|Wednesday 13 September|
|09:00||09:30||Opening and Overview|
|09:30||10:30||Invited Speaker: Irfan Essa, Georgia Institute of Technology, US|
|11:00||12:20||Oral Session 1: Face and Body Recognition|
|“Deep Face Model Compression Using Entropy-based Filter Selection” by Bingbing Han, Zhihong Zhang, Chuanyu Xu, Beizhan Wang, Guosheng Hu, Lu Bai, Qingqi Hong, Edwin Hancock|
|“Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices” by Mohamed Daoudi, Stefano Berretti,
Pietro Pala, Yvonne Delevoye, Alberto Del Bimbo
|“Virtual EMG via facial video analysis” by Giuseppe Boccignone, Vittorio Cuculo, Giuliano Grossi, Raffaella Lanzarotti, Raffaella Migliaccio|
|“Person Re-Identification using Partial Least Squares Appearance Modelling” by Gregory Watson, Abhir Bhalerao|
|12:20||13:20||Oral Session 2: Neural Networks|
|“Linear Regularized Compression of Deep Convolutional Neural Networks” by Claudio Ceruti, Paola Campadelli, Elena Casiraghi|
|“Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation” by Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elena Ricci, Samuel Rota Bulò|
|“Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture” by Patricia Suarez, Angel Sappa, Boris Vintimilla|
|15:00||16:30||Interactive Session 1|
|17:00||18:00||Invited Speaker: Roberto Scopigno, ISTI-CNR, Italy|
|Thursday 14 September|
|09:00||10:00||Invited Speaker: Daniel Cremers, Technische Universität München, DE|
|10:00||11:00||Oral Session 3: Action Recognition|
|“A Compact Kernel Approximation for 3D Action Recognition” by Jacopo Cavazza, Pietro Morerio, Vittorio Murino|
|“Joint orientations from skeleton data for human activity recognition” by Annalisa Franco, Antonio Magnani, Dario Maio|
|“Discriminative Dictionary Design for Action Classification in Still Images” by Abhinaba Roy, Biplab Banerjee, Vittorio Murino|
|11:30||12:30||Oral Session 4: Visual Search|
|“Learning to Weight Color And Depth for RGB-D Visual Search” by Alioscia Petrelli, Luigi Di Stefano|
|“Two-Stage Recognition for Oracle Bone Inscriptions” by Lin Meng|
|“Feature clustering with fading affect bias: building visual vocabularies on the fly” by Ziyin Wang, Gavriil Tsechpenakis|
|12:30||13:30||Oral Session 5: Special Session Imaging Solutions for Improving the Quality of Life (I-LIFE’17)|
|“Showing Different Images to Observers by using Difference in Retinal Impulse Response” by Daiki Ikeba, Fumihiko Sakaue, Jun Sato, Roberto Cipolla|
|“Interconnected Neural Networks Based on Voting Scheme and Local Detectors for Retinal Image Analysis and Diagnosis” by Dan Popescu, Traian CARAMIHALE, Loretta Ichim|
|“Measuring Refractive Properties of Human Vision by Showing 4D Light Fields” by Megumi Hori, Fumihiko Sakaue, Jun Sato, Roberto Cipolla|
|15:00||17:00||Interactive Session 2 & Coffee Break|
|17:00||18:00||Invited Speaker: Alain Tremeau, University Jean Monnet, FR|
|“Toward scene understanding: color perception versus 3D computer vision”|
|19:30||Gala Dinner at Palazzo Biscari|
|Friday 15 September|
|09:00||10:00||Invited Speaker: Fernando Peréz-Gonzalez, University of Vigo, ES|
|10:00||11:00||Oral Session 6: Forensics|
|“Identity documents classification as an image classification problem” by Ronan Sicre, Montaser Awal, Teddy Furon|
|“Using LDP-TOP in Video-Based Spoofing Detection” by Quoc-Tin Phan, Duc-Tien Dang-Nguyen, Giulia Boato, Francesco De Natale|
|“PRNU-based forgery localization in a blind scenario” by Davide Cozzolino, Francesco Marra, Giovanni Poggi, Carlo Sansone, Luisa Verdoliva|
|11:30||12:50||Oral Session 7: Automotive|
|“Learning to Map Vehicles into Bird’s Eye View” by Andrea Palazzi, Guido Borghi, Davide Abati, Simone Calderara, Rita Cucchiara|
|“Semi-Automatic Training of a Vehicle Make and Model Recognition System Abstract” by Matthijs Zwemer, Guido Brouwers, Rob Wijnhoven, Peter de With|
|“Analysis of the Discriminative Generalized Hough Transform for Pedestrian Detection” by Eric Gabriel, Hauke Schramm, Carsten Meyer|
|“Dynamic 3D Scene Reconstruction and Enhancement” by CANSEN JIANG, Yohan Fougerolle, David Fofi, Cedric Demonceaux|
|15:00||17:00||Interactive Session 3 & Coffee Break|
|17:00||18:00||ICIAP Awards & Farewell Greetings|
Interactive Session 1
|Paper Title||Author Names|
|Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images||Masaharu Sakamoto*, IBM Japan; Hiroki Nakano, IBM Japan; Kun Zhao, IBM Japan; Taro Sekiyama, IBM Japan|
|Learning from enhanced contextual similarity in brain imaging data for classification of schizophrenia||Tewodros Mulugeta Dagnew*, University of Milan; Letizia Squarcina, Scientic Institute IRCCS “E. Medea”, Bosisio Parini, Italy; Massimo Rivolta, Università degli Studi di Milano; Paolo Brambilla, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico; roberto Sassi, Università degli Studi di Milano|
|One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network||Vedran Vukotić*, INRIA / IRISA Rennes; Silvia-Laura Pintea, TU Delft; Christian Raymond, INRIA / IRISA Rennes & INSA Rennes; Guillaume Gravier, INRIA / IRISA Rennes & CNRS; Jan van Gemert, TU Delft|
|Revisiting Human Action Recognition: Personalization vs. Generalization||Andrea Zunino*, Istituto Italiano di Tecnologia; Jacopo Cavazza, Istituto Italiano di Tecnologi; Vittorio Murino,|
|Human action classification using an extended BoW formalism||Raquel Almeida, ; Benjamin Bustos, ; Zenilton Kleber Patrocinio, PUC Minas; Silvio Guimaraes*, PUC Minas|
|Graph-based Hierarchical Video Cosegmentation||Franciele Rodrigues, PUC Minas; Pedro Leal, PUC Minas; Yukiko Kenmochi, ESIEE; Jean Cousty, ESIEE; Laurent Najman, ESIEE; Silvio Guimaraes, PUC Minas; Zenilton Kleber Patrocinio*, PUC Minas|
|How Far Can You Get by Combining Change Detection Algorithms?||Simone Bianco, Italy ; Gianluigi Ciocca*, IT ; Raimondo SChettini, Università di Milano Bicocca|
|Video Saliency detection based on Boolean Map theory||Rahma Kalboussi*, ENISO Tunisia; Mehrez Abdellaoui, ENISO Tunisia; Ali Douik, ENISO Tunisia|
|Weighty LBP: a new selection strategy of LBP codes depending on their information content||Daniel Riccio*, Italy ; Maria De Marsico, Italy|
|Interest Region based Motion Magnification||Manisha Verma*, IIT Gandhinagar; Shanmuganathan Raman, IIT Gandhinagar|
|Histological image analysis by invariant descriptors||Andrea Loddo*, University of Cagliari; Cecilia Di Ruberto, University of Cagliari; Lorenzo Putzu, University of Cagliari|
|Robust Tracking of Walking Persons by Elite-type Particle Filters and RGB-D Images||Akari Oshima*, Hokkaido University; Shun’ichi Kaneko, Hokkaido univercity; Masaya Itoh, Hitachi Ltd.|
