwhile, a novel Dynamic-Weighted Variance loss (DWV loss) function is also posed to cluster the embedding of enhanced versions for each sample. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database indicate a 91.25% classification accuracy of our method fine-tuned on only 3.13% training data. Even though a linear evaluation is performed on the same training data, the accuracy can still reach 90.13%. We also verified the effectiveness of BIDFC in OpenSarShip database, indicating that our method can be generalized to other datasets. Our code is avaliable at: https://github.com/Wenlve-Zhou/BIDFC-master.

Published
2024
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6. GradVAE: An Explainable Variational Autoencoder Model Based on Online Attentions Preserving Curvatures of Representations

Author

Abukmeil, Mohanad, Ferrari, Stefano, Genovese, Angelo, Piuri, Vincenzo, Scotti, Fabio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sclaroff, Stan, editor, Distante, Cosimo, editor, Leo, Marco, editor, Farinella, Giovanni M., editor, and Tombari, Federico, editor

Published
2022
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7. Touchless Palmprint and Fingerprint Recognition

Author

Donida Labati, Ruggero, Genovese, Angelo, Piuri, Vincenzo, Scotti, Fabio, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nicopolitidis, Petros, editor, Misra, Sudip, editor, Yang, Laurence T., editor, Zeigler, Bernard, editor, and Ning, Zhaolng, editor

Published
2022
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8. Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Author

Donida Labati, Ruggero, Piuri, Vincenzo, Rundo, Francesco, Scotti, Fabio, Spampinato, Concetto, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Del Bimbo, Alberto, editor, Cucchiara, Rita, editor, Sclaroff, Stan, editor, Farinella, Giovanni Maria, editor, Mei, Tao, editor, Bertini, Marco, editor, Escalante, Hugo Jair, editor, and Vezzani, Roberto, editor

Published
2021
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9. Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification

Author

Rundo, Francesco, Trenta, Francesca, Leotta, Roberto, Spampinato, Concetto, Piuri, Vincenzo, Conoci, Sabrina, Labati, Ruggero Donida, Scotti, Fabio, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Del Bimbo, Alberto, editor, Cucchiara, Rita, editor, Sclaroff, Stan, editor, Farinella, Giovanni Maria, editor, Mei, Tao, editor, Bertini, Marco, editor, Escalante, Hugo Jair, editor, and Vezzani, Roberto, editor

Published
2021
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11. Computational Intelligence in Cloud Computing

Author

Donida Labati, Ruggero, Genovese, Angelo, Piuri, Vincenzo, Scotti, Fabio, Vishwakarma, Sarvesh, Rudas, Imre J., Series Editor, Szakál, Anikó, Series Editor, Batyrshin, Ildar, Editorial Board Member, Bokor, József, Editorial Board Member, De Baets, Bernard, Editorial Board Member, Fujita, Hamido, Editorial Board Member, Fukuda, Toshio, Editorial Board Member, Harashima, Fumio, Editorial Board Member, Hirota, Kaoru, Editorial Board Member, Pap, Endre, Editorial Board Member, Wilamowski, Bogdan M., Editorial Board Member, Baranyi, P., Advisory Editor, Bodenhofer, U., Advisory Editor, Fichtinger, G., Advisory Editor, Fullér, R., Advisory Editor, Galántai, A., Advisory Editor, Hluchý, L., Advisory Editor, Jamshidi, M. O., Advisory Editor, Kelemen, J., Advisory Editor, Kocur, D., Advisory Editor, Korondi, P., Advisory Editor, Kovács, G., Advisory Editor, Kóczy, L. T., Advisory Editor, Madarász, L., Advisory Editor, Nguyen, CH. C., Advisory Editor, Petriu, E., Advisory Editor, Precup, R.-E., Advisory Editor, Preitl, S., Advisory Editor, Prostean, O., Advisory Editor, Puri, V., Advisory Editor, Sallai, G. Y., Advisory Editor, Somló, J., Advisory Editor, Takács, M., Advisory Editor, Tar, J., Advisory Editor, Ungvari, L., Advisory Editor, Várkonyi-Kóczy, A. R., Advisory Editor, Várlaki, P., Advisory Editor, Vokorokos, L., Advisory Editor, Kovács, Levente, editor, and Haidegger, Tamás, editor

