Autonomous vehicle applications are gaining growing attention for their increasing reliability and capability to reduce the ecological impact of transportation. However, most prototypes fail to perceive the conditions of the road surface, which are crucial to adapt the driving controllers and avoid risks, e.g., due to hydroplaning. We propose a method to identify the major causes of grip reduction, namely friction, unevenness, and type of material, using a sequential architecture based on an LSTM module. As the multiple combination of these conditions alters the appearance of the ground surface, our model is trained to identify the most evident conditions first, then to use the predicted information to infer the less evident conditions. After the sequential prediction, a final classification stage allows for surpassing the limits of the sequential approach and recovering errors that occurred in early iterations, thus reducing their impact on subsequent ones. Experimental results show that the method outperforms previous ones on a challenging 27-class and 3-task dataset, by about 2.5 and 5.0 percentage points in terms of overall and balanced accuracy, respectively, while marginally increasing the inference time. Moreover, the conducted thorough error analysis demonstrates that our approach can implicitly learn the application constraints, like the absence of the material label if the surface is covered by snow, and provide useful information to interpret the sequence of predictions produced at each iteration of the sequential architecture.

Illegal waste dumping represents one of the most pervasive threats to environmental health and the well-being of terrestrial ecosystems. As widely documented in the literature, this phenomenon is deeply rooted in cultural factors and civic behavior, making early detection a crucial component in effective prevention strategies. Recent advances in artificial intelligence offer promising avenues for automatically identifying illicit dumping actions; however, progress in this direction is severely hindered by the scarcity of publicly available video datasets tailored to the Illegal Waste Dumping Detection (IWDD) task. To address this critical gap, we introduce Mivia-IWDD-500 , a novel, fully balanced dataset comprising 500 videos: 250 positive samples and 250 negative samples. The positive class is further evenly divided into 125 videos depicting static disposal events, intentional and spatially localized acts such as depositing garbage bags or bulky items, and 125 videos capturing dynamic disposal behaviors, which involve brief, spontaneous, and spatially dispersed actions such as discarding waste while walking or from a moving vehicle. Alongside the dataset, we present a baseline model designed to serve as a reference point for future research. The proposed baseline model achieves an F 1 -score of 0.79, demonstrating the viability of the dataset and establishing a solid foundation for subsequent advancements.

Predicting brain age from structural Magnetic Resonance Imaging (MRI) has emerged as a critical task at the intersection of medical imaging and Artificial Intelligence, with Deep learning (DL) models achieving state-of-the-art performance. However, despite their predictive power, such models remain susceptible to algorithmic bias, especially when applied to populations whose demographic characteristics differ from those seen during training. In this paper, we investigate how demographic factors influence the performance of brain age prediction models. We leverage a large, demographically diverse MRI dataset including 7480 healthy subjects (3599 female and 3881 male) spanning three major racial groups: White, Black, and Asian. To explore the effects of data composition and model architecture on generalization, we design and compare multiple training paradigms, including models trained on single group and a Multi-Input architecture that explicitly incorporates demographic metadata. Results on an external test set including 3194 subjects (2162 White, 694 Black, and 338 Asian) reveal evidence of demographic bias, with the Multi-Input model achieving the most balanced performance across groups (mean absolute error: 2.94 ± 0.07 for White, 2.91 ± 0.16 for Black, and 3.34 ± 0.17 for Asian subjects). These findings highlight the need for fairness-aware approaches, advocating for strategies that mitigate bias, and enhance generalizability. • 3D CNNs for brain age prediction in structural MRI scans. • Demographic bias assessment across White, Black, and Asian subgroups. • Comparison of four training paradigms (TPs) in fairness and generalization.

Video-based alarm notification systems play a critical role in safety-critical applications such as fire detection and pedestrian monitoring, because they allow for prompt intervention in the event of dangerous situations. However, existing approaches often struggle to balance high sensitivity and specificity with real-time performance, particularly in complex scenes. To address these limitations, we propose a novel method enabling Fast Alarm Notification with Task-Aware Spatio-Temporal Image Classification (FAN-TAST-IC). It is an innovative and efficient framework that combines a lightweight task-specific object detector with a pre-trained Vision-Language Model (VLM) encoder and a binary classifier. Unlike end-to-end multimodal systems, FAN-TAST-IC leverages the VLM solely as a frozen visual feature extractor, preserving its rich semantic knowledge while ensuring computational efficiency. The object detector performs a rough but real-time filtering of the temporal frames and spatial positions where objects can be. This filtering is then refined in two distinct ways, depending on the time constraints of the specific application, either to discard temporally incoherent detections or to confirm those with high confidence. The selection of such candidates drastically reduces the input space for the VLM and classifier to dubious detections only. Thus, the latter is trained on detector-guided positive and negative samples, enabling precise alarm validation, improving specificity, and preserving sensitivity without requiring extensive labeled datasets or fine-tuning the VLM. Experiments on fire and pedestrian detection tasks demonstrate the effectiveness of our method since FAN-TAST-IC consistently outperforms all the compared approaches, achieving superior F-scores and precision on challenging benchmarks while maintaining real-time capabilities.

