Speaker recognition is the task of identifying or verifying a person’s identity using their voice. This problem involves challenges like variations in speech due to emotional states, health conditions, heterogeneity of microphone models, different environments and background noise. Accurate speaker recognition is critical for security, personalization, and forensic applications. Applying a CNN with Monte Carlo dropout can enhance Speaker Recognition by enabling robust uncertainty-aware predictions, making the presented architecture particularly effective for smaller, noisy datasets without the need for large-scale pre-training. This approach helps mitigate overfitting and improves generalization, making it effective in handling diverse speech patterns. The designed deep learning model showcases superior performance in multiple dimensions, achieving a peak validation accuracy of 93.27% for speaker recognition on a specific dataset recorded in the wild by phone, and 0.030 of EER, showing competitive performance with respect to state-of-the-art baselines.

Abstract Cloud detection is fundamental for accurate weather monitoring, often achieved through remote sensing technology, such as satellite imagery or radar. This study explores the use of lidar ceilometer backscatter data, a rich but noisy source of atmospheric information, to enhance cloud detection. Leveraging data acquired from a Lufft CHM 15k ceilometer over three months near Mount Etna, Italy, we gathered a novel dataset comprising time-height plots derived from backscatter profiles. The Weather Research and Forecasting (WRF) model was used for ground-truth data labeling, ensuring reliable model validation. We benchmarked state-of-the-art deep learning architectures, including CNN-based models (e.g., ResNet50, VGG16, InceptionV3, EfficientNet) and the Vision Transformer (ViT), on our collected dataset. Among these, ResNet50 achieved the highest accuracy ( $$89.57\%$$ 89.57 % ), closely followed by ViT ( $$89.36\%$$ 89.36 % ), showcasing the efficacy of residual learning and transformer-based approaches in extracting complex patterns from atmospheric data. Our results highlight the potential of lidar-based systems for accurate cloud detection, complementing other remote sensing technologies. Our work contributes to the field by introducing a publicly available dataset and providing comprehensive benchmarking results that establish a baseline for future research. This study also opens avenues for broader applications of ceilometer data, such as the detection of pollutants and other atmospheric phenomena. Our dataset is publicly available at https://zenodo.org/records/10616434 .

Accurate weather monitoring depends significantly on cloud detection, a crucial process achievable through remote sensing tools such as satellite imagery and radar or through the analysis of data obtained from ceilometers. A ceilometer is a lidar-based device allowing to analyse the atmosphere and detect the presence of particles within clouds. The data retrieved from ceilometers involve analysis of the backscatter of the lidar signal returning to the surface. Given the inherent noise in this data, we leverage deep learning models to detect the presence of clouds in the data. To label the data, we take advantage of a Weather Research & Forecasting (WRF) model, which provided us with ground-truth used for validation purposes. We performed a comparative analysis with current state-of-the-art deep learning architectures on this specialist domain. This comparative analysis shows that the best model is ResNet 50, but also a transformer-based model, such as ViT, achieves great results. These preliminary results pave the scenario for future works aimed at detecting other particles composing the atmosphere, such as polluting agents that can be detected from the ceilometer backscatter data.

Accurate segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) scans is crucial for clinical diagnosis and effective treatment planning. In this work, we investigate the effectiveness of Diffusion Models (DM) in achieving pixel-wise segmentation of MS lesions. DM significantly improves segmentation sensitivity, especially in regions with subtle abnormalities. We conducted extensive experiments using the magnetic resonance volumes from a public dataset, encompassing various imaging modalities. Our analysis demonstrated how DM can achieve performance levels that are on par with state-of-the-art techniques, as evidenced by a mean Dice coefficient comparable to the best existing methods. Furthermore, some variants of standard DM exhibits robustness across various imaging modalities, showcasing its versatility in clinical settings.

This paper presents semantic food segmentation to detect individual food items in an image. The presented approach has been developed in the context of the FoodRec project, which aims to study and develop an automatic framework to track and monitor the dietary habits of people, during their smoke quitting protocol. The goal of food segmentation is to train a model that can look at the images of food items and infer semantic information to recognize individual food items present in an image. In this contribution, we propose a novel Convolutional Deconvolutional Pyramid Network for food segmentation to understand the semantic information of an image at a pixel level. This network employs convolution and deconvolution layers to build a feature pyramid and achieves high-level semantic feature map representation. As a consequence, the novel semantic segmentation network generates a dense and precise segmentation map of the input food image. Furthermore, the proposed method demonstrated significant improvements on a well-known public benchmark dataset.