Published
2020
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14. Large-Scale High-Altitude UAV-Based Vehicle Detection via Pyramid Dual Pooling Attention Path Aggregation Network

Author

Ying, Zilu, Zhou, Jianhong, Zhai, Yikui, Quan, Hao, Li, Wenba, Genovese, Angelo, Piuri, Vincenzo, and Scotti, Fabio

Abstract

UAVs can collect vehicle data in high-altitude scenes, playing a significant role in intelligent urban management due to their wide of view. Nevertheless, the current datasets for UAV-based vehicle detection are acquired at altitude below 150 meters. This contrasts with the data perspective obtained from high-altitude scenes, potentially leading to incongruities in data distribution. Consequently, it is challenging to apply these datasets effectively in high-altitude scenes, and there is an ongoing obstacle. To resolve this challenge, we developed a comprehensive vehicle dataset named LH-UAV-Vehicle, specifically collected at flight altitudes ranging from 250 to 400 meters. Collecting data at higher flight altitudes offers a broader perspective, but it concurrently introduces complexity and diversity in the background, which consequently impacts vehicle localization and recognition accuracy. In response, we proposed the pyramid dual pooling attention path aggregation network (PDPA-PAN), an innovative framework that improves detection performance in high-altitude scenes by combining spatial and semantic information. Object attention integration in both spatial and channel dimensions is aimed by the pyramid dual pooling attention module (PDPAM), which is achieved through the parallel integration of two distinct attention mechanisms. Furthermore, we have individually developed the pyramid pooling attention module (PPAM) and the dual pooling attention module (DPAM). The PPAM emphasizes channel attention, while the DPAM prioritizes spatial attention. This design aims to enhance vehicle information and suppress background interference more effectively. Extensive experiments conducted on the LH-UAV-Vehicle conclusively demonstrate the efficacy of the proposed vehicle detection method. Our code and dataset can be found at https://github.com/yikuizhai/PDPA-PAN.

Published
2024
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16. A Scheme for Fingerphoto Recognition in Smartphones

Author

Donida Labati, Ruggero, Genovese, Angelo, Piuri, Vincenzo, Scotti, Fabio, Kang, Sing Bing, Series Editor, Singh, Sameer, Founding Editor, Bischof, Horst, Advisory Editor, Bowden, Richard, Advisory Editor, Dickinson, Sven, Advisory Editor, Jia, Jiaya, Advisory Editor, Lee, Kyoung Mu, Advisory Editor, Sato, Yoichi, Advisory Editor, Schiele, Bernt, Advisory Editor, Sclaroff, Stan, Advisory Editor, Rattani, Ajita, editor, Derakhshani, Reza, editor, and Ross, Arun, editor

Published
2019
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21. A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN

Author

Zhai, Yikui, Cao, He, Cao, Lu, Ma, Hui, Gan, Junyin, Zeng, Junying, Piuri, Vincenzo, Scotti, Fabio, Deng, Wenbo, Zhi, Yihang, Wang, Jinxin, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Zhou, Jie, editor, Wang, Yunhong, editor, Sun, Zhenan, editor, Jia, Zhenhong, editor, Feng, Jianjiang, editor, Shan, Shiguang, editor, Ubul, Kurban, editor, and Guo, Zhenhua, editor

Published
2018
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22. Advanced Biometric Technologies: Emerging Scenarios and Research Trends

Author

Genovese, Angelo, Muñoz, Enrique, Piuri, Vincenzo, Scotti, Fabio, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Samarati, Pierangela, editor, Ray, Indrajit, editor, and Ray, Indrakshi, editor

Published
2018
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30. Deep Convolutional Neural Network for Facial Expression Recognition

Author

Zhai, Yikui, Liu, Jian, Zeng, Junying, Piuri, Vincenzo, Scotti, Fabio, Ying, Zilu, Xu, Ying, Gan, Junying, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Zhao, Yao, editor, Kong, Xiangwei, editor, and Taubman, David, editor

Published
2017
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31. SAR Automatic Target Recognition Based on Deep Convolutional Neural Network