Climate change and the occurrence of intense and unexpected weather events highlighted the need for real-time weather warning systems, especially in smart roads and isolated scenarios like rural areas. In this work, we propose to jointly recognize the weather and the ground surface conditions using existing video surveillance systems. Previous works separately tackled these two tasks even if they are correlated to each other. We propose a convolutional neural network with shared weights in the lower layers and two separate classification branches on top to exploit the correlation between the tasks and, at the same time, learn diverse high-level features for each task. Moreover, the network architecture implements attention mechanisms allowing the classification branches to focus on diverse image regions. The method is versatile and allows us to train the network on partially labeled data. The experimental analysis on real data demonstrate the effectiveness of the proposed method on both tasks, confirmed by the accuracy comparison with existing methods for the recognition of weather and ground surface conditions. The multi-task solution improves the inference speed (50 frames per second) and reduces the required memory (less than 1 GB) with respect to a system with two different single-task approaches; these results confirm that the proposed solution is ready for video surveillance applications to support smart cities. • A novel multi-task neural network for real-time recognition of weather and ground surface conditions is proposed. • A task-specific attention mechanism focuses the network’s receptive field on relevant image parts. • A masked asymmetric loss is proposed to deal with unbalanced and partially labeled datasets • We adaptively balance the backpropagated gradients of the two tasks.

Fire detection from video is effective for most video surveillance applications. The algorithm that processes the video acquired by cameras in real time has a twofold goal: detect as many fires as possible and keep the number of false alarms low. While existing approaches obtain the first goal, they often produce many false alarms due to their inability to account for the specific characteristics of diverse application environments. This paper introduces Fire Observation and Control Using Scenarios (FOCUS), a novel configurable fire detection method designed to bridge the gap between the literature methods and the application needs by exploiting scenario-specific knowledge. FOCUS leverages scalable and configurable modules for robust fire detection, incorporating three key steps: (1) fire detection, which identifies potential fire regions using visual cues; (2) fire candidate filtering through motion analysis, to eliminate false positives by analyzing the dynamic behavior of the identified fire candidates; (3) a vision-language model, which evaluates and confirms fire alarms by correlating visual evidence with contextual knowledge. By tailoring the configuration to the scenario and integrating the advanced filtering mechanisms according to the complexity of the environment, FOCUS improves performance in all the considered application scenarios. The analysis of the results shows that the proposed approach outperforms existing methods demonstrating higher resilience on real data, which enables its usage in real-world applications.

Abstract Among the catastrophic natural events posing hazards to human lives and infrastructures, fire is the phenomenon causing more frequent damages. Thanks to the spread of smart cameras, video fire detection is gaining more attention as a solution to monitor wide outdoor areas where no specific sensors for smoke detection are available. However, state-of-the-art fire detectors assure a satisfactory Recall but exhibit a high false-positive rate that renders the application practically unusable. In this paper, we propose FLAME, an efficient and adaptive classification framework to address fire detection from videos. The framework integrates a state-of-the-art deep neural network for frame-wise object detection, in an automatic video analysis tool. The advantages of our approach are twofold. On the one side, we exploit advances in image detector technology to ensure a high Recall. On the other side, we design a model-based motion analysis that improves the system’s Precision by filtering out fire candidates occurring in the scene’s background or whose movements differ from those of the fire. The proposed technique, able to be executed in real-time on embedded systems, has proven to surpass the methods considered for comparison on a recent literature dataset representing several scenarios. The code and the dataset used for designing the system have been made publicly available by the authors at ( https://mivia.unisa.it/large-fire-dataset-with-negative-samples-lfdn/ ).

Fire detection from images or videos has gained a growing interest in recent years due to the criticality of the application. Both reliable real-time detectors and efficient retrieval techniques, able to process large databases acquired by sensor networks, are needed. Even if the reliability of artificial vision methods improved in the last years, some issues are still open problems. In particular, literature methods often reveal a low generalization capability when employed in scenarios different from the training ones in terms of framing distance, surrounding environment, or weather conditions. This can be addressed by considering contextual information and, more specifically, using vision-language models capable of interpreting and describing the framed scene. In this work, we propose FIRE-TASTIC: Fire Recognition with Task-Aware Spatio-Temporal Image Captioning, a novel framework to use object detectors in conjunction with vision-language models for fire detection and information retrieval. The localization capability of the former makes it able to detect even tiny fire traces but expose the system to false alarms. These are strongly reduced by the impressive zero-shot generalization capability of the latter, which can recognize and describe fire-like objects without prior fine-tuning. We also present a variant of the FIRE-TASTIC framework based on visual question answering instead of image captioning, which allows one to customize the retrieved information with personalized questions. To integrate the high-level information provided by both neural networks, we propose a novel method to query the vision-language models using the temporal and spatial localization information provided by the object detector. The proposal can improve the retrieval performance, as evidenced by the experiments conducted on two recent fire detection datasets, showing the effectiveness and the generalization capabilities of FIRE-TASTIC, which surpasses the state of the art. Moreover, the vision-language model, which is unsuitable for video processing due to its high computational load, is executed only on suspicious frames, allowing for real-time processing. This makes FIRE-TASTIC suitable for both real-time processing and information retrieval on large datasets.

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.

