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981 results on '"Sansone, Carlo"'

404. Active Collection of Well-Being and Health Data in Mobile Devices

Author

Marques, João, Faria, Francisco, Machado, Rita, Cardoso, Heitor, Bernardino, Alexandre, Moreno, Plinio, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fred, Ana, editor, Sansone, Carlo, editor, Gusikhin, Oleg, editor, and Madani, Kurosh, editor

Published
2023
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405. Forest Aboveground Biomass Estimation Using Machine Learning Ensembles: Active Learning Strategies for Model Transfer and Field Sampling Reduction.

Author

Amitrano, Donato, Giacco, Giovanni, Marrone, Stefano, Pascarella, Antonio Elia, Rigiroli, Mattia, and Sansone, Carlo

Subjects
*BIOMASS estimation, *ACTIVE learning, *FOREST biomass, *LEARNING strategies, *MACHINE learning, *UNCERTAINTY (Information theory), *CLIMATE change
Abstract

Biomass is a crucial indicator of the carbon sequestration capacity of a vegetation ecosystem. Its dynamic is of interest because it impacts on the carbon cycle, which plays an important role in the global climate and its changes. This work presents a novel technique, able to transfer a calibrated regression model between different areas by exploiting an active learning methodology and using Shannon's entropy as a discriminator for sample selection. Model calibration is performed based on a reference area for which an extended ground truth is available and implemented via regression bootstrap. Then, re-calibration samples for model transfer are selected through active learning, allowing for choosing a limited number of points to be investigated for training data collection. Different sampling strategies and regression techniques have been tested to demonstrate that a significant reduction in the number of calibration samples does not affect the estimation performance. The proposed workflow has been tested on a dataset concerning Finnish forests. Experimental results show that the joint exploitation of regression ensembles and active learning dramatically reduces the amount of field sampling, providing aboveground biomass estimates comparable to those obtained using literature techniques, which need extended training sets to build reliable predictions. [ABSTRACT FROM AUTHOR]

Published
2023
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406. Development of Technologies for the Detection of (Cyber)Bullying Actions: The BullyBuster Project.

Author

Orrù, Giulia, Galli, Antonio, Gattulli, Vincenzo, Gravina, Michela, Micheletto, Marco, Marrone, Stefano, Nocerino, Wanda, Procaccino, Angela, Terrone, Grazia, Curtotti, Donatella, Impedovo, Donato, Marcialis, Gian Luca, and Sansone, Carlo

Subjects
*PSYCHOLOGICAL techniques, *BULLYING, *AUTOMATIC identification, *CYBERBULLYING, *ARTIFICIAL intelligence
Abstract

Bullying and cyberbullying are harmful social phenomena that involve the intentional, repeated use of power to intimidate or harm others. The ramifications of these actions are felt not just at the individual level but also pervasively throughout society, necessitating immediate attention and practical solutions. The BullyBuster project pioneers a multi-disciplinary approach, integrating artificial intelligence (AI) techniques with psychological models to comprehensively understand and combat these issues. In particular, employing AI in the project allows the automatic identification of potentially harmful content by analyzing linguistic patterns and behaviors in various data sources, including photos and videos. This timely detection enables alerts to relevant authorities or moderators, allowing for rapid interventions and potential harm mitigation. This paper, a culmination of previous research and advancements, details the potential for significantly enhancing cyberbullying detection and prevention by focusing on the system's design and the novel application of AI classifiers within an integrated framework. Our primary aim is to evaluate the feasibility and applicability of such a framework in a real-world application context. The proposed approach is shown to tackle the pervasive issue of cyberbullying effectively. [ABSTRACT FROM AUTHOR]

Published
2023
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407. An Intelligent Conversational Agent for the Legal Domain.

