Development of predictive models a partir de imagen y vídeo #computerVisiona partir de señal y series temporales #timeSeriesForecastinga partir de texto #naturalLanguageProcessingbasados en la interacción entorno-agente #reinforcementLearning
CVBLab
Research areas
Inteligencia Artificial
Creation of automatic models using machine learning techniques. Manual feature extraction through descriptors and the use of classifiers to create predictive models. Approaches based on deep neural networks are also explored.
Procesado de imagen
Techniques for image enhancement, 2D-3D visualization, object segmentation, registration, texture analysis, and classification are applied to different image modalities: 2D/3D images, depth images, or hyperspectral images.
Procesado de señal
Characteristic patterns are extracted from any type of one-dimensional signal, and automatic prediction models are trained and/or comprehensive statistical analyses are performed. Additionally, physiological signals are processed to respond to human behavior.
Procesado de video
Object detection and tracking algorithms, facial recognition, and posture analysis have been applied in video surveillance, railway transportation security, behavior pattern recognition, augmented reality, and restoration of old films.
Curated
CVBLab Projects
Predictive models From image and video #computerVisionfrom signal and time series #timeSeriesForecastingfrom text #naturalLanguageProcessingBased on the interaction environment-agent #reinforcementLearning
New Artificial Intelligence Algorithm for Non-Invasive Automatic Evaluation of Blastocyst
Improving embryo evaluation is essential to increasing the success of assisted reproduction treatments, and NAIAEANIBL focuses on doing so through safe, non-invasive approaches that reduce risks and improve outcomes for patients.
Embryo evaluation is one of the most critical aspects of assisted reproduction treatments, as it largely determines the success of the process. Currently, techniques such as Preimplantation Genetic Testing make it possible to analyze the chromosomal status of embryos, but they require invasive, costly procedures with important limitations. In this context, the NAIAEANIBL project aims to move towards a new generation of diagnostic tools that are more precise, accessible, and respectful of the embryo.
The project proposes the development of an Artificial Intelligence–based system capable of evaluating blastocysts in a completely non-invasive manner. To achieve this, multiple sources of information—traditionally analyzed separately—will be integrated. On the one hand, images and videos of embryo development obtained through time-lapse technologies will be used to study both morphology and temporal evolution. On the other hand, molecular data extracted from the culture medium, such as cell-free DNA and proteomic profiles, will provide key insights into the genetic and functional state of the embryo.
Based on this combination of data, advanced deep learning models will be developed to identify complex patterns and relationships that are not detectable using conventional methods. This approach will not only enable the determination of embryo ploidy without the need for biopsy, but also improve the prediction of implantation potential and treatment success, providing specialists with an objective and highly valuable decision-support tool.
One of the most innovative aspects of the project lies in the fusion of multimodal information within a single Artificial Intelligence model, an approach that has not yet been explored in this field. This strategy opens the door to a more comprehensive and accurate evaluation of embryos, reducing the uncertainty associated with current methods and enabling improved embryo selection.
The expected impact is twofold. From a clinical perspective, the project aims to increase success rates in assisted reproduction treatments, reduce risks associated with invasive techniques, and improve patient experience. From a technological standpoint, it will contribute to advancing the state of the art in Artificial Intelligence applied to biomedicine, generating new methodologies and capabilities that can be transferred to other domains.
NAIAEANIBL is being developed through a collaboration between IVI Valencia, an international leader in assisted reproduction providing the clinical environment and medical expertise for validation, and the Universitat Politècnica de València (UPV), through the CVBLab research group, which leads the development of the Artificial Intelligence models based on its expertise in computer vision and advanced biomedical data analysis.
CHARACTERIZATION OF ANOMALIES AND EVENT DETECTION IN WATER NETWORKS USING ARTIFICIAL INTELLIGENCE AND EDGE-COMPUTING TECHNOLOGIES
ADRIATICO was created to address one of the most urgent challenges in water management: the early and accurate detection of incidents in supply and sanitation networks, within a context of increasing water stress and ageing infrastructure.
The project proposes a new monitoring paradigm based on the integration of high-precision fiber-optic sensors with advanced Artificial Intelligence models and edge-computing data processing. This combination will enable real-time identification of critical events such as leaks, infiltrations, unauthorized access, or structural damage, preventing losses, reducing maintenance costs, and improving the sustainability of the resource.
The main goal of ADRIATICO is to develop deep-learning models capable of detecting and characterizing complex anomalies from distributed optical signals, and to optimize these models so they can run directly on specialized hardware such as FPGAs. This approach will allow data to be processed where it is generated, drastically reducing the dependence on central servers and improving the system’s operational and energy efficiency. Throughout the project, the developments will first be validated in laboratory conditions and later in a relevant environment within an operational water network, ensuring their practical applicability and transferability.