The recent development of deep learning based fire detection techniques and the availability of smart cameras able to execute these algorithms on the edge paved the way for sophisticated and efficient video-based firefighting systems. However, the limited available data to train these algorithms cast shadows on their robustness and generalization capability. In this survey, we review 153 papers published in the literature and 17 publicly available fire detection datasets with the aim of identifying application scenarios that better describe real-world fire detection challenges. In the proposed taxonomy, these are characterized by two features: i) the fire size in the framed scene that depends on several parameters, foremost the distance from the fire but also the camera optic; ii) the background activity, due to the presence of moving objects that may mislead the detector. On this basis, we analyzed the existing methods under a common scheme according to this new taxonomy and matched the solutions with the needs of specific application scenarios. Similarly, for 9 interesting video datasets acquired from cameras, we labeled 536 videos according to the proposed taxonomy and shared these annotations with the community. The aim of this fire detection review is two-fold: on one hand, we classify the existing scientific works according to the real application scenarios, determining the features that are promising in specific operative conditions; on the other hand, we provide a detailed analysis and annotation of available datasets to promote the development of more reliable validation protocols and the collection of data from missing scenarios.

Modern Fingerprint Presentation Attack Detection (FPAD) modules have been particularly successful in avoiding attacks exploiting artificial fingerprint replicas against Automated Fingerprint Identification Systems (AFISs). As for several other domains, Machine and Deep Learning strongly contributed to this success, with all recent state-of-the-art detectors leveraging learning-based approaches. An insidious flip side is represented by adversarial attacks, namely, procedures intended to mislead a target detector. Indeed, despite this type of attack has been considered unrealistic, as it presupposes access to the communication channel between the sensor and the detector, in a recent work, we have highlighted the possibility of transferring a fingerprint adversarial attack from the digital domain to the physical one. In this work, we take a step further by introducing a new procedure designed to make the physical adversarial presentation attack i) more robust to the physical crafting of the PAI by exploiting explainability techniques, ii) easier to adapt to different fingerprint scanners and adversarial algorithms, and iii) usable in a black-box scenario. To quantify the impact of these novel adversarial presentation attacks family, designed to be robust to the physical crafting process, we assess the performance of both state-of-the-art PAD modules alone and integrated AFISs. Results highlight the approach’s feasibility, opening a new series of threats in the context of fingerprint PAD.

In biomedical image processing, Deep Learning (DL) is increasingly exploited in various forms and for diverse purposes. Despite unprecedented results, the huge number of parameters to learn, which necessitates a substantial number of annotated samples, remains a significant challenge. In medical domains, obtaining high-quality labelled datasets is still a challenging task. In recent years, several works have leveraged data augmentation to face this issue, mostly thanks to the introduction of generative models able to produce artificial samples having the same characteristics as the acquired ones. However, we claim that biological principles must be considered in this process, as all medical imaging techniques exploit one or more physical laws or properties directly associated with the physiological characteristics of the tissues under analysis. A notable example is the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), in which the kinetic of the contrast agent (CA) highlights both morphological and physiological aspects. In this paper, we introduce a novel generative approach explicitly relying on Physiologically Based Pharmacokinetic (PBPK) modelling and on an Intrinsic Deforming Autoencoder (DAE) to implement a physiologically-aware data augmentation strategy. As a case of study, we consider breast DCE-MRI. In particular, we tested our proposal on two private and one public datasets with different acquisition protocols, demonstrating that the proposed method significantly improves the performance of several DL-based lesion classifiers.

Level Crossings are among the most critical railway assets, concerning both the risk of accidents and their maintainability, due to intersections with promiscuous traffic and difficulties in remotely monitoring their health status. Failures can be originated from several factors, including malfunctions in the bar mechanisms and warning devices, such as light signals and bells. This paper focuses on the intelligent detection of anomalies in warning bells through non-intrusive acoustic monitoring by: (1) introducing a new concept for autonomous monitoring of level crossings; (2) generating and sharing a specific dataset collecting relevant audio signals from publicly available audio recordings; (3) implementing and evaluating a solution combining deep learning and transfer learning for warning bell detection. The results show a high accuracy in detecting anomalies and suggest viability of the approach in real-world applications, especially where network cameras with on-board microphones are installed for multi-purpose level crossing surveillance.

In Biomedical Named Entity Recognition (BioNER), the use of current cutting-edge deep learning-based methods, such as deep bidirectional transformers (e.g. BERT, GPT-3), can be substantially hampered by the absence of publicly accessible annotated datasets. When the BioNER system is required to annotate multiple entity types, various challenges arise because the majority of current publicly available datasets contain annotations for just one entity type: for example, mentions of disease entities may not be annotated in a dataset specialized in the recognition ofdrugs, resulting in a poor ground truth when using the two datasets to train a single multi-task model. In this work, we proposeTaughtNet, a knowledge distillationbased framework allowing us to fine-tune a single multi-task student model by leveraging both the ground truth and the knowledge of single-taskteachers. Our experiments on the recognition of mentions of diseases, chemical compounds and genes show the appropriateness and relevance of our approach w.r.t. strong state-of-the-art baselines in terms of precision, recall and F1 scores. Moreover, TaughtNet allows us to train smaller and lighter student models, which may be easier to be used in real-world scenarios, where they have to be deployed on limitedmemory hardware devices and guarantee fast inferences, and shows a high potential to provide explainability. We publicly release both our code on github1 and our multi-task model on the huggingface repository.