|Network Edge Entropy from Maxwell-Boltzmann Statistics||Jianjia Wang*, University of York; Richard Wilson, University of York; Edwin Hancock, University of York|
|Emotion Recognition Based on Occluded Facial Expressions||Jadisha Cornejo, University of Campinas; Helio Pedrini*, University of Campinas|
|Deep Multibranch Neural Network for Painting Categorization||Simone Bianco*, Italy ; Davide Mazzini, University of Milano-Bicocca; Raimondo SChettini, Università di Milano Bicocca|
|HoP: Histogram of Patterns for Human Action Representation||Vito Monteleone*, University of Palermo; Liliana Lo Presti, University of Palermo; Marco La Cascia, Italy|
|360° Tracking using a virtual PTZ Camera||Marco La Cascia*, Italy ; Luca Greco, Universita degli Studi di Palermo|
|Complexity and Accuracy of Hand-Crafted Detection Methods Compared to Convolutional Neural Networks||Valeria Tomaselli*, STMicroelectronics; Emanuele Plebani, STMicroelectronics; Sebastiano Mauro Strano, STMicroelectronics; Danilo Pau, STMicroelectronics|
|Organizing Videos Streams for Clustering and Estimation of Popular Scenes||Sebastiano Battiato, University of Catania; Giovanni Maria Farinella, University of Catania; Filippo Milotta*, University of Catania; Alessandro Ortis, University of Catania; Filippo Stanco, University of Catania; Valeria D’Amico, TIM – Telecom Italia – JOL WAVE; Luca Addesso, TIM – Telecom Italia – JOL WAVE; Giovanni Torrisi, TIM – Telecom Italia – JOL WAVE|
|Exploiting context information for image description||Andrea Apicella*, Federico II University; Anna Corazza, Università di Napoli Federico II; Francesco Isgro, Università di Napoli Federico II; Giuseppe Vettigli, Università di Napoli Federico II|
|Investigating the use of space-time primitives to understand human movements||Damiano Malafronte, University of Genoa; Gaurvi Goyal, University of Genoa; Alessia Vignolo, Italian Institute of Technology; Francesca Odone, Italy ; Nicoletta Noceti*, University of Genoa|
|Visual and Textual Sentiment Analysis of brand-related social media pictures using Deep Convolutional Neural Networks||Marina Paolanti*, Università Politecnica delle M; Carolin Kaiser, GfK Verein; renè Schallner, GfK Verein; Emanuele Frontoni, Università Politecnica delle Marche; Primo Zingaretti, Università Politecnica delle Marche|
|On the Importance of Domain Adaptation in Texture Classification||Barbara Caputo*, Italy ; Claudio Cusano, ; Martina Lanzi, ; Paolo Napoletano, IT; Raimondo SChettini, Università di Milano Bicocca|
|A System for Autonomous Landing of a UAV on a Moving Vehicle||Sebastiano Battiato, University of Catania; Luciano Cantelli, ; Fabio D’Urso, ; Giovanni Maria Farinella, University of Catania; Luca Guarnera, ; Dario Guastella, ; Donato Melita, ; Giovanni Muscato, ; Alessandro Ortis*, University of Catania; Francesco Ragusa, ; Corrado Santoro,|
|Benchmarking two algorithms for people detection from top-view depth cameras||Vincenzo Carletti, University of Salerno; Luca Del Pizzo, University of Salerno; Gennaro Percannella*, University of Salerno; Mario Vento, Italy|
|Gesture Modelling and Recognition by Integrating Declarative Models and Pattern Recognition Algorithms||Giorgio Fumera*, University of Cagliari; Davide Spano, University of Cagliari; Alessandro Carcangiu, University of Cagliari; Fabio Roli, University of Cagliari|
|A Rank Aggregation Framework for Video Interestingness Prediction||Jurandy Almeida*, UNIFESP; Lucas Valem, UNESP; Daniel Pedronette, UNESP|
|Indoor actions classification through long short term memory neural networks||Emanuele Cipolla, ICAR-CNR; Ignazio Infantino, ICAR-CNR; Umberto Maniscalco, ICAR-CNR; Giovanni Pilato, ICAR-CNR; Filippo Vella*, ICAR-CNR|
|Convex Polytope Ensembles for Spatio-Temporal Anomaly Detection||Francesco Turchini, Univeristy of Florence; Lorenzo Seidenari*, Media Integration and Communic; Alberto Del Bimbo, University of Firenze|
|3D object detection method using LiDAR information in multiple frames||Jung-Un Kim, Catholic University of Korea; Jihong Min, Agency for defense development; Hang-Bong Kang*, Catholic University of Korea|
|Rotation invariant co-occurrence matrix features||Lorenzo Putzu*, University of Cagliari; Cecilia Di Ruberto, University of Cagliari|
|A Machine Learning Approach for the Online Separation of Handwriting from Freehand Drawing||Danilo Avola, Sapienza University; Marco Bernardi, Sapienza University; Luigi Cinque*, Sapienza University; Gian Luca Foresti, University of Udine, Italy ; Marco Raoul Marini, Sapienza University; Cristiano Massaroni, Sapienza University|
|Bedload Solid Transport Estimation from Underwater Video Sequences||Oliver Giudice*, University of Catania; Sebastiano Battiato, University of Catania; Marco Grasso, University of Catania; Alberto Campisano, University of Catania; Gashin Shahsavari, Paris Diderot University|
|A Tensor Framework for Data Stream Clustering and Compression||Boguslaw Cyganek*, AGH University of Science|
|Generating Knowledge-Enriched Image Annotations for Fine-grained Visual Classification||Francesca Murabito*, ; Simone Palazzo, ; Concetto Spampinato, ; Daniela Giordano,|
|Lifting 2D object detections to 3D: A geometric approach in multiple views||Cosimo Rubino, Istituto Italiano di Tecnologia; Andrea Fusiello, ; Alessio Del Bue*, Italy|
Interactive Session 2
|Paper Title||Author Names|
|Feature Points Densification and Refinement||Andrey Bushnevskiy*, University of Hannover; Lorenzo Sorgi, ; Bodo Rosenhahn, Germany|
|Join cryptography and digital watermarking for 3D multiresolution meshes security||Ikbel Sayahi*, REGIM-Lab; Akram Elkefi, REGIM-Lab; Chokri Ben Amar, REGIM-Lab|
|Towards Video Captioning with Naming: a Novel Dataset and a Multi-Modal Approach||Stefano Pini, University of Modena and Reggio Emilia; Marcella Cornia, University of Modena and Reggio Emilia; Lorenzo Baraldi*, Università di Modena; Rita Cucchiara, University of Modena and Reggio Emilia|
|A Matrix Decomposition Perspective on Calibrated Photometric Stereo||Luca Magri*, University of Verona; Roberto Toldo, 3Dflow; Umberto Castellani, Italy ; Andrea Fusiello,|
|Optical Coherence Tomography Denoising by Means of a Fourier Butterworth Filter-based Approach||gabriela Samagaio, University of A Coruna; José Joaquim De Moura Ramos*, University of A Coruna; Jorge Novo, University of A Coruna; Marcos Ortega, University of A Coruna|
