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- From a Visual Scene to a Virtual Representation: A Cross-Domain ReviewPublication . Pereira, Américo; Carvalho, Pedro; Pereira, Nuno; Viana, Paula; Côrte-Real, LuísThe widespread use of smartphones and other low-cost equipment as recording devices, the massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities, made visual data a must in several scenarios, including surveillance, sports, retail, entertainment, and intelligent vehicles. Despite significant advances in analyzing and extracting data from images and video, there is a lack of solutions able to analyze and semantically describe the information in the visual scene so that it can be efficiently used and repurposed. Scientific contributions have focused on individual aspects or addressing specific problems and application areas, and no cross-domain solution is available to implement a complete system that enables information passing between cross-cutting algorithms. This paper analyses the problem from an end-to-end perspective, i.e., from the visual scene analysis to the representation of information in a virtual environment, including how the extracted data can be described and stored. A simple processing pipeline is introduced to set up a structure for discussing challenges and opportunities in different steps of the entire process, allowing to identify current gaps in the literature. The work reviews various technologies specifically from the perspective of their applicability to an endto- end pipeline for scene analysis and synthesis, along with an extensive analysis of datasets for relevant tasks.
- A Review of Recent Advances and Challenges in Grocery Label Detection and RecognitionPublication . Guimarães, Vânia; Nascimento, Jéssica; Viana, Paula; Carvalho, PedroWhen compared with traditional local shops where the customer has a personalised service, in large retail departments, the client has to make his purchase decisions independently, mostly supported by the information available in the package. Additionally, people are becoming more aware of the importance of the food ingredients and demanding about the type of products they buy and the information provided in the package, despite it often being hard to interpret. Big shops such as supermarkets have also introduced important challenges for the retailer due to the large number of different products in the store, heterogeneous affluence and the daily needs of item repositioning. In this scenario, the automatic detection and recognition of products on the shelves or off the shelves has gained increased interest as the application of these technologies may improve the shopping experience through self-assisted shopping apps and autonomous shopping, or even benefit stock management with real-time inventory, automatic shelf monitoring and product tracking. These solutions can also have an important impact on customers with visual impairments. Despite recent developments in computer vision, automatic grocery product recognition is still very challenging, with most works focusing on the detection or recognition of a small number of products, often under controlled conditions. This paper discusses the challenges related to this problem and presents a review of proposed methods for retail product label processing, with a special focus on assisted analysis for customer support, including for the visually impaired. Moreover, it details the public datasets used in this topic and identifies their limitations, and discusses future research directions of related fields.
- Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion DetectionPublication . Mosiichuk, Vladyslav; Sampaio, Ana; Viana, Paula; Oliveira, Tiago; Rosado, LuísLiquid-based cytology (LBC) plays a crucial role in the effective early detection of cervical cancer, contributing to substantially decreasing mortality rates. However, the visual examination of microscopic slides is a challenging, time-consuming, and ambiguous task. Shortages of specialized staff and equipment are increasing the interest in developing artificial intelligence (AI)-powered portable solutions to support screening programs. This paper presents a novel approach based on a RetinaNet model with a ResNet50 backbone to detect the nuclei of cervical lesions on mobile-acquired microscopic images of cytology samples, stratifying the lesions according to The Bethesda System (TBS) guidelines. This work was supported by a new dataset of images from LBC samples digitalized with a portable smartphone-based microscope, encompassing nucleus annotations of 31,698 normal squamous cells and 1395 lesions. Several experiments were conducted to optimize the model’s detection performance, namely hyperparameter tuning, transfer learning, detected class adjustments, and per-class score threshold optimization. The proposed nucleus-based methodology improved the best baseline reported in the literature for detecting cervical lesions on microscopic images exclusively acquired with mobile devices coupled to the μSmartScope prototype, with per-class average precision, recall, and F1 scores up to 17.6%, 22.9%, and 16.0%, respectively. Performance improvements were obtained by transferring knowledge from networks pre-trained on a smaller dataset closer to the target application domain, as well as including normal squamous nuclei as a class detected by the model. Per-class tuning of the score threshold also allowed us to obtain a model more suitable to support screening procedures, achieving F1 score improvements in most TBS classes. While further improvements are still required to use the proposed approach in a clinical context, this work reinforces the potential of using AI-powered mobile-based solutions to support cervical cancer screening. Such solutions can significantly impact screening programs worldwide, particularly in areas with limited access and restricted healthcare resources.
- Deep Learning Approach for Seamless Navigation in Multi-View Streaming ApplicationsPublication . Costa, Tiago S.; Viana, Paula; Andrade, Maria TeresaQuality of Experience (QoE) in multi-view streaming systems is known to be severely affected by the latency associated with view-switching procedures. Anticipating the navigation intentions of the viewer on the multi-view scene could provide the means to greatly reduce such latency. The research work presented in this article builds on this premise by proposing a new predictive view-selection mechanism. A VGG16-inspired Convolutional Neural Network (CNN) is used to identify the viewer’s focus of attention and determine which views would be most suited to be presented in the brief term, i.e., the near-term viewing intentions. This way, those views can be locally buffered before they are actually needed. To this aim, two datasets were used to evaluate the prediction performance and impact on latency, in particular when compared to the solution implemented in the previous version of our multi-view streaming system. Results obtained with this work translate into a generalized improvement in perceived QoE. A significant reduction in latency during view-switching procedures was effectively achieved. Moreover, results also demonstrated that the prediction of the user’s visual interest was achieved with a high level of accuracy. An experimental platform was also established on which future predictive models can be integrated and compared with previously implemented models.