The involvement of axillary lymph node metastasis in breast cancer is one of the most important independent prognostic factors. While the metastasis of lymph node depends on primary tumour intrinsic behaviour, morphology and angioinvasivity, the involvement of the peritumoral tissue by the neoplastic cells also provides useful information for the potential tumour aggressiveness. The lymph node status is currently evaluated by histological invasive procedures with possible complications, asking for introducing safer approaches. Among different imaging techniques, the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) highlights physiological and morphological characteristics, reflecting breast lesions behaviour and aggressiveness. In the recent years, deep learning (DL) approaches, such as Convolutional Neural Networks, gained increasing popularity for biomedical image processing. Thanks to their ability to autonomously learn from images the set of features for the specific task to solve, they allow finding non-invasive alternatives to the standard procedures used up to now. This paper aims to evaluate the applicability of DL approaches for the axillary lymph node metastasis prediction, considering primary tumour DCE-MRI sequence. Differently from other work in the literature, we include a detailed analysis of healthy tissue influence in lymph node tumour spread through the evaluation of different tumour bounding options. Promising results are reported on a dataset of 153 patients with 155 malignant lesions.

Cancer is one of the leading causes of death in the western world, with medical imaging playing a key role for early diagnosis. Focusing on breast cancer, one of the emerging imaging methodologies is Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). The flip side of using DCE-MRI is in its long acquisition times that often causes the patient to move. This results in motion artefacts, namely distortions in the acquired image that can affect DCE-MRI analysis. A possible solution consists in the use of Motion Correction Techniques (MCTs), i.e. procedures intended to re-align the post-contrast image to the corresponding pre-contrast (reference) one. This task is particularly critic in DCE-MRI, due to brightness variations introduced in post-contrast images by the contrast-agent flowing. To face this problem, in this work we introduce a new MCT for breast DCE-MRI leveraging Physiologically Based PharmacoKinetic (PBPK) modelling and Artificial Neural Networks (ANN) to determine the most suitable physiologically-compliant transformation. To this aim, we propose a Neural Registration Network relying on a very task-specific loss function explicitly designed to take into account the contrast agent flowing while enforcing a correct re-alignment. We compared the obtained results against some conventional motion correction techniques, evaluating the performance on a patient-by-patient basis. Results show that the proposed approach results to be the best performing even when compared against other techniques designed to take into account for brightness variations.

Lung ultrasound imaging is reaching growing interest from the scientific community. On one side, thanks to its harmlessness and high descriptive power, this kind of diagnostic imaging has been largely adopted in sensitive applications, like the diagnosis and follow-up of preterm newborns in neonatal intensive care units. On the other side, state-of-the-art image analysis and pattern recognition approaches have recently proven their ability to fully exploit the rich information contained in these data, making them attractive for the research community. In this work, we present a thorough analysis of recent deep learning networks and training strategies carried out on a vast and challenging multicenter dataset comprising 87 patients with different diseases and gestational ages. These approaches are employed to assess the lung respiratory status from ultrasound images and are evaluated against a reference marker. The conducted analysis sheds some light on this problem by showing the critical points that can mislead the training procedure and proposes some adaptations to the specific data and task. The achieved results sensibly outperform those obtained by a previous work, which is based on textural features, and narrow the gap with the visual score predicted by the human experts.

Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is increasingly succeeding as a complementary methodology for breast cancer, with Computer Aided Detection/Diagnosis (CAD) systems becoming essential technological tools to provide early detection and diagnosis of tumours. Several CADs make use of machine learning, resulting in a constant design of hand-crafted features aimed at better assisting the physician. In recent years, Deep learning (DL) approaches raised in popularity in many pattern recognition tasks thanks to their ability to learn compact hierarchical features that well fit the specific task to solve. If, on one and, this characteristic suggests to explore DL suitability for biomedical image processing, on the other, it is important to take into account the physiological inheritance of the images under analysis. With this goal in mind, in this work we propose "3TP U-Net", an U-Shaped Deep Convolutional Neural Network that exploits the well-known Three Time Points approach for the lesion segmentation task. Results show that our proposal is able to outperform not only the classical (non-deep) approaches but also some very recent deep proposal, achieving a median Dice Similarity Coefficient of 61.24%.

The use of biometric-based authentication systems spread over daily life consumer electronics. Over the years, researchers' interest shifted from hard (such as fingerprints, voice and keystroke dynamics) to soft biometrics (such as age, ethnicity and gender), mainly by using the latter to improve the authentication systems effectiveness. While newer approaches are constantly being proposed by domain experts, in the last years Deep Learning has raised in many computer vision tasks, also becoming the current state-of-art for several biometric approaches. However, since the automatic processing of data rich in sensitive information could expose users to privacy threats associated to their unfair use (i.e. gender or ethnicity), in the last years researchers started to focus on the development of defensive strategies in the view of a more secure and private AI. The aim of this work is to exploit Adversarial Perturbation, namely approaches able to mislead state-of-the-art CNNs by injecting a suitable small perturbation over the input image, to protect subjects against unwanted soft biometrics-based identification by automatic means. In particular, since ethnicity is one of the most critical soft biometrics, as a case of study we will focus on the generation of adversarial stickers that, once printed, can hide subjects ethnicity in a real-world scenario.