Author

Amato, Flora, Fonisto, Mattia, Giacalone, Marco, and Sansone, Carlo

Subjects
*INTELLIGENT agents, *INFORMATION storage & retrieval systems, *LEGAL professions, *NATURAL language processing, *LEGAL documents, *LEGAL language
Abstract

An intelligent conversational agent for the legal domain is an AI-powered system that can communicate with users in natural language and provide legal advice or assistance. In this paper, we present CREA2, an agent designed to process legal concepts and be able to guide users on legal matters. The conversational agent can help users navigate legal procedures, understand legal jargon, and provide recommendations for legal action. The agent can also give suggestions helpful in drafting legal documents, such as contracts, leases, and notices. Additionally, conversational agents can help reduce the workload of legal professionals by handling routine legal tasks. CREA2, in particular, will guide the user in resolving disputes between people residing within the European Union, proposing solutions in controversies between two or more people who are contending over assets in a divorce, an inheritance, or the division of a company. The conversational agent can later be accessed through various channels, including messaging platforms, websites, and mobile applications. This paper presents a retrieval system that evaluates the similarity between a user's query and a given question. The system uses natural language processing (NLP) algorithms to interpret user input and associate responses by addressing the problem as a semantic search similar question retrieval. Although a common approach to question and answer (Q&A) retrieval is to create labelled Q&A pairs for training, we exploit an unsupervised information retrieval system in order to evaluate the similarity degree between a given query and a set of questions contained in the knowledge base. We used the recently proposed SBERT model for the evaluation of relevance. In the paper, we illustrate the effective design principles, the implemented details and the results of the conversational system and describe the experimental campaign carried out on it. [ABSTRACT FROM AUTHOR]

Published
2023
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409. Adversarial liveness detector: Leveraging adversarial perturbations in fingerprint liveness detection.

Author

Galli, Antonio, Gravina, Michela, Marrone, Stefano, Mattiello, Domenico, and Sansone, Carlo

Subjects
*CONVOLUTIONAL neural networks, *DATA augmentation, *DETECTORS, *HOUSEHOLD electronics
Abstract

The widespread use of fingerprint authentication systems (FASs) in consumer electronics opens for the development of advanced presentation attacks, that is, procedures designed to bypass a FAS using a forged fingerprint. As a consequence, FAS are often equipped with a fingerprint presentation attack detection (FPAD) module, to recognise live fingerprints from fake replicas. In this work, a novel FPAD approach based on Convolutional Neural Networks (CNNs) and on an ad hoc adversarial data augmentation strategy designed to iteratively increase the considered detector robustness is proposed. In particular, the concept of adversarial fingerprint, that is, fake fingerprints disguised by using ad hoc fingerprint adversarial perturbation algorithms was leveraged to help the detector focus only on salient portions of the fingerprints. The procedure can be adapted to different CNNs, adversarial fingerprint algorithms and fingerprint scanners, making the proposed approach versatile and easily customisable todifferent working scenarios. To test the effectiveness of the proposed approach, the authors took part in the LivDet 2021 competition, an international challenge gathering experts to compete on fingerprint liveness detection under different scanners and fake replica generation approach, achieving first place out of 23 participants in the 'Liveness Detection in Action track'. [ABSTRACT FROM AUTHOR]

Published
2023
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410. 3T-MRI Artificial Intelligence in Patients with Invasive Breast Cancer to Predict Distant Metastasis Status: A Pilot Study.

Author

Calabrese, Alessandro, Santucci, Domiziana, Gravina, Michela, Faiella, Eliodoro, Cordelli, Ermanno, Soda, Paolo, Iannello, Giulio, Sansone, Carlo, Zobel, Bruno Beomonte, Catalano, Carlo, and de Felice, Carlo

Subjects
*DEEP learning, *PATIENT aftercare, *ARTIFICIAL intelligence, *METASTASIS, *MAGNETIC resonance imaging, *CONTRAST media, *RETROSPECTIVE studies, *RISK assessment, *TUMOR classification, *CONTENT mining, *DESCRIPTIVE statistics, *ARTIFICIAL neural networks, *RECEIVER operating characteristic curves, *SENSITIVITY & specificity (Statistics), *BREAST tumors, RISK of metastasis
Abstract

