Pattern recognition and beyond: Alfredo Petrosino's scientific results

Abstract We summarize the main scientific contributions of our friend and colleague Alfredo Petrosino, full professor in computer science at the University of Naples Parthenope, Italy. They mainly cover topics in high-performance computing, neural network models, soft and granular computing, computer vision, and machine learning. We also highlight how most of his research activity lays the foundation for biometry and its applications.

[1]  Lucia Maddalena,et al.  Scene background initialization: A taxonomy , 2017, Pattern Recognit. Lett..

[2]  Alfredo Petrosino,et al.  Strict Pyramidal Deep Architectures for Person Re-identification , 2016, Advances in Neural Networks.

[3]  Gerardo Iovane,et al.  A Novel Blockchain Scheme Combining Prime Numbers and Iris for Encrypting Coding , 2019, 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech).

[4]  Sankar K. Pal,et al.  Guest Editorial on Decision Making in Human and Machine Vision , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[5]  Lucia Maddalena,et al.  People counting by learning their appearance in a multi-view camera environment , 2014, Pattern Recognit. Lett..

[6]  Silvio Savarese,et al.  A Bayesian Approach to Tracking Learning Detection , 2013, ICIAP.

[7]  Alfredo Petrosino,et al.  A Rough Fuzzy Perspective to Dimensionality Reduction , 2012, CHDD.

[8]  Alfredo Petrosino,et al.  TGLSTM: A time based graph deep learning approach to gait recognition , 2019, Pattern Recognit. Lett..

[9]  Alfredo Petrosino,et al.  Encoding nondeterministic fuzzy tree automata into recursive neural networks , 2004, IEEE Transactions on Neural Networks.

[10]  Alfredo Petrosino,et al.  Fuzzy Connectivity and Its Application to Image Segmentation , 2005, WIRN/NAIS.

[11]  Jiri Matas,et al.  The Enhanced Flock of Trackers , 2014, Registration and Recognition in Images and Videos.

[12]  Alfredo Petrosino,et al.  Watch Out: Embedded Video Tracking with BST for Unmanned Aerial Vehicles , 2018, J. Signal Process. Syst..

[13]  Alfredo Petrosino,et al.  Protein Structural Blocks Representation and Search through Unsupervised NN , 2012, ICANN.

[14]  Roberto Tagliaferri,et al.  Neural associative memories with minimum connectivity , 1992, Neural Networks.

[15]  Alfredo Petrosino,et al.  An Heuristic Approach to Page Recommendation in Web Usage Mining , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[16]  Luis Salgado,et al.  A Benchmarking Framework for Background Subtraction in RGBD Videos , 2017, ICIAP Workshops.

[17]  Lucia Maddalena,et al.  A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications , 2008, IEEE Transactions on Image Processing.

[18]  Alfredo Petrosino,et al.  Multi-feature adaptive classifiers for SAR image segmentation , 1997, Neurocomputing.

[19]  Lucia Maddalena,et al.  Video-Based Access Control by Automatic License Plate Recognition , 2015, Advances in Neural Networks.

[20]  Alessandro Rozza,et al.  A Novel Graph Embedding Framework for Object Recognition , 2014, ECCV Workshops.

[21]  Gabriella Sanniti di Baja,et al.  A Supervised Approach to 3D Structural Classification of Proteins , 2013, ICIAP Workshops.

[22]  Alfredo Petrosino,et al.  Kernel Methods for Graphs: A Comprehensive Approach , 2008, KES.

[23]  Alfredo Petrosino,et al.  Attributed Relational SIFT-Based Regions Graph for Art Painting Retrieval , 2013, ICIAP.

[24]  Alfredo Petrosino,et al.  Feature Selection Through Composition of Rough-Fuzzy Sets , 2016, WILF.

[25]  Alfredo Petrosino,et al.  Competitive neural networks on message-passing parallel computers , 1993, Concurr. Pract. Exp..

[26]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[27]  Andrzej Skowron,et al.  Data science, big data and granular mining , 2015, Pattern Recognit. Lett..

[28]  Alfredo Petrosino,et al.  Multi-scale Kernel Operators for Reflection and Rotation Symmetry: Further Achievements , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[29]  Lucia Maddalena,et al.  The SOBS algorithm: What are the limits? , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[30]  Sankar K. Pal,et al.  Fuzzy Image Processing and Recognition: Uncertainty Handling and Applications , 2001, Int. J. Image Graph..

[31]  Sankar K. Pal,et al.  A Rough Set Approach to Spatio-temporal Outlier Detection , 2011, WILF.

[32]  Takeo Kanade,et al.  Layered detection for multiple overlapping objects , 2002, Object recognition supported by user interaction for service robots.

[33]  Sankar K. Pal,et al.  A Rough-Fuzzy HSV Color Histogram for Image Segmentation , 2011, ICIAP.