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated, in recent years, a great potential as a complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the large amount of data, DCE-MRI manual inspection is error prone and can hardly be handled without the use of a Computer Aided Diagnosis (CAD) system. In a typical CAD processing, the segmentation of the breast parenchyma is a crucial stage aimed to reduce computational effort and to increase reliability. In the last years, deep convolutional networks have outperformed the state-of-the-art in many visual tasks, such as image classification and object recognition. However, very few proposals based on a deep learning approach have been applied so far for segmentation tasks in the biomedical field. The aim of this work is to apply a suitably modified convolutional neural network for fully-automating the non-trivial breast tissues segmentation task in 3D MR data, in order to accurately segment breast parenchyma from the air and other tissues (such as chest-wall). The proposed approach has been validated over 42 DCE-MRI studies. The median segmentation accuracy and Dice similarity index were 98.93 (±0, 15) and 95.90 (±0, 74) respectively with p<;0.05, and 100% of neoplastic lesion coverage.

Dynamic contrast enhanced‐magnetic resonance imaging (DCE‐MRI) is a valid complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the amount of data, the examination is difficult without the support of a computer‐aided detection and diagnosis (CAD) system. Since magnetic resonance imaging data includes different tissues and patient movements (i.e. breathing) may introduce artefacts during acquisition, CADs need some stages aimed to identify breast parenchyma and to reduce motion artefacts. Among the major issues in developing a fully automated CAD, there are the accurate segmentation of lesions in regions of interest and their consequent staging (classification). This work introduces breast lesion automatic detection and diagnosis system (BLADeS), a comprehensive fully automated breast CAD aimed to support the radiologist during the patient diagnosis. The authors propose a hierarchical architecture that implements modules for breast segmentation, attenuation of motion artefacts, localisation of lesions and, finally, classification according to their malignancy. Performance was evaluated on 42 patients with histopathologically proven lesions, performing cross‐validation to ensure a fair comparison. Results show that BLADeS can be successfully used to perform a fully automated breast lesion diagnosis starting from T1‐weighted DCE‐MRI, without requiring any operator interaction in any of the processing stages.

Presentation attacks such as using a contact lens with a printed pattern or printouts of an iris can be utilized to bypass a biometric security system. The first international iris liveness competition was launched in 2013 in order to assess the performance of presentation attack detection (PAD) algorithms, with a second competition in 2015. This paper presents results of the third competition, LivDet-Iris 2017. Three software-based approaches to Presentation Attack Detection were submitted. Four datasets of live and spoof images were tested with an additional cross-sensor test. New datasets and novel situations of data have resulted in this competition being of a higher difficulty than previous competitions. Anonymous received the best results with a rate of rejected live samples of 3.36% and rate of accepted spoof samples of 14.71%. The results show that even with advances, printed iris attacks as well as patterned contacts lenses are still difficult for software-based systems to detect. Printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses as was also seen in previous competitions.

We devised an algorithm, inspired by contrast-set mining algorithms such as STUCCO, to automatically find statistically significant properties (correlations) in crash groups. Many earlier works focused on improving the clustering of crashes but, to the best of our knowledge, the problem of automatically describing properties of a cluster of crashes is so far unexplored. This means developers currently spend a fair amount of time analyzing the groups themselves, which in turn means that a) they are not spending their time actually developing a fix for the crash; and b) they might miss something in their exploration of the crash data (there is a large number of attributes in crash reports and it is hard and error-prone to manually analyze everything). Our algorithm helps developers and release managers understand crash reports more easily and in an automated way, helping in pinpointing the root cause of the crash. The tool implementing the algorithm has been deployed on Mozilla's crash reporting service.

We address the problem of clustering a set of images, according to their source device, in the absence of any prior information. Image similarity is computed based on noise residuals, regarded as single-image estimates of the camera's photo-response non-uniformity (PRNU) pattern. First, residuals are grouped by correlation clustering, and several alternative data partitions are computed as a function of a running decision boundary. Then, these partitions are processed jointly to extract a single, more reliable, consensus clustering and, with it, more reliable PRNU estimates. Finally, both clustering and PRNU estimates are progressively refined by merging pairs of the same-PRNU clusters, selected on the basis of a maximum-likelihood ratio statistic. Extensive experiments prove the proposed method to outperform the current state of the art both on pristine images and compressed images downloaded from social networks. A remarkable feature of the method is that it does not require the user to set any parameter, nor to provide a training set to estimate them. Moreover, through a suitable choice of basic tools, and efficient implementation, complexity remains always quite limited.