|Feature definition and selection for epiretinal membrane characterization in Optical Coherence Tomography images||Sergio Baamonde*, University of A Coruña; José Joaquim De Moura Ramos, University of A Coruna; Jorge Novo, University of A Coruna; José Rouco, University of A Coruña; Marcos Ortega, University of A Coruna|
|Contactless Physiological Data Analysis for User Quality of Life Improving by Using a Humanoid Social Robot||Roxana Agrigoroaie*, ENSTA-ParisTech; Adriana Tapus, Ensta-ParisTech|
|Efficient confidence measures for embedded stereo||Matteo Poggi*, University of Bologna; Fabio Tosi, University of Bologna; Stefano Mattoccia, University of Bologna|
|Adaptive Low Cost Algorithm for Video Stabilization||Giuseppe Spampinato, STMIcroelectronics; Arcangelo Bruna, STMicroelectronics; Filippo Naccari, STMicroelectronics; Valeria Tomaselli*, STMicroelectronics|
|CNN-based Identification of Hyperspectral Bacterial Signatures for Digital Microbiology||Giovanni Turra, University of Brescia — Copan Italia SpA; Simone Arrigoni, University of Brescia — Copan Italia SpA; Alberto Signoroni*, University of Brescia|
|Kinect-based gait analysis for people recognition over time||Elena Gianaria*, University of Turin; Marco Grangetto, ; Nello Balossino,|
|Exploiting Social Images to Understand Tourist Behaviour||Alessandro Torrisi*, University of Catania; Giovanni Gallo, University of Catania; Giovanni Signorello, Department of Agriculture, Food and Environment (Di3A), University of Catania; Giovanni Maria Farinella, University of Catania|
|A Hough Voting Strategy for Registering Historical Aerial Images to Present-Day Satellite Imagery||Sebastian Zambanini*, Computer Vision Lab, TU Wien; Robert Sablatnig, Computer Vision Lab, TU Wien|
|A smartphone-based system for detecting falls using anomaly detection||Vincenzo Carletti*, University of Salerno; Alessia Saggese, University of Salerno; Antonio Greco, University of Salerno; Mario Vento, Italy|
|Deep Appearance Features for Abnormal Behavior Detection in Video||Sorina Smeureanu, SecurifAI; Radu Tudor Ionescu*, University of Bucharest; Marius Popescu, University of Bucharest; Bogdan Alexe, University of Bucharest|
|Crossing the Road Without Traffic Lights: An Android-based Safety Device||Adi Perry, ; Dor Verbin, Tel Aviv University; Nahum Kiryati*, Tel Aviv University|
|Combining Color Fractal with LBP Information for Flood Segmentation in UAV-based Images||Dan Popescu*, UPB; Loretta Ichim, UPB|
|On the Estimation of Children’s Poses||Giuseppa Sciortino*, CNR National Research Council; Giovanni Maria Farinella, University of Catania; Sebastiano Battiato, University of Catania; Marco Leo, CNR-ISASI; Cosimo Distante, CNR-ISASI|
|Fast and Accurate Facial Landmark Localization in Depth Images for In-car Applications||Elia Frigieri, University of Modena; Guido Borghi*, ; Roberto Vezzani, Italy ; Rita Cucchiara, University of Modena and Reggio Emilia|
|Pixel classiﬁcation methods to detect skin lesions on dermoscopic medical images||Fabrizio Balducci*, University of Modena; Costantino Grana,|
|ARCA (Automatic Recognition of Color for Archaeology): a Desktop Application for Munsell Estimation||Filippo Milotta*, University of Catania; Filippo Stanco, University of Catania; Davide Tanasi, University of South Florida|
|GRAPHJ: A Forensics Tool for Handwriting Analysis||Luca Guarnera, University of Catania; Giovanni Maria Farinella, University of Catania; Antonino Furnari*, University of Catania; Angelo Salici, Raggruppamento Carabinieri Investigazioni Scientifiche RIS di Messina; Claudio Ciampini, Raggruppamento Carabinieri Investigazioni Scientifiche RIS di Messina; Vito Matranga, Raggruppamento Carabinieri Investigazioni Scientifiche RIS di Messina; Sebastiano Battiato, University of Catania|
|Wink detection on the eye image as a control tool in multimodal interaction||Piotr Kowalczyk, ; Dariusz Sawicki*, University of Technology|
|Description of Breast Morphology through Bag of Normals Representation||Dario Allegra*, University of Catania; Filippo Milotta, University of Catania; Diego Sinitò, Department of Mathematics and Computer Science, University of Catania, Italy; Filippo Stanco, University of Catania; Giovanni Gallo, University of Catania; Wafa Taher, Fellow of the International Fellowship Querci della Rovere; Giuseppe Catanuto, Azienda Ospedaliera Cannizzaro, Italy|
|Smartphone based pupillometry: an empirical evaluation of accuracy and safety||Sergio Di Martino*, University of Naples Federico ; Daniel Riccio, Italy ; Davide Maria Calandra, University of Naples; Antonio Visconti, Sober-EYE|
|A Classification Engine for Image Ballistics of Social Data||Oliver Giudice*, University of Catania; Sebastiano Battiato, University of Catania; Antonino Paratore, iCTLab SRL; Marco Moltisanti, University of CAtania|
|Fully-Automated CNN-based Computer Aided Celiac Disease Diagnosis||Michael Gadermayr*, RWTH Aachen University; Georg Wimmer, ; Andreas Uhl, ; Hubert Kogler, ; Andreas Vécsei, ; Dorit Merhof,|
|Recognizing Context for Privacy Preserving of First Person Vision Image Sequences||Sebastiano Battiato, University of Catania; Giovanni Maria Farinella*, University of Catania; Christian Napoli, University of Catania; Gabriele Nicotra, University of Catania; Salvatore Riccobene, University of Catania|
|A Novel Statistical Detector for Contourlet Domain Image Watermarking Using 2D-GARCH Model||Maryam Amirmazlaghani*, Amirkabir University of Techno|
|Bio-Inspired Feed-Forward System for Skin Lesion Analysis, Screening and Follow-up||Francesco Rundo*, ; Sabrina Conoci, STMicroelectronics; Giuseppe Banna, Cannizzaro Medical Hospital; Filippo Stanco, University of Catania; Sebastiano Battiato, University of Catania|
|Remote biometric verication for eLearning applications: where we are||Pietro Sanna*, EGnosis; Gian Luca Marcialis, University of Cagliari|
|An investigation of deep learning for lesions malignancy classification in breast DCE-MRI||Stefano Marrone, ; Gabriele Piantadosi, ; Roberta Fusco, ; Antonella Petrillo, ; Mario Sansone, ; Carlo Sansone*, University of Naples Federico|
|A Unified Color and Contrast Age-Dependent Visual Content Adaptation||M’Hand Kedjar*, Irystec; Greg Ward, ; Hyunjin Yoo, Irystec; Afsoon Soudi, Irystec; Tara Akhavan,, Irystec; Carlos Vazquez, École de technologie supérieur|
|Real Time Indoor 3D Pipeline for an Advanced Sensory Substitution Device||Anca Morar*, UPB; Florica Moldoveanu, UPB; Lucian Petrescu, UPB; Alin Moldoveanu, UPB|
|H-264/RTSP Multicast Stream Integrity||Andrea Bruno*, Università Degli Studi di Sale; Giuseppe Cattaneo, Università degli Studi di Salerno – DI; Fabio Petagna, eTuitus|