Pollen grain classification has a remarkable role in many fields from medicine to biology and agronomy. Indeed, automatic pollen grain classification is an important task for all related applications and areas. This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects. After an introduction to the problem of pollen grain classification and its motivations, the paper focuses on the employed data acquisition steps, which include aerobiological sampling, microscope image acquisition, object detection, segmentation and labelling. Furthermore, a baseline experimental assessment for the task of pollen classification on the built dataset, together with discussion on the achieved results, is presented.

Visual Sentiment Analysis aims to understand how images affect people, in terms of evoked emotions. Although this field is rather new, a broad range of techniques have been developed for various data sources and problems, resulting in a large body of research. This paper reviews pertinent publications and tries to present an exhaustive overview of the field. After a description of the task and the related applications, the subject is tackled under different main headings. The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view: emotional models, dataset definition, feature design. A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways. To this aim, this paper considers a structured formalization of the problem which is usually used for the analysis of text, and discusses it's suitability in the context of Visual Sentiment Analysis. The paper also includes a description of new challenges, the evaluation from the viewpoint of progress toward more sophisticated systems and related practical applications, as well as a summary of the insights resulting from this study.

Visual Sentiment Analysis aims to estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment. To this aim, most of the state of the art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. In this paper we extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the users. The proposed method defines a multimodal embedding space based on the contribute of both visual and textual features. The sentiment polarity is then inferred by a supervised Support Vector Machine trained on the representations of the obtained embedding space. Experiments performed on a representative dataset of 47235 labelled samples demonstrate that the exploitation of the proposed Objective Text helps to outperform state-of-the-art for sentiment polarity estimation.

Traditional methods for early detection of melanoma rely on the visual analysis of the skin lesions performed by a dermatologist. The analysis is based on the so‐called ABCDE (Asymmetry, Border irregularity, Colour variegation, Diameter, Evolution) criteria, although confirmation is obtained through biopsy performed by a pathologist. The proposed method exploits an automatic pipeline based on morphological analysis and evaluation of skin lesion dermoscopy images. Preliminary segmentation and pre‐processing of dermoscopy image by SC‐cellular neural networks is performed, in order to obtain ad‐hoc grey‐level skin lesion image that is further exploited to extract analytic innovative hand‐crafted image features for oncological risks assessment. In the end, a pre‐trained Levenberg–Marquardt neural network is used to perform ad‐hoc clustering of such features in order to achieve an efficient nevus discrimination (benign against melanoma), as well as a numerical array to be used for follow‐up rate definition and assessment. Moreover, the authors further evaluated a combination of stacked autoencoders in lieu of the Levenberg–Marquardt neural network for the clustering step.

We present The Social Picture, a framework to collect and explore huge amount of crowdsourced social images about public events, cultural heritage sites and other customized private events.The Social Picture aims to create social communities of users that contribute to the creation of image collections about common interests. The collections can be explored through a number of advanced Computer Vision and Machine Learning algorithms, able to capture the visual content of images in order to organize them in a semantic way. The interfaces of The Social Picture allow the users to create customized collections by exploiting semantic filters based on visual features, social network tags, geolocation, and other information related to the images.

The proliferation of mobile devices and the diffusion of social media have changed the communication paradigm of people that share multimedia data by allowing new interaction models (e.g., social networks). In social events (e.g., concerts), the automatic video understanding goal includes the interpretation of which visual contents are the most popular. The popularity of a visual content depends on how many people are looking at that scene, and therefore it could be obtained through the "visual consensus" among multiple video streams acquired by the different users devices. In this work we present RECfusion, a system able to automatically create a single video from multiple video sources by taking into account the popularity of the acquired scenes. The frames composing the final popular video are selected from the different video streams by considering those visual scenes which are pointed and recorded by the highest number of users' devices. Results on two benchmark datasets confirm the effectiveness of the proposed system.