Dynamic Contrast Enhanced - Magnetic Resonance Imaging (DCE-MRI) is gaining popularity as complementary diagnostic tool for breast cancer. In a typical Computer Aided Detection (CAD) processing, the identification and segmentation of the breast parenchyma is a crucial stage aimed to reduce computational effort and increase reliability, by reducing the number of voxels to analyse and removing foreign tissues and air. The aim of this work is to propose a fully-automated geometrical-based breast-mask extraction method in DCE-MRI, that combines three 2D Fuzzy C-Means clustering and geometrical breast anatomy characterization. In particular, seven well defined key-points have been considered in order to accurately segment breast parenchyma from air and chest-wall. The proposed approach has been validated on 30 DCE-MRI studies. The median segmentation accuracy and Dice similarity index were 97.86 (±0.49) and 92.66 (±1.48) respectively with p <; 0.05, and 100% of neoplastic lesion coverage.

Biometric authentication systems are quite vulnerable to sophisticated spoofing attacks. To keep a good level of security, reliable spoofing detection tools are necessary, preferably implemented as software modules. The research in this field is very active, with local descriptors, based on the analysis of microtextural features, gaining more and more popularity, because of their excellent performance and flexibility. This paper aims at assessing the potential of these descriptors for the liveness detection task in authentication systems based on various biometric traits: fingerprint, iris, and face. Besides compact descriptors based on the independent quantization of features, already considered for some liveness detection tasks, we will study promising descriptors based on the joint quantization of rich local features. The experimental analysis, conducted on publicly available data sets and in fully reproducible modality, confirms the potential of these tools for biometric applications, and points out possible lines of development toward further improvements.

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated in the last decades a great potential in screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. The aim of this work is to propose a novel model-based measure for quality evaluation of image registration techniques in DCE-MRI. The proposed measure is based on a compartmental model of blood plasma and of the extra vascular extra cellular space (EES) for tumour tissue. Its suitability for evaluating image registration techniques has been indirectly verified by considering the results obtained, after each image registration technique, by a CAD segmentation system, developed in our previous work. In particular, it has been shown that the ranking obtained by means of the proposed quality assessment measure is in agreement with the ranking of the CAD segmentation systems, proving that our measure can correctly evaluate image registration techniques in the DCE-MRI context.

The need of achieving high throughput and, at the same time, high levels of classii¬cation accuracy is nowadays a common problem within multiple research i¬elds, most of them are well known among the Big Data issues. On the other hand, in the Pattern Recognition community it is well known that the usage of more than one classii¬er can improve the classii¬cation performance. This notwithstanding, the feasibility and the advantages of implement this strategy in hardware have not been deeply considered yet. In this paper we will make an experimental evaluation about the trade-off between accuracy and throughput by using an FPGA implementation of the a Decision Tree (DT) classii¬er and of a Bagging multiple classii¬er system that uses DTs as base classii¬ers. Preliminart results are presented with reference to a set of artii¬cially generated data.

PRNU-based techniques guarantee a good forgery detection performance irrespective of the specific type of forgery. The presence or absence of the camera PRNU pattern is detected by a correlation test. Given the very low power of the PRNU signal, however, the correlation must be averaged over a pretty large window, reducing the algorithm's ability to reveal small forgeries. To improve resolution, we estimate correlation with a spatially adaptive filtering technique, with weights computed over a suitable pilot image. Implementation efficiency is achieved by resorting to the recently proposed guided filters. Experiments prove that the proposed filtering strategy allows for a much better detection performance in the case of small forgeries.

Digital camera identification is a very active research area, with important applications in the forensics field. Several approaches have been proposed in recent years for this task. One of the most promising is based on the estimation of the sensor noise pattern, used as a sort of camera fingerprint. However, a clever attacker can estimate a camera fingerprint and use it maliciously: this calls for new countermeasures, and so on, in a typical two-party game. In this paper we consider the triangle test, a well-know countermeasure against fake fingerprint attacks, and propose a new algorithm for improving the attacker's success rate. Numerical experiments show that, in typical scenarios, the proposed algorithm improves significantly the attacker performance.

Graphics editing programs of the last generation provide ever more powerful tools, which allow for the retouching of digital images leaving little or no traces of tampering. The reliable detection of image forgeries requires, therefore, a battery of complementary tools that exploit different image properties. Techniques based on the photo-response non-uniformity (PRNU) noise are among the most valuable such tools, since they do not detect the inserted object but rather the absence of the camera PRNU, a sort of camera fingerprint, dealing successfully with forgeries that elude most other detection strategies. In this paper, we propose a new approach to detect image forgeries using sensor pattern noise. Casting the problem in terms of Bayesian estimation, we use a suitable Markov random field prior to model the strong spatial dependences of the source, and take decisions jointly on the whole image rather than individually for each pixel. Modern convex optimization techniques are then adopted to achieve a globally optimal solution and the PRNU estimation is improved by resorting to nonlocal denoising. Large-scale experiments on simulated and real forgeries show that the proposed technique largely improves upon the current state of the art, and that it can be applied with success to a wide range of practical situations.