[34]  Raffaele Montella,et al.  DeepNautilus: A Deep Learning Based System for Nautical Engines' Live Vibration Processing , 2019, CAIP.

[35]  Luca Lombardi,et al.  Distributed recursive learning for shape recognition through multiscale trees , 2007, Image Vis. Comput..

[36]  Ihsan Ullah,et al.  Spatiotemporal Features Learning with 3DPyraNet , 2016, ACIVS.

[37]  Lucia Maddalena,et al.  Real-Time Stopped Object Detection by Neural Dual Background Modeling , 2010, Euro-Par Workshops.

[38]  Alfredo Petrosino,et al.  P-AFLC: a parallel scalable fuzzy clustering algorithm , 2004, ICPR 2004.

[39]  Alfredo Petrosino,et al.  Granular trajectory based anomaly detection for surveillance , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[40]  Alfredo Petrosino,et al.  Salient feature based graph matching for person re-identification , 2015, Pattern Recognit..

[41]  Riccardo Distasi,et al.  Optimization of Score-Level Biometric Data Fusion by Constraint Construction Training , 2019, iSCI.

[42]  Alfredo Petrosino,et al.  A parallel fuzzy scale-space approach to the unsupervised texture separation , 2002, Pattern Recognit. Lett..

[43]  Alfredo Petrosino,et al.  A Neuro Fuzzy Approach for Handling Structured Data , 2008, SUM.

[44]  Michael Felsberg,et al.  The Visual Object Tracking VOT2013 Challenge Results , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[45]  Sunanda Mitra,et al.  Adaptive fuzzy leader clustering of complex data sets in pattern recognition , 1992, IEEE Trans. Neural Networks.

[46]  Nilanjan Dey,et al.  Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review , 2019, IEEE Sensors Journal.

[47]  Lucia Maddalena,et al.  Stopped Object Detection by Learning Foreground Model in Videos , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[48]  Lucia Maddalena,et al.  Extensive Benchmark and Survey of Modeling Methods for Scene Background Initialization , 2017, IEEE Transactions on Image Processing.

[49]  Lucia Maddalena,et al.  Extracting a background image by a multi-modal scene background model , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[50]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[51]  Alfredo Petrosino,et al.  A Rough Fuzzy Neural Based Approach to Face Detection , 2010, IPCV.

[52]  Alfredo Petrosino,et al.  Parallel image understanding algorithms on MIMD multicomputers , 1998, Computing.

[53]  Marco Gori,et al.  Semantic-based regularization for learning and inference , 2017, Artif. Intell..

[54]  Alfredo Petrosino,et al.  Parallel processing for image and video processing: Issues and challenges , 2008, Parallel Comput..

[55]  Lucia Maddalena,et al.  Background Model Initialization for Static Cameras , 2014 .

[56]  Witold Pedrycz,et al.  Uninorm Based Fuzzy Network for Tree Data Structures , 2009, WILF.

[57]  Gabriella Sanniti di Baja,et al.  GRUNTS: Graph Representation for UNsupervised Temporal Segmentation , 2015, ICIAP.

[58]  Alfredo Petrosino,et al.  Adjusted F-measure and kernel scaling for imbalanced data learning , 2014, Inf. Sci..

[59]  Lucia Maddalena,et al.  Self-organizing background subtraction using color and depth data , 2018, Multimedia Tools and Applications.

[60]  Lucia Maddalena,et al.  Neural Moving Object Detection by Pan-Tilt-Zoom Cameras , 2012, WIRN.

[61]  Zhenyu He,et al.  The Visual Object Tracking VOT2016 Challenge Results , 2016, ECCV Workshops.

[62]  Alfredo Petrosino,et al.  Finding Hidden Events in Astrophysical Data using PCA and Mixture of Gaussians Clustering , 2002, Pattern Analysis & Applications.

[63]  Arun Ross,et al.  Periocular Biometrics in the Visible Spectrum , 2011, IEEE Transactions on Information Forensics and Security.

[64]  Alfredo Petrosino,et al.  Protein motifs retrieval by SS terns occurrences , 2013, Pattern Recognit. Lett..

[65]  Alfredo Petrosino,et al.  WhoAreYou (WAY): A Mobile CUDA Powered Picture ID Card Recognition System , 2017, ICIAP Workshops.

[66]  Lucia Maddalena,et al.  Restoration of blue scratches in digital image sequences , 2008, Image Vis. Comput..

[67]  Alfredo Petrosino,et al.  Human activity modeling by spatio temporal textural appearance , 2013, Pattern Recognit. Lett..

[68]  Sankar K. Pal,et al.  The Role of Soft Computing in Image Analysis , 2012, Handbook of Soft Computing for Video Surveillance.