- Data2MV - A user behaviour dataset for multi-view scenariosPublication . Soares da Costa, Tiago; Andrade, Maria Teresa; Viana, Paula; Silva, Nuno CastroThe Data2MV dataset contains gaze fixation data obtained through experimental procedures from a total of 45 partic- ipants using an Intel RealSense F200 camera module and seven different video playlists. Each of the playlists had an approximate duration of 20 minutes and was viewed at least 17 times, with raw tracking data being recorded with a 0.05 second interval. The Data2MV dataset encompasses a total of 1.0 0 0.845 gaze fixations, gathered across a total of 128 exper- iments. It is also composed of 68.393 image frames, extracted from each of the 6 videos selected for these experiments, and an equal quantity of saliency maps, generated from aggregate fixation data. Software tools to obtain saliency maps and generate complementary plots are also provided as an open- source software package. The Data2MV dataset was publicly released to the research community on Mendeley Data and constitutes an important contribution to reduce the current scarcity of such data, particularly in immersive, multi-view streaming scenarios.
- A Machine Learning App for Monitoring Physical Therapy at HomePublication . Pereira, Bruno; Cunha, Bruno; Viana, Paula; Lopes, Maria; Melo, Ana S. C.; Sousa, Andreia S. P.Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research.
- Multidimensional scaling and visualization of patterns in global large-scale accidentsPublication . Lopes, António M.; Tenreiro Machado, J. A.Catastrophic events have been commonly referred to as phase transitions in complex systems (CS). This paper proposes an approach based on unsupervised machine learning to identify phases and phase transitions in the dynamics of CS. The testbed is a dataset of causalities and events associated with global large-scale accidents. Multidimensional time-series are generated from the raw data and are interpreted as the output of a CS. The time-series are normalized and segmented in the time-domain, and the resulting objects are used to characterize the behavior of the dynamical process. The objects are compared through a number of distances and the information by the multidimensional scaling (MDS) technique, respectively. The time is displayed as a parametric variable. The generated portraits have a complex nature, with periods of chaotic-like behavior, and are analyzed in terms of the emerging patterns. The results show that the adoption of MDS is a relevant modeling tool using present day computational resources.
- LMI-based stability analysis of fractional order systems of neutral type with time varying delays under actuator saturationPublication . Aghayan, Zahra Sadat; Alfi, Alireza; Tenreiro Machado, J. A.This article addresses the stability of uncertain fractional order systems of neutral type under actuator saturation. Some criteria regarding the asymptotic robust stability of such type of systems are constructed with the help of the Lyapunov–Krasovskii functional. Moreover, a state-feedback control law is formulated by means of linear matrix inequalities. In order to analyze the domain of attraction, an algorithm for determining the controller gain is provided via the cone complementarity linearization method. The main results are illustrated via numerical examples.
- A piecewise spectral-collocation method for solving fractional Riccati differential equation in large domainsPublication . Azin, H.; Mohammadi, F.; Tenreiro Machado, J. A.This paper addresses the approximate solution of the fractional Riccati differential equation (FRDE) in large domains. First, the solution interval is divided into a finite number of subintervals. Then, the Legendre–Gauss–Radau points along with the Lagrange interpolation method are employed to approximate the FRDE solution in each subinterval. The method has the advantage of providing the approximate solutions in large intervals. Additionally, the convergence analysis of the numerical algorithm is also provided. Three illustrative examples are given to illustrate the efficiency and applicability of the proposed method.
- Advances in fractional differential equations (IV): Time-fractional PDEsPublication . Zhou, Yong; Feckan, Michal; Liu, Fawang; Machado, J. A. TenreiroThe fractional calculus (FC) started more than three centuries ago. In the last years, FC is playing a very important role in various scientific fields. In fact, FC has been recognized as one of the best tools to describe long-memory processes. Fractional-order models are interesting not only for engineers and physicists, but also for mathematicians. Among such models those described by partial differential equations (PDEs) containing fractional derivatives are of utmost importance. Their evolution was more complex than for the classical integer-order counterpart. Nonetheless, classical PDEs’ methods are hardly applicable directly to fractional PDEs. Therefore, new theories and methods are required, with concepts and algorithms specifically developed for fractional PDEs. This is the fourth special issue on Advances in Fractional Differential Equations of the journal Computers and Mathematics with Applications. This selection of 38 papers focuses on innovative theoretical and numerical methods, and in applications of FC to important problems that encompass the most relevant areas of current research on fractional PDEs.