Detection of image forgeries is an important topic for forensics applications. One of the most interesting approaches to forgery detection relies on the photo-response non uniformity noise (PRNU), that can be considered as a sort of camera fingerprint and used as such to accomplish forgery detection. In fact, while genuine parts of an image exhibit the camera PRNU, this is not present in tampered areas. In this work, we present a new method to detect forgeries by using PRNU. In particular, we propose a minimum-risk Bayesian classification, aimed at minimizing the probability of error or, more in general, a weighted average of the two types of errors (false alarm, missing detection) according to their importance for the application. Then, we introduce a regularization term in the decision process to take into account prior information on the classification map. This step weights optimally the observed data and the regularity constraints to minimize the Bayesian risk. Since the regularization term is based on spatial properties of the decision map, classification cannot work on each pixel individually but must be carried out jointly on the whole image. The ensuing problem is NP-hard but we use relaxation and convex optimization techniques, based on proximal methods, to obtain a global optimum solution in limited time. Preliminary experiments with digital forgeries of different sizes and shapes prove that the improved technique provides a significant performance gain w.r.t. the original, at the cost of a limited increase in complexity.

The aim of this study is to propose an approach, based on Multi Layer Perceptron classification of dynamic and textural features, for breast lesions segmentation and classification using Dynamic Contrast Enhanced-Magnetic Resonance Imaging data. We compared the performance obtainable with dynamic, textural and spatio-temporal features. In particular, 98 dynamic features, 60 textural features and 72 spatio-temporal features were considered. The dataset included 20 breast lesions, 10 benign and 10 malignant. The performance of lesion segmentation have been evaluated with respect to manual segmentation provided by an expert radiologist. Results of lesion classification were compared to histological findings. Our results indicate that Multi Layer Perceptron can achieve better results in terms of sensitivity, specificity and accuracy when dynamic features are considered both for lesion segmentation and classification (accuracy of 91 % and 70 %, respectively).

Beyond Quality of Service and billing, one of the most important applications of traffic identification is in the field of network security. Despite their simplicity, current approaches based on port numbers are highly unreliable. This paper proposes an identification approach, based on a cascade of decision trees. The approach uses the sign pattern and payload size of the first four packets in each flow, thus remaining applicable to encrypted traffic too. The effectiveness of the proposed approach is evaluated on five real traffic traces collected in different time periods and over four different networks. The obtained overall accuracy gives us grounds to consider the adoption of this approach as stand-alone in on-line platforms for network traffic identification or in combination with classical firewall architectures.

In this paper we propose to apply an algorithm for finding out and cleaning mislabeled training sample in an adversarial learning context, in which a malicious user tries to camouflage training patterns in order to limit the classification system performance. In particular, we describe how this algorithm can be effectively applied to the problem of identifying HTTP traffic flowing through port TCP 80, where mislabeled samples can be forced by using port-spoofing attacks.

The presence of spam can seriously compromise normal user activities, forcing them to navigate through mailboxes to find the - relatively few - interesting emails. Even if a quite huge variety of spam filters has been developed until now, this problem is far to be resolved since spammers continuously modify their malicious techniques in order to bypass filters. In this paper we present a system for overcoming some of the problems that still remain with spam filters when checking emails that can contain attached images. A comparison with other state-of-the-art spam filters on a database containing both image and textual spam is also reported.

In some Computer Vision applications there is the need for grouping, in one or more clusters, only a part of the whole dataset. This happens, for example, when samples of interest for the application at hand are present together with several noisy samples. In this paper we present a graph-based algorithm for cluster detection that is particularly suited for detecting clusters of any size and shape, without the need of specifying either the actual number of clusters or the other parameters. The algorithm has been tested on data coming from two different computer vision applications. A comparison with other four state-of-the-art graph-based algorithms was also provided, demonstrating the effectiveness of the proposed approach.

In this paper we propose a method based on a graph-theoretical cluster analysis for automatically finding cluster of micro-calcifications in mammographic images. It is applied to the image after a micro-calcification detection phase and is able to cope with the unavoidable false positives that each automatic detection algorithm produces. The proposed approach has been tested on a standard database of 40 mammographic images and revealed to be very effective even when the detection phase gives rise to several false positives.

In this contribution, we show how a hierarchical edge image analysis at macroblock, block and subblock levels allows identifying preminent directional image components. The analysis, performed in the Radon domain, selects the scale level at which the image features present a directional structure that can be better reconstructed by a directional spatial interpolation. When the directional information is known at the decoder side it can drive the spatial error concealment. The experimental results, referring to the case of H.264 coded video, show the significant improvement of the decoded video visual quality achievable by the described technique.

In this paper an observer-based fault diagnosis (FD) approach for autonomous underwater vehicles (AUVs), subject to actuator faults (i.e., faults affecting the propulsion system and/or the control surfaces), is proposed. A diagnostic observer is developed based on the available dynamic model of the AUV. Compensation of unknown dynamics, uncertainties and disturbances is achieved through the adoption of a class of neural interpolators (support vector machines, SVMs) trained off line. On the other hand, interpolation of unknown actuator faults is performed by adopting a radial basis function (RBF) network, whose weights are adaptively tuned on line. The effectiveness of the approach is tested in a simulation case study developed for the NPS AUV II (PHOENIX) vehicle.

A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching algorithms proposed from the late seventies, and describes the different classes of algorithms. The second taxonomy considers the types of common applications of graph-based techniques in the Pattern Recognition and Machine Vision field.