As a key outcome, ADRIATICO is expected to contribute significantly to the modernization of the water cycle through technologies that support proactive decision-making and predictive maintenance. Thanks to the system’s ability to detect events accurately and early, water utilities will be able to reduce losses, optimize resources, and enhance the resilience of infrastructures that are essential for economic development and social well-being. Additionally, the project will generate important scientific and technological impact through publications, demonstrators, and new capabilities applicable to future developments.
The consortium behind ADRIATICO is composed of entities that combine industrial experience, technological capability, and research excellence: FIBSEN Monitorizaciones, S.L., which will lead the integration and validation of the solution thanks to its extensive experience in fiber-optic sensing applied to the water sector; theUniversitat Politècnica de València, through our research group CVBLab, contributing specialized knowledge in artificial intelligence and deep learning and taking responsibility for designing and training the predictive models that will detect anomalous events in water networks; and ARTIKODE Intelligence, S.L., which will contribute advanced tools for high-resolution data annotation and management, as well as the optimization of the models for execution on edge-computing hardware.
MADE-TO-MEASURE MICROMACHINING WITH LASER BEAMS TAILORED IN AMPLITUDE AND PHASE
METAMORPHA proposes to create a unique and agile ultrashort pulse laser micromachining platform that replaces many conventional manufacturing process chains. It has the potential to eliminate thousands of environmentally damaging production processes. METAMORPHA is fully electric and digital, produces no chemical waste and enables novel new process steps for product rework and repair.
Microelement production processes are currently based on a chain production with different manufacturing stages. These stages usually produce the required patterns on the part by means of physical and chemical processes, which complement each other. However, this technology produces a large amount of chemical waste. To address this limitation, the use of laser technology has been one of the solutions that has led the European industry in recent years. However, it needs to be improved in terms of agility, digitization and environmental sustainability of the same.
METAMORPHA addresses this challenge by proposing a technology that enables a green and digital approach to these challenges. To this end, it seeks to replace mechanical and chemical processes from production lines with a single, fully digital green process based on ultra-short pulse laser micromachining. Specifically, the technology developed is moving towards “right first time” processes with 30% less resource consumption compared to state-of-the-art technology.
To achieve this, METAMORPHA will be a technology based on a combination of two cascaded spatial light modulators (SLMs) with an integrated galvo scanner, enabling the most sophisticated digital beam shaping and steering ever developed. It will also feature a phase-based control that enables digital beam steering for agile multi-directional processing. In this project, CVBLAB will develop novel machine learning algorithms that automate the calibration of the laser performance parameters based on the material used, desired pattern, and defects encountered. This methodology will be based on continuous learning systems, capable of adapting the algorithms to new data collected during the use of the system. Finally, METAMORPHA will be tested in three different use cases for the production line of products in leading companies.
QUANTUM COMPUTATION AND ITS APPLICATION TO STRATEGIC INDUSTRIES
The objective of the project is to progress the state of the art of quantum algorithms and apply this knowledge to a series of proofs of concept in different strategic sectors of the Spanish economy such as: Energy, Financial, Space, Defense and Logistics.
Quantum technologies are set to play a disruptive role in the coming years due to the impact they will have in many areas, most notably in massive computing power and secure encrypted communications, fields in which, according to all forecasts, they will mark a new era.
Part of the current momentum in the areas of quantum applications in industry is due to the “Quantum Manifesto”. The document was presented in 2016 at the Quantum Europe Conference and the interest was such that the European Commission responded with a 10-year project (Flagship) and an investment of more than €1 billion. The programme started in 2018 and fosters collaboration between EU countries. The main objective of the Flagship is to train and support European industry in quantum technologies. Knowing how to define and develop technology to use quantum physics, identifying how this revolution will change the world, or in which use cases it can and cannot be applied, are some of the questions that the Flagship and European industry should be able to answer thanks to this plan.
The CUCO project (Quantum Computing and its application to strategic industries) emerges as the first major Quantum Computing project at national and enterprise level. CUCO is led by GMV, with the participation of start-ups recognised in the quantum field (Multiverse Computing, Qilimanjaro), as well as two of the major Spanish companies with newly created departments formed for the study and application of quantum computing: REPSOL and BBVA. Top-level research centres such as ICFO, CSIC, BSC, Tecnalia, UPV, and DIPC are also participating. The consortium is completed by AMATECH and DAS as leading companies in the logistics and defence/security fields. The knowledge acquired in this project will be used to study whether quantum computing improves the performance of classical computing in a series of proofs of concept in different strategic sectors of the Spanish economy such as: Energy, Finance, Space, Defence and Security: Energy, Finance, Space, Defence and Logistics.