|A Framework for Activity Recognition through Deep Learning and Abnormality Detection in Daily Activities||Irina Mocanu*, University Politehnica of Buch; Bogdan Cramariuc, IT Center for Science and Technology; Oana Balan, University Politehnica of Bucharest; Alin Moldoveanu, UPB|
|3D Reconstruction from Specialized Wide Field of View Camera System using Unified Spherical Model||AHMAD ZAWAWI JAMALUDDIN*, UNIVERSITE DE BOURGOGNE; CANSEN JIANG, Univ. Bourgogne Franche-Comte,; Olivier Morel, Universite de Bourgogne; Ralph Seulin, Universite de Bourgogne; David Fofi,|
|Automated Optic Disc Segmentation using Polar Transform based Adaptive Thresholding for Glaucoma Detection||Muhammad Nauman Zahoor, Seecs, NUST; Muhammad Moazam Fraz*, seecs, NUST; Arsalan Ahmad, National University of Sciences and Technology|
|Automatic Multi-Seed Detection For MR Breast Image Segmentation||Albert Comelli, Università degli studi di Palermo; Alessandro Bruno*, Università degli Studi di Palermo; Maria Laura Di Vittorio, Università degli studi di Palermo; federica Ienzi, Università degli studi di Palermo; Roberto Lagalla, Università degli studi di Palermo; Salvatore Vitabile, Università degli studi di Palermo; Edoardo Ardizzone,|
|Efficient Image Segmentation in Graphs with Localized Curvilinear Features||Hans Ccacyahuillca Bejar, IME-USP; Fábio Cappabianco, Universidade Federal de São Paulo; Paulo Vechiatto de Miranda*, University of São Paulo|
|Synchronization in the Symmetric Inverse Semigroup||Federica Arrigoni*, University of Udine; Eleonora Maset, UniUD; Andrea Fusiello,|
Interactive Session 3
|Paper Title||Author Names|
|Enhanced Bags of Visual Words Representation Using Spatial Information||Lotfi Abdi*, ENISO; Rahma Kalboussi, ENISO Tunisia; Aref Meddeb, ENISO|
|Product Recognition in Store Shelves As a Sub-Graph Isomorphism Problem||Alessio Tonioni*, CVLab unibo; Luigi Di Stefano, University of Bologna|
|A Proposal of Objective Evaluation Measures Based on Eye-Contact and Face to Face Conversation for Videophone||KEIKO MASUDA, Tokyo University of Science; Ryuhei Hishiki, Tokyo University of Science; SEIICHIRO HANGAI*, Tokyo University of Science|
|A Fully Convolutional Network for Salient Object Detection||Simone Bianco, Italy ; Marco Buzzelli*, Università Milano-Bicocca; Raimondo SChettini, Università di Milano Bicocca|
|Segmentation of green areas using bivariate histograms based in Hue-Saturation type color spaces||LUIS MORALES-HERNANDEZ*, UAQ; Gilberto Alvarado-Robles, Universidad Autónoma de Querétaro; Ivan Terol-Villalobos, CIDETEQ; Marco Garduño-Ramon, Universidad Autónoma de Querétaro|
|Demographic Classification Using Skin RGB Albedo Image Analysis||Wei Chen, Ecole Centrale Lyon; Miguel Viana*, Ecole Centrale Lyon; Mohsen Ardabilian, Ecole Centrale Lyon; Abdelmalek Zine, Ecole Centrale Lyon|
|Deep Passenger State Monitoring using Viewpoint Warping||Ian Tu*, University of Warwick; Abhir Bhalerao, University of Warwick; nathan Griffiths, University of Warwick; Mauricio Delgado, Jaguar Land Rover; Thomas Popham, Jaguar Land Rover; Alex Mouzakitis, Jaguar Land Rover|
|Computer Aided Diagnosis of Pleural Effusion in Tuberculosis Chest Radiographs||Utkarsh Sharma, IIT Delhi; Brejesh Lall*, IIT Delhi|
|No-Reference Learning-based and Human Visual-based Image Quality Assessment Metric||Christophe Charrier*, Universite Caen Normandie; AdbelHakim Saadane, ; Christine Fernandez-Maloigne,|
|Tampering detection and localization in images from social networks: A CBIR approach||Cédric Maigrot*, IRISA; Ewa Kijak, IRISA, Université de Rennes 1; Ronan Sicre, IRISA, CNRS; Vincent Claveau, IRISA, CNRS|
|Exploiting Visual Saliency Algorithms for Object-Based Attention: a New Color and Scale-Based Approach||Edoardo Ardizzone, ; Alessandro Bruno, Università degli Studi di Palermo; Francesco Gugliuzza*, Università degli Studi di Palermo|
|Design of a Classification Strategy for Light Microscopy Images of the Human Liver||Luigi Cinque, Sapienza University; A. De Santis, University of L’Aquila; P. Di Giamberardino, University of L’Aquila; D. Iacoviello, University of L’Aquila; Giuseppe Placidi*, University of L’Aquila; Matteo Spezialetti, University of L’Aquila; Antonella Vetuschi, University of L’Aquila; Simona Pompili, University of L’Aquila; Roberta Sferra, University of L’Aquila|
|Multi-branch CNN for multi-scale age estimation||Marco Del Coco*, CNR-ISASI; Pierluigi Carcagni, CNR-ISASI; Marco Leo, CNR-ISASI; Paolo Spagnolo, CNR-ISASI; Pier Luigi Mazzeo, CNR-ISASI; Cosimo Distante, CNR-ISASI|
|Food Recognition using Fusion of Classifiers based on CNNs||Eduardo Aguilar*, UB; Marc Bolaños, UB; Petia Radeva, UB|
|Object Detection for Crime Scene Evidence Analysis using Deep Learning||SURAJIT SAIKIA*, University of Leon; Eduardo Fidalgo, ; Enrique Alegre, University of Leon; Laura Fernandez-Robles, Univeristy of Leon|
|Gender and Expression Analysis Based on Semantic Face Segmentation||Pierangelo Migliorati*, Univ. of Brescia; Khalil Khan, Univ. of Brescia; Riccardo Leonardi, Univ. of Brescia; Massimo Mauro, Yonder|
|Perceptual-based Color Quantization||Giuliana Ramella*, National Research Council; Vittoria Bruni, University of Rome La Sapienza; Domenico Vitulano, National Research Council|
|Towards automatic skin tone classification in facial images||Diana Borza*, TUCN; Sergiu Nistor, UBB Cluj Napoca; Adrian Darabant, UBB Cluj Napoca|
|Retinal Vessel Segmentation through Denoising and Mathematical Morphology||Benedetta Savelli, University of Cassino and L.M.; Agnese Marchesi, University of Cassino and L.M.; Alessandro Bria, University of Cassino and L.M.; Claudio Marrocco*, University of Cassino and L.M.; Mario Molinara, University of Cassino and L.M.; Francesco Tortorella, Italy|
|Real-Time Incremental and Geo-Referenced Mosaicking by Small-Scale UAVs||Danilo Avola, Sapienza University; Gian Luca Foresti*, University of Udine, Italy ; Niki Martinel, University of Udine; Christian Micheloni, Italy ; Daniele Pannone, ; Claudio Piciarelli, University of Udine|
|Spatial Enhancement by Dehazing for Detection of Microcalcifications||Alessandro Bria*, University of Cassino and L.M.; Claudio Marrocco, University of Cassino and L.M.; Adrian Galdran, INESC TEC; Aurélio Campilho, Faculdade de Engenharia, Universidade do Porto, Portugal; Agnese Marchesi, University of Cassino and L.M.; Jan-Jurre Mordang, DIAG, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands; Nico Karssemeijer, DIAG, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands; Mario Molinara, University of Cassino and L.M.; Francesco Tortorella, Italy|