Author

Xu, Ying, Liu, Kaipin, Ying, Zilu, Shang, Lijuan, Liu, Jian, Zhai, Yikui, Piuri, Vincenzo, Scotti, Fabio, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Zhao, Yao, editor, Kong, Xiangwei, editor, and Taubman, David, editor

Published
2017
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36. Introduction

Author

Genovese, Angelo, Piuri, Vincenzo, Scotti, Fabio, Jajodia, Sushil, Series editor, Genovese, Angelo, Piuri, Vincenzo, and Scotti, Fabio

Published
2014
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38. Palmprint Biometrics

Author

Genovese, Angelo, Piuri, Vincenzo, Scotti, Fabio, Jajodia, Sushil, Series editor, Genovese, Angelo, Piuri, Vincenzo, and Scotti, Fabio

Published
2014
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40. Biometric Systems

Author

Genovese, Angelo, Piuri, Vincenzo, Scotti, Fabio, Jajodia, Sushil, Series editor, Genovese, Angelo, Piuri, Vincenzo, and Scotti, Fabio

Published
2014
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44. Automated Border Control Systems: Biometric Challenges and Research Trends

Author

Labati, Ruggero Donida, Genovese, Angelo, Muñoz, Enrique, Piuri, Vincenzo, Scotti, Fabio, Sforza, Gianluca, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Jajoda, Sushil, editor, and Mazumdar, Chandan, editor

Published
2015
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45. Conclusions

Author

Genovese, Angelo, Piuri, Vincenzo, Scotti, Fabio, Jajodia, Sushil, Series editor, Genovese, Angelo, Piuri, Vincenzo, and Scotti, Fabio

Published
2014
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46. DS-HyFA-Net: A Deeply Supervised Hybrid Feature Aggregation Network With Multiencoders for Change Detection in High-Resolution Imagery

Author

Ying, Zilu, Xian, Tingfeng, Zhai, Yikui, Jia, Xudong, Zhang, Hongsheng, Pan, Jiahao, Coscia, Pasquale, Genovese, Angelo, Piuri, Vincenzo, and Scotti, Fabio

Abstract

With the advancement of deep learning (DL) technologies, remarkable progress has been achieved in change detection (CD). Existing DL-based methods primarily focus on the discrepancy in bitemporal images, while overlooking the commonality in bitemporal images. However, one of the reasons hindering the improvement of CD performance is the inadequate utilization of image information. To address the above issue, we propose a Deeply Supervised Hybrid Feature Aggregation Network (DS-HyFA-Net). This network predicts changes by integrating the distinctness and the commonality in bitemporal images. Specifically, the DS-HyFA-Net primarily consists of a set of encoders and a Hybrid Feature Aggregation (HyFA) module. It uses a Siamese encoder (or Encoder I) and a specialized encoder (or Encoder II) to extract distinct and common features (CFs) in bitemporal images, respectively. The HyFA module efficiently aggregates distinct and common features (or hybrid features) and generates a change map using a predictor. In addition, a common feature learning strategy (CFLS) is introduced, based on deeply supervised (DS) techniques, to guide Encoder II in learning CFs. Experimental results on three well-recognized datasets demonstrate the effectiveness of the innovative DS-HyFA-Net, achieving F1-Scores of 93.33% on WHU-CD, 90.98% on LEVIR-CD, and 81.14% on SYSU-CD. Our code is available at https://github.com/yikuizhai/DS-HyFA-Net.

Published
2024
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47. Learning to Count Arbitrary Industrial Manufacturing Workpieces

Author

Jiang, Ziyi, Zhai, Yikui, Ke, Feng, Zhou, Jianhong, Genovese, Angelo, Piuri, Vincenzo, and Scotti, Fabio

Abstract

Man-made workpiece counting is a routine job for manufactory workers; however, this is an error-prone task. In this article, we are interested in detecting and counting arbitrary workpieces in industrial manufacturing. Therefore, we construct a comprehensive and large-scale open-world public benchmark dataset for workpiece counting, called workpiece counting dataset, which includes 121 475 instances of workpieces from 351 different categories. We also propose a novel method for workpiece detection and counting, named two-stage workpiece counting network. The first stage of the network is to develop a class-agnostic detector to localize each workpiece instance, followed by the second stage to employ an unsupervised deep clustering strategy with the backbone network pretrained in a workpiece convolutional autoencoder for decision boundary prediction, achieving workpiece clustering under unknown K values. Finally, our experiments show that the proposed method outperforms current mainstream methods, greatly enhancing the efficiency of factory operations.