[69]  Ihsan Ullah,et al.  A Strict Pyramidal Deep Neural Network for Action Recognition , 2015, ICIAP.

[70]  C. A. Murthy,et al.  Formulation of a multivalued recognition system , 1992, IEEE Trans. Syst. Man Cybern..

[71]  Lucia Maddalena,et al.  Towards Benchmarking Scene Background Initialization , 2015, ICIAP Workshops.

[72]  Alfredo Petrosino,et al.  A two-subcycle thinning algorithm and its parallel implementation on SIMD machines , 2000, IEEE Trans. Image Process..

[73]  Alfredo Petrosino,et al.  The orientation matching approach to circular object detection , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[74]  Antonio Maratea,et al.  Triadic Motifs in the Partitioned World Trade Web , 2016, EUSPN/ICTH.

[75]  Arun Ross,et al.  Handbook of Multibiometrics , 2006, The Kluwer international series on biometrics.

[76]  Soumitra Dutta,et al.  Class-dependent rough-fuzzy granular space, dispersion index and classification , 2012, Pattern Recognit..

[77]  Alfredo Petrosino,et al.  Fuzzy modeling for data cleaning in sensor networks , 2008, Int. J. Hybrid Intell. Syst..

[78]  Alfredo Petrosino,et al.  Neural recognition in a pyramidal structure , 2002, IEEE Trans. Neural Networks.

[79]  Azriel Rosenfeld,et al.  Image enhancement and thresholding by optimization of fuzzy compactness , 1988, Pattern Recognit. Lett..

[80]  Isabelle Bloch,et al.  A Fuzzy Mathematical Morphology Approach to Multiseeded Image Segmentation , 2005, WILF.

[81]  Alfredo Petrosino,et al.  MATRIOSKA: A Multi-level Approach to Fast Tracking by Learning , 2013, ICIAP.

[82]  Alfredo Petrosino,et al.  A Neural Based WSN Mote Trajectory Reconstruction for Mining Periodic Patterns , 2008, WIRN.

[83]  Alfredo Petrosino,et al.  High performance missing data detection and interpolation for video compression and restoration applications , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[84]  Alfredo Petrosino,et al.  The Matrioska Tracking Algorithm on LTDT2014 Dataset , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[85]  Ajita Rattani,et al.  Ocular biometrics in the visible spectrum: A survey , 2017, Image Vis. Comput..

[86]  Alfredo Petrosino,et al.  Iris recognition through machine learning techniques: A survey , 2016, Pattern Recognit. Lett..

[87]  Sankar K. Pal,et al.  Rough Sets, Kernel Set, and Spatiotemporal Outlier Detection , 2014, IEEE Transactions on Knowledge and Data Engineering.

[88]  Alfredo Petrosino,et al.  Content-based Image Retrieval by a Fuzzy Scale-space Approach , 2006, Int. J. Pattern Recognit. Artif. Intell..

[89]  Lucia Maddalena,et al.  Background Subtraction for Moving Object Detection in RGBD Data: A Survey , 2018, J. Imaging.

[90]  Alfredo Petrosino,et al.  Clustering Local Motion Estimates for Robust and Efficient Object Tracking , 2014, ECCV Workshops.

[91]  Alessandro Rozza,et al.  A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning , 2014, 2014 22nd International Conference on Pattern Recognition.

[92]  Michael Felsberg,et al.  The Visual Object Tracking VOT2017 Challenge Results , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[93]  Fabio Narducci,et al.  An UAV Autonomous Warehouse Inventorying by Deep Learning , 2019, ICIAP.

[94]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[95]  Michael Felsberg,et al.  The Sixth Visual Object Tracking VOT2018 Challenge Results , 2018, ECCV Workshops.

[96]  Ihsan Ullah,et al.  EmoP3D: A Brain Like Pyramidal Deep Neural Network for Emotion Recognition , 2018, ECCV Workshops.

[97]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[98]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[99]  Alfredo Petrosino,et al.  Rough fuzzy set-based image compression , 2009, Fuzzy Sets Syst..

[100]  Alfredo Petrosino,et al.  Rough fuzzy set based scale space transforms and their use in image analysis , 2006, Int. J. Approx. Reason..

[101]  Lucia Maddalena,et al.  A fusion-based approach to digital movie restoration , 2009, Pattern Recognit..

[102]  Lucia Maddalena,et al.  A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection , 2010, Neural Computing and Applications.

[103]  Lucia Maddalena,et al.  The 3dSOBS+ algorithm for moving object detection , 2014, Comput. Vis. Image Underst..

[104]  Zhenyu He,et al.  The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results , 2016, ECCV Workshops.

[105]  Lucia Maddalena,et al.  Moving Object Detection for Real-Time Applications , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[106]  Gabriella Sanniti di Baja,et al.  Iris Detection through Watershed Segmentation , 2014, BIOMET.