Simple Summary: Breast cancer is still the most common cancer in the female population and is the second leading cause of cancer death in women. Although only 6% of breast cancers have metastatic spread at onset, metastases remain the first cause of death. An artificial intelligence approach could be a valuable noninvasive predictor of the risk of distant metastasis. The purpose of this study is to determine the role of a Deep Learning model approach based on a convolutional neural network in predicting the risk of distant metastasis in patients with breast cancer using dynamic Contrast-Enhanced 3T-MRI images. Background: The incidence of breast cancer metastasis has decreased over the years. However, 20–30% of patients with early breast cancer still die from metastases. The purpose of this study is to evaluate the performance of a Deep Learning Convolutional Neural Networks (CNN) model to predict the risk of distant metastasis using 3T-MRI DCE sequences (Dynamic Contrast-Enhanced). Methods: A total of 157 breast cancer patients who underwent staging 3T-MRI examinations from January 2011 to July 2022 were retrospectively examined. Patient data, tumor histological and MRI characteristics, and clinical and imaging follow-up examinations of up to 7 years were collected. Of the 157 MRI examinations, 39/157 patients (40 lesions) had distant metastases, while 118/157 patients (120 lesions) were negative for distant metastases (control group). We analyzed the role of the Deep Learning technique using a single variable size bounding box (SVB) option and employed a Voxel Based (VB) NET CNN model. The CNN performance was evaluated in terms of accuracy, sensitivity, specificity, and area under the ROC curve (AUC). Results: The VB-NET model obtained a sensitivity, specificity, accuracy, and AUC of 52.50%, 80.51%, 73.42%, and 68.56%, respectively. A significant correlation was found between the risk of distant metastasis and tumor size, and the expression of PgR and HER2. Conclusions: We demonstrated a currently insufficient ability of the Deep Learning approach in predicting a distant metastasis status in patients with BC using CNNs. [ABSTRACT FROM AUTHOR]

Published
2023
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411. CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI.

Author

Santucci, Domiziana, Faiella, Eliodoro, Gravina, Michela, Cordelli, Ermanno, de Felice, Carlo, Beomonte Zobel, Bruno, Iannello, Giulio, Sansone, Carlo, and Soda, Paolo

Subjects
*BREAST cancer prognosis, *BREAST tumor diagnosis, *DEEP learning, *DIGITAL image processing, *STATISTICS, *LYMPH nodes, *MAGNETIC resonance imaging, *CONTRAST media, *BREAST, *HISTOLOGY, *SENSITIVITY & specificity (Statistics), *RECEIVER operating characteristic curves
Abstract

Simple Summary: Breast cancer represents the most frequent cancer in women in the world. The state of the axillary lymph node is considered an independent prognostic factor and is currently evaluated only with invasive methods. Deep learning approaches, especially the ones based on convolutional neural networks, offer a valid, non-invasive alternative, allowing extraction of large amounts of the quantitative data that are used to build predictive models. The aim of our work is to evaluate the influence of the peritumoral parenchyma through different bounding box techniques on the prediction of the axillary lymph node in breast cancer patients using a deep learning artificial intelligence approach. Background: The axillary lymph node status (ALNS) is one of the most important prognostic factors in breast cancer (BC) patients, and it is currently evaluated by invasive procedures. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), highlights the physiological and morphological characteristics of primary tumor tissue. Deep learning approaches (DL), such as convolutional neural networks (CNNs), are able to autonomously learn the set of features directly from images for a specific task. Materials and Methods: A total of 155 malignant BC lesions evaluated via DCE-MRI were included in the study. For each patient's clinical data, the tumor histological and MRI characteristics and axillary lymph node status (ALNS) were assessed. LNS was considered to be the final label and dichotomized (LN+ (27 patients) vs. LN− (128 patients)). Based on the concept that peritumoral tissue contains valuable information about tumor aggressiveness, in this work, we analyze the contributions of six different tumor bounding options to predict the LNS using a CNN. These bounding boxes include a single fixed-size box (SFB), a single variable-size box (SVB), a single isotropic-size box (SIB), a single lesion variable-size box (SLVB), a single lesion isotropic-size box (SLIB), and a two-dimensional slice (2DS) option. According to the characteristics of the volumes considered as inputs, three different CNNs were investigated: the SFB-NET (for the SFB), the VB-NET (for the SVB, SIB, SLVB, and SLIB), and the 2DS-NET (for the 2DS). All the experiments were run in 10-fold cross-validation. The performance of each CNN was evaluated in terms of accuracy, sensitivity, specificity, the area under the ROC curve (AUC), and Cohen's kappa coefficient (K). Results: The best accuracy and AUC are obtained by the 2DS-NET (78.63% and 77.86%, respectively). The 2DS-NET also showed the highest specificity, whilst the highest sensibility was attained by the VB-NET based on the SVB and SIB as bounding options. Conclusion: We have demonstrated that a selective inclusion of the DCE-MRI's peritumoral tissue increases accuracy in the lymph node status prediction in BC patients using CNNs as a DL approach. [ABSTRACT FROM AUTHOR]

Published
2022
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412. Local contrast phase descriptor for fingerprint liveness detection.