|Historical Handwritten Text Images Word Spotting through Sliding Window HOG Features||Federico Bolelli*, University of Modena; Guido Borghi, ; Costantino Grana,|
|Towards Detecting High-Uptake Lesions from Lung CT Scans Using Deep Learning||Krzysztof Pawełczyk, ; Michal Kawulok*, Silesian University of Technol; Jakub Nalepa, ; Michael Hayball, ; Sarah McQuaid, ; Vineet Prakash, ; Balaji Ganeshan,|
|Mine detection based on adaboost and polynomial image decomposition||Redouane EL MOUBTAHIJ*, LSIS Laboratory; Djamal Merad, Lsis Laboratory; Jean-luc Damoiseaux, Lsis Laboratory; Pierre Drap, Lsis Laboratory|
|Automatic Detection of Subretinal Fluid and Cyst in Retinal Images||Melinda Katona*, University of Szeged; Attila Kovács, ; Rózsa Dégi, ; László G. Nyúl,|
|Embedded Real-time Visual Search with Visual Distance Estimation||Marco Paracchini*, Politecnico di Milano; Emanuele Plebani, STMicroelectronics; Mehdi Ben Iche, STMicroelectronics; Danilo Pau, STMicroelectronics; Marco Marcon, Politecnico di Milano|
|3D Face Recognition in Continuous Spaces||Francisco Josè Silva Mata, CENATAV, Havana, Cuba; Elaine Grenot Castellanos, CENATAV, Havana, Cuba; Alfredo Munoz Briseno, CENATAV, Havana, Cuba; Isneri Talavera Bustamante, CENATAV, Havana, Cuba; Stefano Berretti*, University of Firenze|
|Face Recognition with Single Training Sample per Subject||Taher Khadhraoui*, ENIT-Tunis; Hamid Amiri, National Engineering School of Tunis (ENIT)|
|Two More Strategies to Speed Up Connected Components Labeling Algorithms||Federico Bolelli*, University of Modena; Michele Cancilla, ; Costantino Grana,|
|A Convexity Measure for Gray-Scale Images Based on hv-Convexity||Peter Bodnar*, University of Szeged; László G. Nyúl, ; Peter Balazs, University of Szeged|
|A Computer Vision System for Monitoring Ice-Cream Freezers||Alessandro Torcinovich*, Ca’ Foscari University; Marco Fratton, Prosa S.r.l.; Marcello Pelillo, Ca’ Foscari University; Alberto Pravato, Prosa S.r.l.; Alessandro Roncato, Prosa S.r.l.|
|A Computer Vision System for the Automatic Inventory of a Cooler||Marco Fiorucci*, Ca’ Foscari University; Marco Fratton, Prosa S.r.l.; Tinsae Dulecha, Ca’ Foscari; Marcello Pelillo, Ca’ Foscari University; Alberto Pravato, Prosa S.r.l.; Alessandro Roncato, Prosa S.r.l.|
|Improving face recognition in low quality video sequences: single frame vs multi-frame super-resolution||Andrea Apicella*, Federico II University; Francesco Isgro, Università di Napoli Federico II; Daniel Riccio, Italy|
|Performance Evaluation of Multiscale Covariance Descriptor in Underwater Object Detection||Farah Rekik*, National Engineering School of; walid ayedi, National Engineering School of Sfax; Mohamed Jallouli, National Engineering School of Sfax|
|A lightweight Mamdani Fuzzy Controller for noise removal on iris images||Andrea Abate, University Of Salerno; Silvio Barra, BIPLab; Gianni Fenu, University of Cagliari; Michele Nappi, University Of Salerno; Fabio Narducci*, University Of Salerno|
|Incremental Support Vector Machine on Fingerprint Presentation Attack Detection updating.||Pierluigi Tuveri*, DIEE; Gian Luca Marcialis, University of Cagliari; Mikel Zurutuza,|
|Exploiting spatial context in nonlinear mapping of hyperspectral image data||Evgeny Myasnikov*, Samara University|
|Bubble Shape Identification and Calculation in Gas-Liquid Slug Flow Using Semi-Automatic Image Segmentation||Mauren Andrade*, UTFPR; Lucia Valeria Arruda, Federal University of Technology of Parana – Brazil; Eduardo Dos Santos, Universidade Tecnologica Federal do Parana – UTPFR; Daniel Pipa, Universidade Tecnologica Federal do Parana – UTPFR|
|MR Brain Tissue Segmentation based on Clustering Techniques and Neural Network||Hayat Al-Dmour*, University of Technology Sydney; Ahmed Al-Ani, University of Technology Sydney|
|Title||Quantitative imaging in monitoring response to treatment: Challenges and opportunities|
|Speaker||Habib Zaidi, Ph.D|
|Date – Location||Sept. 11 – Room 13|
|Abstract||This talk reflects the tremendous increase in interest in molecular and dual-modality imaging (PET/CT, SPECT/CT and PET/MRI) as both clinical and research imaging modalities in the past decade. An overview of molecular mutli-modality medical imaging instrumentation as well as simulation, reconstruction, quantification and related image processing issues with special emphasis on quantitative analysis of nuclear medical images are presented. This tutorial aims to bring the biomedical image processing community a review on the state-of-the-art algorithms used and under development for accurate quantitative analysis in multimodality and multiparametric molecular imaging and their validation mainly from the developer’s perspective with emphasis on image reconstruction and analysis techniques. It will inform the audience about a series of advanced development recently carried out at the PET instrumentation & Neuroimaging Lab of Geneva University Hospital and other active research groups. Current and prospective future applications of quantitative molecular imaging are also addressed especially its use prior to therapy for dose distribution modelling and optimisation of treatment volumes in external radiation therapy and patient-specific 3D dosimetry in targeted therapy towards the concept of image-guided radiation therapy.|
|Title||Virtual Cell Imaging (methods and principles)|
|Date – Location||Sept. 12 – Room 13|
|Abstract||The interdisciplinary research connecting the pure image processing and pure biology/medicine brings many challenging tasks. The tasks are highly practically oriented and their solution have a direct impact on the development of some disease treatments or drugs development, for example. This talk aims at those students/researchers who plan joining some application-oriented research groups, where the segmentation or tracking methods for the proper analysis of fixed of living cells are developed or utilized. The attendees of this tutorial will be not only able to know and use the commonly available simulation toolkits or the benchmark image data produced by these toolkit to verify the accuracy of the inspected image analysis method. They will also understand the principles of these simulation frameworks and will be able to design and implement their own toolkits hand-tailored to their private data.|
|Title||Image Tag Assignment, Refinement and Retrieval|