Shot Change Detection (SCD) in MPEG coded videos is a complex and still open research problem whose interest is growing up more and more due to the diffusion of Video Databases and Digital Libraries. Techniques providing fully satisfactory performances on complex video domains are not yet available even if a number of proposals exist; such proposals show very often to be complementary in their results. In this context, the Authors investigated the use of Multi-Expert Systems (MES) for approaching the SCD problem. In the present paper, we propose and discuss a strategy to select the SCD techniques to be combined and a method for choosing an effective combining rule. In order to assess the performance of the proposed MES, we set up a database that is significantly wider than the ones commonly used in the field. Experimental results demonstrate that the proposed system performs better than each of the single SCD technique considered.

We present an algorithm for graph isomorphism and subgraph isomorphism suited for dealing with large graphs. A first version of the algorithm has been presented in a previous paper, where we examined its performance for the isomorphism of small and medium size graphs. The algorithm is improved here to reduce its spatial complexity and to achieve a better performance on large graphs; its features are analyzed in detail with special reference to time and memory requirements. The results of a testing performed on a publicly available database of synthetically generated graphs and on graphs relative to a real application dealing with technical drawings are presented, confirming the effectiveness of the approach, especially when working with large graphs.

In this paper we will try to characterize the role that graphs are conquering within the pattern recognition field. To this aim, a taxonomy built considering the most common applications of graph based techniques in the pattern recognition and image processing field is presented and discussed.

In this paper we propose a reject rule applicable to a Multi-Expert System (MES). The rule is adaptive to the given domain and allows the achievement of the best trade-off between reject and error rates as a function of the costs attributed to errors and rejects in the considered application. The results of the method are particularly effective since the method does not rely on particular statistical assumptions, as other reject rules. An experimental analysis carried out on publicly available databases is reported together with a comparison with other methods present in the literature.

The performance of an attributed relational graph (ARG) matching algorithm, tailored for dealing with large graphs, is evaluated in the context of a real application. The detection of component parts in CAD images of mechanical drawings. The matching problem is a graph-subgraph isomorphism and the algorithm exploits semantic information about nodes while does not require information about the topology of the graphs to be matched. Experimental results, compared with those obtained with a different method, show the overall efficiency of the algorithm and the matching time reduction obtainable by exploiting the semantic information held by ARGs.

A method defining a reject option that is applicable to a given 0-reject classifier is proposed. The reject option is based on an estimate of the classification reliability, measured by a reliability evaluator /spl Psi/. Trivially, once a reject threshold /spl sigma/ has been fixed, a sample is rejected if the corresponding value of /spl Psi/ is below /spl sigma/. Obviously, as /spl sigma/ represents the least tolerable classification reliability level, when its value varies the reject option becomes more or less severe. In order to adapt the behavior of the reject option to the requirements of the considered application domain, a function P characterizing the reject option's adequacy to the domain has been introduced. It is shown that P can be expressed as a function of /spl sigma/ and, consequently, the optimal value for /spl sigma/ is defined as the one which maximizes the function P. The method for determining the optimal threshold value is independent of the specific 0-reject classifier, while the definition of the reliability evaluators is related to the classifier's architecture. General criteria for defining appropriate reliability evaluators within a classification paradigm are illustrated in the paper and are based on the localization, in the feature space, of the samples that could be classified with a low reliability. The definition of the reliability evaluators for three popular architectures of neural networks (backpropagation, learning vector quantization and probabilistic network) is presented. Finally, the method has been tested with reference to a complex classification problem with data generated according to a distribution-of-distributions model.

A three-stage serial multi-expert system for facing the problem of signature verification is proposed. The first two stages, respectively devoted to the recognition of random and simple forgeries and of skilled forgeries, employ suitable criteria for estimating the reliability of the performed classification. In case of uncertainty the signature is forwarded to the successive stage which takes the final decision, taking into account the decisions of the previous stages together with their reliability estimations. Criteria for selecting the features to be used at each stage and for computing classification reliability and reliability thresholds are discussed The obtained results are compared with those achieved, on the same database of 49 writers, by other existing systems.

A graph matching algorithm is illustrated and its performance compared with that of a well known algorithm performing the same task. According to the proposed algorithm the matching process is carried out by using a state space representation: a state represents a partial solution of the matching between two graphs, and a transition between states corresponds to the addition of a new pair of matched nodes. A set of feasibility rules is introduced for pruning states corresponding to partial matching solutions not satisfying the required graph morphism. Results outlining the computational cost reduction achieved by the method are given with reference to a set of randomly generated graphs.

A novel description method is presented. It is based on the combination of structural and statistical approaches, and is applied to the problem of unconstrained isolated handwritten character recognition. Characters are preliminarily decomposed in terms of structural primitives (circular arcs) and successively described in terms of statistical features (geometrical moments suitably normalized). A multilayer perceptron is adopted in the classification stage. Results of the method on the digits of the ETL Database are reported.

The paper illustrates an algorithm for the inexact matching of attributed relational graphs. A sample graph is considered matchable with one of the prototypes if, by using a defined set of syntactic and semantic transformations, it can be made isomorphic to the graph of the prototype. The applicability of a transformation is contextually defined, i.e. each transformation can be defined with reference to a prototype, and can be applied only when the sample graph is being matched with that prototype. The reduction of the computational complexity with respect to a brute-force approach is given with reference to an OCR application.