Author

Gragnaniello, Diego, Poggi, Giovanni, Sansone, Carlo, and Verdoliva, Luisa

Subjects
*DESCRIPTOR systems, *HUMAN fingerprints, *IMAGE analysis, *DATA mining, *HISTOGRAMS, *SUPPORT vector machines
Abstract

We propose a new local descriptor for fingerprint liveness detection. The input image is analyzed both in the spatial and in the frequency domain, in order to extract information on the local amplitude contrast, and on the local behavior of the image, synthesized by considering the phase of some selected transform coefficients. These two pieces of information are used to generate a bi-dimensional contrast-phase histogram, used as feature vector associated with the image. After an appropriate feature selection, a trained linear-kernel SVM classifier makes the final live/fake decision. Experiments on the publicly available LivDet 2011 database, comprising datasets collected from various sensors, prove the proposed method to outperform the state-of-the-art liveness detection techniques. [ABSTRACT FROM AUTHOR]

Published
2015
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413. Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach.

Author

Pontillo, Giuseppe, Penna, Simone, Cocozza, Sirio, Quarantelli, Mario, Gravina, Michela, Lanzillo, Roberta, Marrone, Stefano, Costabile, Teresa, Inglese, Matilde, Morra, Vincenzo Brescia, Riccio, Daniele, Elefante, Andrea, Petracca, Maria, Sansone, Carlo, and Brunetti, Arturo

Abstract

Objectives: To stratify patients with multiple sclerosis (pwMS) based on brain MRI-derived volumetric features using unsupervised machine learning. Methods: The 3-T brain MRIs of relapsing-remitting pwMS including 3D-T1w and FLAIR-T2w sequences were retrospectively collected, along with Expanded Disability Status Scale (EDSS) scores and long-term (10 ± 2 years) clinical outcomes (EDSS, cognition, and progressive course). From the MRIs, volumes of demyelinating lesions and 116 atlas-defined gray matter regions were automatically segmented and expressed as z-scores referenced to external populations. Following feature selection, baseline MRI-derived biomarkers entered the Subtype and Stage Inference (SuStaIn) algorithm, which estimates subgroups characterized by distinct patterns of biomarker evolution and stages within subgroups. The trained model was then applied to longitudinal MRIs. Stability of subtypes and stage change over time were assessed via Krippendorf's α and multilevel linear regression models, respectively. The prognostic relevance of SuStaIn classification was assessed with ordinal/logistic regression analyses. Results: We selected 425 pwMS (35.9 ± 9.9 years; F/M: 301/124), corresponding to 1129 MRI scans, along with healthy controls (N = 148; 35.9 ± 13.0 years; F/M: 77/71) and external pwMS (N = 80; 40.4 ± 11.9 years; F/M: 56/24) as reference populations. Based on 11 biomarkers surviving feature selection, two subtypes were identified, designated as "deep gray matter (DGM)-first" subtype (N = 238) and "cortex-first" subtype (N = 187) according to the atrophy pattern. Subtypes were consistent over time (α = 0.806), with significant annual stage increase (b = 0.20; p < 0.001). EDSS was associated with stage and DGM-first subtype (p ≤ 0.02). Baseline stage predicted long-term disability, transition to progressive course, and cognitive impairment (p ≤ 0.03), with the latter also associated with DGM-first subtype (p = 0.005). Conclusions: Unsupervised learning modelling of brain MRI-derived volumetric features provides a biologically reliable and prognostically meaningful stratification of pwMS. Key Points: • The unsupervised modelling of brain MRI-derived volumetric features can provide a single-visit stratification of multiple sclerosis patients. • The so-obtained classification tends to be consistent over time and captures disease-related brain damage progression, supporting the biological reliability of the model. • Baseline stratification predicts long-term clinical disability, cognition, and transition to secondary progressive course. [ABSTRACT FROM AUTHOR]

Published
2022
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414. Machine Learning and Clinical-Radiological Characteristics for the Classification of Prostate Cancer in PI-RADS 3 Lesions.