|Speaker||Xirong Li, Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees Snoek, Alberto Del Bimbo|
|Date – Location||Sept. 12 – Room 8|
|Abstract||In this half-day tutorial we focus on challenges in content-based image retrieval in the context of social image platforms and automatic image annotation, with a unified review on three closely linked problems in the field, i.e., image tag assignment, tag refinement, and tag-based image retrieval. Existing works in tag assignment, refinement, and retrieval vary in terms of their targeted tasks and methodology, making it non-trivial to interpret them within a unified framework. We reckon that all works rely on the key functionality of tag relevance, i.e., estimating the relevance of a specific tag with respect to the visual content of a given image. Given such a tag relevance function, one can perform tag assignment and refinement by sorting tags in light of the function, and retrieve images by sorting them accordingly. Consequently, we present a taxonomy, which structures the rich literature along two dimensions, namely media and learning. The media dimension characterizes what essential information the tag relevance function exploits, while the learning dimension depicts how such information is exploited. With this taxonomy, we discuss connections and difference between the many methods, their advantages as well as limitations.
A selected set of eleven representative and highly cited works have been implemented and evaluated on the test bed for tag assignment, refinement, and/or retrieval. To facilitate comparisons between the state-of-the-art, we present an open-source test bed comprising source code of these eleven methods and an experimental setup based on four social images datasets and on ImageNet; the testbed can be further expanded and using the proposed experimental setup it becomes possible to easily evaluate new methods. Moreover, we provide a brief live demo session with the methods, software and datasets. For repeatable experiments all data (e.g. features) and code are available online.
|Title||Active Vision and Human Robot Collaboration|
|Speaker||Dimitri Ognibene, Fiora Pirri, Guido De Croon, Lucas Paletta, Mario Ceresa, Manuela Chessa, Fabio Solari|
|Date – Location||Sept. 11 – Room 8|
|Abstract||Unstructured social environments, e.g. building sites, release an overwhelming amount of information yet behaviorally relevant variables may be not directly accessible.
Currently proposed solutions for specific tasks, e.g. autonomous cars, usually employ over redundant, expensive and computationally demanding sensory systems which attempt to cover the wide set of sensing conditions which the system may have to deal with.
Active control of the sensors and of the perception process, Active Perception (AP), is a key solution found by nature to cope with such problems, as shown by the foveal anatomy of the eye and its high mobility and control accuracy. The design principles of systems that adaptively find and selects relevant information are important for both Robotics and Cognitive Neuroscience.
At the same time, collaborative robotics has recently progressed to human-robot interaction in real manufacturing. Measuring and modeling of human task specific gaze behaviour is mandatory for smooth human robot interaction supported.
Human-related variables that are related to human attention processes are essential for the evaluation of human-robot interaction metrics. Moreover, anticipatory control for human-in-the-loop architectures, which enable robots to proactively collaborate with humans, heavily relies on observed gaze and actions patterns of their human partners according.
The tutorial will describe several systems employing active vision to support robot behavior and their collaboration with humans.
The systems described employ different strategies:
Distinct complexities and corresponding solution are posed by different settings and tasks. The tutorial will present architectural designs and signal processing methods for active vision systems employed in:
|Title||Humans through the eyes of a robot: how human social cognition could shape computer vision|
|Speaker||Nicoletta Noceti, Alessandra Sciutti|
|Date – Location||Sept. 12 – Room 9|
|Abstract||The new frontiers of robotics research foresee future scenarios where artificial agents will be more and more participating to our daily life activities. If nowadays the presence in our house of robotic devices is limited to vacuum cleaners, pool cleaners and lawn mowers, it is plausible we will experience an extraordinary growth of robotics demand in the consumer sector. According to the EU Strategic Road Map 2014-2020, robotics applications are expected to influence not only domestic activities, but also entertainment, education, monitoring, security and assistive living. This will lead robots to frequent interactions with untrained humans in unstructured environments. The success of the integration of robots in our everyday life is then subordinated to the acceptance of these novel tools by the population. The level of comfort and safety experienced by the users during the interaction plays a fundamental role in this process. Hence, a key challenge in current robotics has become to maximize the naturalness of human-robot interaction (HRI), to foster a pleasant collaboration with potential non-expert users. One possible approach to this goal is drawing inspiration from human-human interaction. Actually, humans have the ability of reading imperceptible signals hidden in others’ movements that reveal their goals and emotional status. This mechanism supports mutual adaptation, synchronization and anticipation, which cut drastically the delays and the need of complex verbal instructions in the interaction and result in seamless and efficient collaboration. In this tutorial we will discuss some guidelines for the design and the implementation of effective and natural HRI, that stems in the principles governing human-human interaction and its development since birth. To this aim, we will discuss the strong interconnections between applied robotics and neuro and cognitive science, showing that the development of human perception may be a rich source of inspiration for the design of intelligent robots able to proficiently understand and collaborate with humans. Particular emphasis will be given to motion analysis, discussing tasks addressed in this domain, methodologies, challenges and open questions, while delineating possible research lines for future developments.