Published
2024
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48. DGMA2-Net: A Difference-Guided Multiscale Aggregation Attention Network for Remote Sensing Change Detection

Author

Ying, Zilu, Tan, Zijun, Zhai, Yikui, Jia, Xudong, Li, Wenba, Zeng, Junying, Genovese, Angelo, Piuri, Vincenzo, and Scotti, Fabio

Abstract

Remote sensing change detection (RSCD) focuses on identifying regions that have undergone changes between two remote sensing images captured at different times. Recently, convolutional neural networks (CNNs) have shown promising results in the challenging task of RSCD. However, these methods do not efficiently fuse bitemporal features and extract useful information that is beneficial to subsequent RSCD tasks. In addition, they did not consider multilevel feature interactions in feature aggregation and ignore relationships between difference features and bitemporal features, which, thus, affects the RSCD results. To address the above problems, a difference-guided multiscale aggregation attention network, DGMA2-Net, is developed. Bitemporal features at different levels are extracted through a Siamese convolutional network and a multiscale difference fusion module (MDFM) is then created to fuse bitemporal features and extract, in a multiscale manner, difference features containing rich contextual information. After the MDFM treatment, two difference aggregation modules (DAMs) are used to aggregate difference features at different levels for multilevel feature interactions. The features through DAMs are sent to the difference-enhanced attention modules (DEAMs) to strengthen the connections between bitemporal features and difference features and further refine change features. Finally, refined change features are superimposed from deep to shallow and a change map is produced. In validating the effectiveness of DGMA2-Net, a series of experiments are conducted on three public RSCD benchmark datasets [LEVIR building change detection dataset (LEVIR-CD), Wuhan University building change detection dataset (BCDD), and Sun Yat-Sen University dataset (SYSU-CD)]. The experimental results demonstrate that DGMA2-Net surpasses the current eight state-of-the-art methods in RSCD. Our code is released at https://github.com/yikuizhai/DGMA2-Net.

Published
2024
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50. Intelligent Traction Inverter in Next Generation Electric Vehicles: The Health Monitoring of Silicon-Carbide Power Modules

Author

Pino, Carmelo, Sitta, Alessandro, Castagnolo, Giulia, Messina, Angelo A., Coffa, Salvatore, Calabretta, Michele, Scotti, Fabio, Genovese, Angelo, Piuri, Vincenzo, Spampinato, Concetto, and Rundo, Francesco

Abstract

In automotive and industrial domains, the “health monitoring” or “condition monitoring” of electronic devices is gradually playing a key role in manufacturing processes and innovation roadmaps. The concept of health monitoring is often related to the so-called “residual lifetime” of the monitored system. In this work, the authors have designed a deep learning system for the health monitoring of power devices in Silicon Carbide (SiC) technology used in the Traction Inverter Systems of the latest generation electric cars. A Temporal Fusion Transformer embedding such layers of Temporal Convolutional Network with a Multi-Head Attention block for the robust lifetime assessment of SiC power devices, is proposed. Specifically, the designed system predicts such future samples of the ON-state voltage between drain and source of the low-side part of the SiC power module $Vds_{LS}$, in half-bridge configuration. Extensive literature confirmed that the $Vds_{LS}$ signal can be efficiently used as a robust predictive device-degradation marker. Through the learning of the temporal feature relationships at different scales and the intelligent selection of relevant input features, the proposed solution will discard unnecessary input dynamics building a multi-step predictive model of the $Vds_{LS}$ signal, significantly more performing than the existing state-of-the-art architectures. The proposed deep pipeline has been tested on several ACEPACK DRIVE SiC power modules delivered by STMicroelectronics, with an average error of about 0.2%, confirming the effectiveness of the proposed system.

Published
2023
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