Author

Gravina, Michela, Spirito, Lorenzo, Celentano, Giuseppe, Capece, Marco, Creta, Massimiliano, Califano, Gianluigi, Collà Ruvolo, Claudia, Morra, Simone, Imbriaco, Massimo, Di Bello, Francesco, Sciuto, Antonio, Cuocolo, Renato, Napolitano, Luigi, La Rocca, Roberto, Mirone, Vincenzo, Sansone, Carlo, and Longo, Nicola

Subjects
*MACHINE learning, *PROSTATE cancer, *TUMOR classification, *PROSTATE cancer patients, *PROSTATE-specific antigen, *BODY mass index
Abstract

The Prostate Imaging Reporting and Data System (PI-RADS) classification is based on a scale of values from 1 to 5. The value is assigned according to the probability that a finding is a malignant tumor (prostate carcinoma) and is calculated by evaluating the signal behavior in morphological, diffusion, and post-contrastographic sequences. A PI-RADS score of 3 is recognized as the equivocal likelihood of clinically significant prostate cancer, making its diagnosis very challenging. While PI-RADS values of 4 and 5 make biopsy necessary, it is very hard to establish whether to perform a biopsy or not in patients with a PI-RADS score 3. In recent years, machine learning algorithms have been proposed for a wide range of applications in medical fields, thanks to their ability to extract hidden information and to learn from a set of data without previous specific programming. In this paper, we evaluate machine learning approaches in detecting prostate cancer in patients with PI-RADS score 3 lesions via considering clinical-radiological characteristics. A total of 109 patients were included in this study. We collected data on body mass index (BMI), location of suspicious PI-RADS 3 lesions, serum prostate-specific antigen (PSA) level, prostate volume, PSA density, and histopathology results. The implemented classifiers exploit a patient's clinical and radiological information to generate a probability of malignancy that could help the physicians in diagnostic decisions, including the need for a biopsy. [ABSTRACT FROM AUTHOR]

Published
2022
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415. Multi input–Multi output 3D CNN for dementia severity assessment with incomplete multimodal data.

Author

Gravina, Michela, García-Pedrero, Angel, Gonzalo-Martín, Consuelo, Sansone, Carlo, and Soda, Paolo

Abstract

Alzheimer's Disease is the most common cause of dementia, whose progression spans in different stages, from very mild cognitive impairment to mild and severe conditions. In clinical trials, Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are mostly used for the early diagnosis of neurodegenerative disorders since they provide volumetric and metabolic function information of the brain, respectively. In recent years, Deep Learning (DL) has been employed in medical imaging with promising results. Moreover, the use of the deep neural networks, especially Convolutional Neural Networks (CNNs), has also enabled the development of DL-based solutions in domains characterized by the need of leveraging information coming from multiple data sources, raising the Multimodal Deep Learning (MDL). In this paper, we conduct a systematic analysis of MDL approaches for dementia severity assessment exploiting MRI and PET scans. We propose a Multi Input–Multi Output 3D CNN whose training iterations change according to the characteristic of the input as it is able to handle incomplete acquisitions, in which one image modality is missed. Experiments performed on OASIS-3 dataset show the satisfactory results of the implemented network, which outperforms approaches exploiting both single image modality and different MDL fusion techniques. [Display omitted] • Evaluation of multimodal deep learning approaches for dementia severity assessment. • Training strategy to manage incomplete dataset in multimodal deep learning. • Multi input-multi output 3D CNN to process brain MRI and PET acquisitions. [ABSTRACT FROM AUTHOR]

Published
2024
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416. DAE-CNN: Exploiting and disentangling contrast agent effects for breast lesions classification in DCE-MRI.