|ICIAP2017||Room 5 (36 seats)||Room 6 (40 seats)||Room 7 (40 seats)||Room 8 (69 seats)||Room 9 (71 seats)||Room 13 (36 seats)|
|Sept. 11||WS: Automatic Affect Analysis & Synthesis (F/D)||WS: Background learning for detection and tracking from RGBD Videos (H/D)||WS: Social Signal Processing and Beyond (F/D)||Tutorial: Active Vision and Human Robot Collaboration (F/D)||WS: First International Workshop on Brain-Inspired Computer Vision (F/D)||Tutorial: Quantitative imaging in monitoring response to treatment: Challenges and opportunities (H/D)|
|Sept. 12||WS: Natural human-computer interaction and ecological perception in immersive virtual and augmented reality (H/D)||WS: Third International Workshop on Multimedia Assisted Dietary Management (F/D)||WS: International Workshop on Biometrics as-a-service: cloud-based technology, systems and applications (F/D)||Tutorial: Image Tag Assignment, Refinement and Retrieval (H/D)||Tutorial: Humans through the eyes of a robot: how human social cognition could shape computer vision (H/D)||Tutorial: Virtual Cell Imaging (methods and principles) (H/D)|
|Title||First International Workshop on Brain-Inspired Computer Vision (WBICV2017)|
|Organizers||George Azzopardi, Laura Fernández-Robles, Antonio Rodríguez-Sánchez|
|Date – Location||Sept. 11 – Room 9|
|Description|| The visual perception of a human is a complex process performed by various elements of the visual system of the brain. This remarkable unit of the brain has been used as a source of inspiration for developing algorithms that can be used in computer vision tasks such as finding objects, analysing motion, identifying or detecting instances, reconstructing scenes or restoring images. One of the most challenging goals in computer vision is, therefore, to design and develop algorithms that can process visual information as humans do.
The main aim of WBICV2017 is to bring together researchers from the diverse fields of computer science (pattern recognition, machine learning, artificial intelligence, high performance computing and visualisation) along with the fields of visual perception and visual psychophysics who aim to model different phenomena of the visual system of the brain. We look forward to discussing the current and next generation of brain system modelling for a wide range of vision related applications. This workshop aims to comprise powerful, innovative and modern image analysis algorithms and tools inspired by the function and biology of the visual system of the brain.
The researchers will present their latest progress and discuss novel ideas in the field. Besides the technologies used, emphasis will be given to the precise problem definition, the available benchmark databases, the need of evaluation protocols and procedures in the context of brain-inspired computer vision methods and applications.
Papers are solicited in, but not limited to, the following TOPICS:
|Title||Third International Workshop on Multimedia Assisted Dietary Management (MADiMa 2017)|
|Organizers||Stavroula Mougiakakou, Giovanni Maria Farinella, Keiji Yanai|
|Date – Location||Sept. 12 – Room 6|
|Description||The prevention of onset and progression of diet-related acute and chronic diseases (e.g. diabetes, obesity, cardiovascular diseases and cancer) requires reliable and intuitive dietary management. The need for accurate, automatic, real-time and personalized dietary advice has been recently complemented by the advances in computer vision and smartphone technologies, permitting the development of the first mobile food multimedia content analysis applications. The proposed solutions rely on the analysis of multimedia content captured by wearable sensors, smartphone cameras, barcode scanners, RFID readers and IR sensors, along with already established nutritional databases and often require some user input. In the field of nutritional management, multimedia not only bridges diverse information and communication technologies, but also computer science with medicine, nutrition and dietetics. This confluence brings new challenges and opportunities on dietary management.
MADiMa2017 aims to bring together researchers from the diverse fields of engineering, computer science and nutrition who investigate the use of information and communication technologies for better monitoring and management of food intake. The combined use of multimedia, machine learning algorithms, ubiquitous computing and mobile technologies permit the development of applications and systems able to monitor the dietary behavior, analyze food intake, identify eating patterns and provide feedback to the user towards healthier nutrition. The researchers will present their latest progress and discuss novel ideas in the field. Besides the technologies used, emphasis will be given to the precise problem definition, the available nutritional databases, the need for benchmarking multimedia databases of packed and unpacked food and the evaluation protocols.
Topics of interest include (but are not limited to) the following:
|Title||Social Signal Processing and Beyond (SSPandBE 2017)|
|Organizers||Mariella Dimiccoli, Petia Ivanova Radeva, Marco Cristani|
|Date – Location||Sept. 11 – Room 7|
|Description||The workshop provides a forum for presenting novel ideas and discussing future directions in the emerging areas of social signal processing in uncontrolled and virtual scenarios. It especially focuses on the interplay between computer vision, pattern recognition, social and psychological sciences. We strongly encourage papers covering topics coming from both the realms of social sciences and computer vision, proposing an original approach that takes from both the worlds. Furthermore, we invite contributions on the more ambitious topics of everyday interactions from wearable cameras, groups and crowd, social interactions in a “virtual” setting, unconventional social signals such as illumination and type of architecture.
Finally, the workshop will also feature an interactive session to explore existing and emerging research problems in the areas of interest for the workshop.
The relevant topics of interest for SSPANDBE include but are not limited to:
The major criteria for the selection of papers will be their potential to generate discussion and influence future research directions. Papers have to present original research contributions not concurrently submitted elsewhere. Any paper published by the ACM, IEEE, etc. which can be properly cited constitutes research which must be considered in judging the novelty of a SSPandBE submission, whether the published paper was in a conference, journal, or workshop. Therefore, any paper previously published as part of a SSPandBE workshop must be referenced and suitably extended with new content to qualify as a new submission to the Research Track at the SSPandBE conference.
Paper submission is single blind and will be handled via EasyChair
For any question about the call for papers please contact email@example.com
|Title||Natural human-computer interaction and ecological perception in immersive virtual and augmented reality (NIVAR2017)|
|Organizers||Manuela Chessa, Fabio Solari, Jean-Pierre Bresciani|
|Date – Location||Sept. 12 – Room 5|
|Description||Given the recent spread of technologies, devices, systems and models for immersive virtual reality (VR) and augmented reality (AR), which are now effectively employed in various field of applications, an emerging issue is addressing how interaction occurs in such systems. In particular, a key problem is the one of achieving a natural and ecological interaction with the devices typically used for immersive VR and for AR, i.e. interacting with them by using the same strategies and eliciting the same perceptual responses as it occurs when interacting in the real world. This is particularly important when VR and AR systems are used in assistive contexts, e.g. targeting elderly or disable people, or for cognitive and physical rehabilitation, but also to prevent and mitigate visual fatigue and cybersickness when targeting healthy people.
The main scope of this workshop is to put together researchers and practitioners from both Academy and Industry, interested in studying and developing innovative solutions with the aim of achieving a Natural human-computer interaction and an ecological perception in VR and AR systems.Technical topics of interest include (but are not limited to):
|Title||Automatic affect analysis and synthesis|
|Organizers||Nadia Berthouze, Simone Bianco, Giuseppe Boccignone, Paolo Napoletano|
|Date – Location||Sept. 11 – Room 5|
|Description||Affective computing is a research field that tries to endow machines with capabilities to recognize, interpret and express emotions. On the one hand, the ability to automatically deal with human emotions is crucial in many human computer interaction applications. On the other hand, people express affects through a complex series of actions relating to facial expression, body movements, gestures, voice prosody accompanied by a variety of physiological signals, such as heart rate and sweat, etc.
Thus, goals set by affective computing involve a number of challenging issues on how systems should be conceived built, validated, and compared.
In this perspective, we are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:
Selected papers of the workshop will be invited to be extended for a special issue on a leading international journal.
|Title||International Workshop on Biometrics as-a-service: cloud-based technology, systems and applications.|
|Organizers||Silvio Barra, Arcangelo Castiglione, Kim-Kwang Raymond Choo, Fabio Narducci|
|Date – Location||Sept. 12 – Room 7|
|Description||Cloud-based Biometrics is a relatively new topic and solutions by emerging companies, e.g., BioID, ImageWare Systems, Animetrics and IriTech, further confirm the expectations of its rapid growing. Biometrics-as-a-service has the same benefits as any other cloud-based service. It is cost-effective, scalable, reliable and hardware agnostic, making enhanced security accessible anytime and anywhere. Moreover, legal and privacy issues vary from country to country, thus limiting the progress of this branch of the research on cloud computing. We therefore expect the contributions could also shed light on such less explored aspects.
Nowadays, the massive spread of cloud-based systems is leading the service providers to offer more advanced access protocols to their own users, which may overcome the limitations and the weaknesses of the traditional alphanumeric passwords. Experts all over the world are pushing for cloud-based biometric systems, which are supposed to be one of the upcoming research frontier of the next years. Biometric credentials are difficult to be stolen and do not need to be remembered, so making them suitable for on-the-move authentication scenarios, typical of the current mobile age. On the other hand, the remote storage of a biometric trait on the cloud is function creep-prone, i.e. the gradual widening of the use of a technology or system beyond the purpose for which it was originally intended. Legal and security issues related to the abuse & misuse of a biometric trait obstruct the rapid and widespread diffusion of such practice.
The objective of IW-BAAS is to capture the latest advances in this research field, soliciting papers and ideas above the cloud based biometric systems and services. Technical, legal, professional and ethical aspects related to the use of biometrics in cloud environments are also encouraged.
Topics of interest include, but are not limited to, the following:
Special Issues on IEEE Cloud Computing will be devoted to the conference topics and the best selected papers will be considered for publication, as extended versions.
Please note that:
|Title||Background learning for detection and tracking from RGBD Videos|
|Organizers||Massimo Camplani, Lucia Maddalena, Luis Salgado|
|Date – Location||Sept. 11 – Room 6|
|Description||The advent of low cost RGB-D sensors such as Microsoft’s Kinect or Asus’s Xtion Pro is completely changing the computer vision world, as they are being successfully used in several applications and research areas. Many of these applications, such as gaming or human computer interaction systems, rely on the efficiency of learning a scene background model for detecting and tracking moving objects, to be further processed and analyzed. Depth data is particularly attractive and suitable for applications based on moving objects detection, since they are not affected by several problems typical of color based imagery. However, depth data suffer from other type of problems, such as depth-camouflage or depth sensor noisy measurements, which bound the efficiency of depth-only based background modeling approaches. The complementary nature of color and depth synchronized information acquired with RGB-D sensors poses new challenges and design opportunities. New strategies are required that explore the effectiveness of the combination of depth and color based features, or their joint incorporation into well known moving object detection and tracking frameworks.
The aim of the Workshop is to bring together researchers interested in background learning for detection and tracking from RGBD videos, in order to disseminate their most recent research results, advocate and promote the research in this area, discuss rigorously and systematically potential solutions and challenges, promote new collaborations among researchers working in different application areas, share innovative ideas and solutions for exploiting the potential synergies emerging from the integration of different application domains.
The workshop comes with the companion SBM-RGBD Challenge specifically devoted to scene background modeling from RGBD videos, aiming at advancing the development of related algorithms and methods through objective evaluation on a common dataset and common metrics.
|Title||Imaging Solutions for Improving the Quality of Life (I-LIFE’17)|
|Organizers||Dan Popescu, Loretta Ichim|
|Description||The session aims to underline the connection between complex image processing and the increasing the quality of life. This is an important challenge of the modern life, which needs interdisciplinary knowledge and effectively solves many problems encountered from different domains: computer science, medicine, biology, psychology, social policy, agriculture, food and nutrition, etc. This special session at the 19th International Conference on Image Analysis and Processing (ICIAP2017) provides a forum for researchers and practitioners to present and discuss advances in the research, development and applications of intelligent systems for complex image processing and interpretation for the increasing quality of life of the persons with disabilities, assisted persons or by detecting and diagnosing the possible diseases of normal persons.
The use of innovative techniques and algorithms in applications like image processing and interpretation for human behavior analysis and medical diagnosis leads to the increasing of life expectancy, wellbeing, independency of people with disabilities and to the improvement of ambient/ active assisted living (AAL) services. For example: the image interpretation for earlier detection of the chronic depression can help to prevent severe diseases; the patient-centric radiation oncology imaging provides a more efficient and personalized cancer care; new methods for the visually impaired (transform visual information into alternative sensory information, or maximizing the residual vision through magnification); eye vasculature and diseases analysis based on image processing software; medical robots controlled by images and so on. Others factors that influences the quality of life refer to food analysis and pollution preventing. So, computer vision exceeds the human ability in: real time inspection of food quality (outside visible spectrum and long term continuous operation); food sorting and defect detection based on color, texture, size and shape; chemical analysis through hyperspectral or multispectral imaging; image processing in agriculture (robotics, chemical analysis, detecting pests, etc.). Also, the quality of life can be determined by: air pollution detection (dust particles detection from ground and remote images, air density pollutants); waste detection and management based on interpretation of aerial images. In the case of disasters like flood, earthquake, fire, radiation, the image interpretation from different sources (ground, air and space) can be successfully used for improving and saving the life (prevention, monitoring and rescue).
The included topics are the following (but not limited): Criteria for efficient feature selection depending on application; Image processing from multi-sources based on neural networks; Medical diagnosis based on complex image processing; New approaches for gesture recognition and interpretation; Assistive technologies based on image processing; Understanding of indoor complexity for persons with disabilities; Ambient monitoring based on image processing; Image processing for quality inspection in food industry; Image processing for the precision and eco agriculture; Image processing for flooding prevention and evaluation.