Author

Gravina, Michela, Marrone, Stefano, Sansone, Mario, and Sansone, Carlo

Subjects
*CONTRAST effect, *CONVOLUTIONAL neural networks, *BREAST, *BODY image, *CLASSIFICATION
Abstract

• A new GAN like architecture for domain-aware classification in DCE-MRI. • Intrinsic Deforming Autoencoder (DEA) for contrast agent effects disentangling. • Nested training strategy for adapting image disentangle to the classification task. • A size and confidence based prediction combining rule for whole lesion classification. • Wide performance improvement (+8% AUC w.r.t. the runner-up) despite on small dataset. [Display omitted] Convolutional Neural Networks (CNNs) are opening for unprecedented scenarios in fields where designing effective features is tedious even for domain experts. This is the case of medical imaging, i.e. procedures acquiring images of a human body interior for clinical proposes. Despite promising, we argue that CNNs naive use may not be effective since "medical images are more than pictures". A notable example is breast Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), in which the kinetic of the injected Contrast Agent (CA) is crucial for lesion classification purposes. Therefore, in this work we introduce a new GAN like approach designed to simultaneously learn how to disentangle the CA effects from all the other image components while performing the lesion classification: the generator is an intrinsic Deforming Autoencoder (DAE), while the discriminator is a CNN. We compared the performance of the proposed approach against some literature proposals (both classical and CNN based) using patient-wise cross-validation. Finally, for the sake of completeness, we also analyzed the impact of variations in some key aspect of the proposed solution. Results not only show the effectiveness of our approach (+ 8 % AUC w.r.t. the runner-up) but also confirm that all the approach's components effectively contribute to the solution. [ABSTRACT FROM AUTHOR]

Published
2021
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417. An Efficient SIMD Implementation of the H.265 Decoder for Mobile Architecture

Author

Bariani, Massimo, Lambruschini, Paolo, Raggio, Marco, Pezzoni, Luca, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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418. Full-Reference SSIM Metric for Video Quality Assessment with Saliency-Based Features

Author

Romani, Eduardo, da Silva, Wyllian Bezerra, Fonseca, Keiko Verônica Ono, Culibrk, Dubravko, de Almeida Prado Pohl, Alexandre, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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419. Video Quality Assessment for Mobile Devices on Mobile Devices

Author

Mirkovic, Milan, Culibrk, Dubravko, Sladojevic, Srdjan, Anderla, Andras, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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420. A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

Author

Piérard, Sébastien, Van Droogenbroeck, Marc, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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421. Evaluation of Signal Processing Methods for Attention Assessment in Visual Content Interaction

Author

Elafoudi, Georgia, Stankovic, Vladimir, Stankovic, Lina, Pappusetti, Deepti, Kalva, Hari, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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422. Why You Trust in Visual Saliency

Author

Ardizzone, Edoardo, Bruno, Alessandro, Greco, Luca, La Cascia, Marco, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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423. Comparison of Matrix Completion Algorithms for Background Initialization in Videos

Author

Sobral, Andrews, Bouwmans, Thierry, Zahzah, El-hadi, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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424. Kinematics Analysis Multimedia System for Rehabilitation

Author

Ye, Minxiang, Yang, Cheng, Stankovic, Vladimir, Stankovic, Lina, Kerr, Andrew, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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425. Computer Aided Evaluation (CAE) of Morphologic Changes in Pigmented Skin Lesions

Author

Rizzi, Maria, D’Aloia, Matteo, Cice, Gianpaolo, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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426. Smart Maintenance to Support Digital Life

Author

Bergenti, Federico, Chiappone, Massimo, Gotta, Danilo, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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427. Analytical Method and Research of Uyghur Language Chunks Based on Digital Forensics

Author

Aizezi, Yasen, Jamal, Anwar, Mamat, Dilxat, Abdurexit, Ruxianguli, Ubul, Kurban, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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428. Interoperability of Biometric Systems: Analysis of Geometric Characteristics of Handwritten Signatures

Author

Pirlo, Giuseppe, Rizzi, Fabrizio, Vacca, Annalisa, Impedovo, Donato, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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429. Nonlinear Background Filter to Improve Pedestrian Detection

Author

Wang, Yi, Piérard, Sébastien, Su, Song-Zhi, Jodoin, Pierre-Marc, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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430. BMTDL for Scene Modeling on the SBI Dataset

Author

Noceti, Nicoletta, Staglianò, Alessandra, Verri, Alessandro, Odone, Francesca, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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431. Background Modeling by Weightless Neural Networks

Author

De Gregorio, Massimo, Giordano, Maurizio, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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432. Real-Time Implementation of Background Modelling Algorithms in FPGA Devices

Author

Kryjak, Tomasz, Gorgon, Marek, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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433. Multi-modal Background Model Initialization

Author

Bloisi, Domenico D., Grillo, Alfonso, Pennisi, Andrea, Iocchi, Luca, Passaretti, Claudio, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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434. Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks

Author

Christodoulidis, Stergios, Anthimopoulos, Marios, Mougiakakou, Stavroula, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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435. Simple Median-Based Method for Stationary Background Generation Using Background Subtraction Algorithms

Author

Laugraud, Benjamin, Piérard, Sébastien, Braham, Marc, Van Droogenbroeck, Marc, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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436. CNN-Based Food Image Segmentation Without Pixel-Wise Annotation

Author

Shimoda, Wataru, Yanai, Keiji, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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437. Towards Benchmarking Scene Background Initialization

Author

Maddalena, Lucia, Petrosino, Alfredo, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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438. MANGO - Mobile Augmented Reality with Functional Eating Guidance and Food Awareness

Author

Waltner, Georg, Schwarz, Michael, Ladstätter, Stefan, Weber, Anna, Luley, Patrick, Bischof, Horst, Lindschinger, Meinrad, Schmid, Irene, Paletta, Lucas, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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439. Objective and Subjective Meal Registration via a Smartphone Application

Author

Moulos, Ioannis, Maramis, Christos, Ioakimidis, Ioannis, van den Boer, Janet, Nolstam, Jenny, Mars, Monica, Bergh, Cecilia, Maglaveras, Nicos, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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440. FooDD: Food Detection Dataset for Calorie Measurement Using Food Images

Author

Pouladzadeh, Parisa, Yassine, Abdulsalam, Shirmohammadi, Shervin, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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441. Towards an Engaging Mobile Food Record for Teenagers

Author

Caon, Maurizio, Carrino, Stefano, Prinelli, Federica, Ciociola, Valentina, Adorni, Fulvio, Lafortuna, Claudio, Tabozzi, Sarah, Serrano, José, Condon, Laura, Khaled, Omar Abou, Mugellini, Elena, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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442. Fractal Nature of Chewing Sounds

Author

Papapanagiotou, Vasileios, Diou, Christos, Lingchuan, Zhou, van den Boer, Janet, Mars, Monica, Delopoulos, Anastasios, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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443. Dish Detection and Segmentation for Dietary Assessment on Smartphones

Author

Dehais, Joachim, Anthimopoulos, Marios, Mougiakakou, Stavroula, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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444. Using Small Checkerboards as Size Reference: A Model-Based Approach

Author

Hassannejad, Hamid, Matrella, Guido, Mordonini, Monica, Cagnoni, Stefano, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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445. Food Object Recognition Using a Mobile Device: State of the Art

Author

Knez, Simon, Šajn, Luka, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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446. Food Recognition Using Consensus Vocabularies

Author

Farinella, Giovanni Maria, Moltisanti, Marco, Battiato, Sebastiano, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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447. On the Exploitation of One Class Classification to Distinguish Food Vs Non-Food Images

Author

Farinella, Giovanni Maria, Allegra, Dario, Stanco, Filippo, Battiato, Sebastiano, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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448. The Use of Temporal Information in Food Image Analysis

Author

Wang, Yu, He, Ye, Zhu, Fengqing, Boushey, Carol, Delp, Edward, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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449. A Printer Indexing System for Color Calibration with Applications in Dietary Assessment

Author

Fang, Shaobo, Liu, Chang, Zhu, Fengqing, Boushey, Carol, Delp, Edward, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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450. Food Recognition and Leftover Estimation for Daily Diet Monitoring

Author

Ciocca, Gianluigi, Napoletano, Paolo, Schettini, Raimondo, 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, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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