TURBO aims to combine several new developments from different disciplines to greatly improve the sustainability of wind turbine blade (WTB)production by reducing defect formation and improving repair strategies in composites and coatings.
Wind turbines are already part of everyday European life and are an essential part of the strategy to meet the Green Deal targets. Almost 3500 turbines (>10000 blades) were installed in 2019 alone. Wind turbine blade (WTB) size is rapidly increasing with new offshore blades >100 m in length. Yet remarkably, the technologies used to manufacture blades have not changed significantly since the late 1970s. Composite blades are manufactured using resin infusion and coating processes which are prone to defects resulting in high rates of re-work, scrap and repair.
To meet the worldwide demand for sustainable green energy, the wind industry must transform itself to be more resource-efficient. To enable zero-defect and zero-waste manufacturing, the industry needs game-changing, innovative, disruptive and ambitious ideas to produce new manufacturing methods. Recent advances and developments in Industry 4.0 present an opportunity for the wind industry to transform. Enabling data-driven manufacturing decisions to reduce defect formation and improve defect assessment will drive the scrap rate down as well as optimising the manufacturing processes to decrease production waste. TURB0 will bring these developments
together in a coordinated workplan to develop new methodologies towards zero-waste WTB manufacturing. The core of turbo will be the development of machine learning-based algorithms for in-line process control during turbine blade production.
SEQUOIA will deliver the highest resolution OCT system ever built, protected from noise by artificial intelligence (AI) based OAM control in a real-world application: retinal imaging.
Optical coherence tomography (OCT) is a key imaging technology, especially for ophthalmology, allowing non-contact high resolution 3D imaging which has helped to save the sight of millions of people across the world. OCT developed rapidly since its invention in 1991 but has stalled since reaching the practical axial resolution (dz) limit of ~1 µm (>5 µm for most commercial systems). Quantum OCT (QOCT) offers a step change ×2 improvement in dz together with greatly reduced dispersion. In addition, by controlling the orbital angular momentum (OAM) it is possible to protect the system from environmental noise and deliver improved edge definition, surface profile distinction and discrimination of chiral objects. SEQUOIA will deliver the highest resolution OCT system ever built, protected from noise by artificial intelligence (AI) based OAM control in a real-world application: retinal imaging.
AI-based algorithms from CVBLab will be used at TUD to program spatial light modulators (SLMs) to encode high purity high-dimensional OAM onto the QOCT beams to increase noise resilience and improve imaging quality.
Retinal imaging, a vital real-world application (using stable test standards from WWU) will be performed, with automated AI-algorithms (CVBLab) to analyse the images and compare performance with classical OCT.
System for Marking Tumor Regions in Gigapixel Histological Images
The main objective is the appraisal of the results of the investigation of new artificial intelligence algorithms for the automatic analysis of histological images (WSI) applied to the diagnosis of different types of cancers, among them (although not limited to): prostate cancer, triple negative breast cancer (TNBC), and skin cancer.
The latest technological advances have led to a drastic change in the possibilities of health care, thus improving the conditions of medical care. But today’s pathology services still rely heavily on the presence of qualified pathologists to recognize characteristic findings in a tissue section under a microscope.
Digital pathology, and innovation in this area, solves multiple problems related to both the development of work, the quality of service, as well as the patient (diagnosis and safety).
Therefore, the main objective of this project is to create a web platform for visualization, annotation, and automatic evaluation of histological cases that supports the identification of different types of cancer. This tool will allow pathologists from all over the world to obtain online diagnostic help based on artificial intelligence techniques.
As a main novelty, said system will host predictive models generated from the most innovative techniques in the field of deep learning. Using new digitized histological samples from any hospital in the world, the predictive models hosted in the cloud will be retrained with these cases using innovative active learning techniques.
Deep learning-based methods for the semantic segmentation of tree-like vegetation: Arecaceae, Pinus, Platanus & Celtis Australis
The aim of this project is to use Deep Learning techniques to identify and predict Green Infrastructure Ecosystem Services from high resolution satellite images, focusing specifically on the Mediterranean basin and on a strategic selection of plant taxa: the family Arecaceae, the genera Pinus and Platanus, and the species Celtis australis.
The core of the project involves the identification and classification of these plant taxa using advanced image segmentation algorithms. Deep Learning techniques will be instrumental in creating mathematical models for the assessment and prediction of four Ecosystem Services. These services, aligned with the Common International Classification of Ecosystem Services (CICES), include atmospheric regulation, thermal and moisture regulation, erosion data control and the enhancement of physical and experiential interaction with the natural environment.
Funding Entity
Green Urban Data, S.L. S.L. IVACE: Valencian Institute for Business Competitiveness
Human Aircraft Roadmap for Virtual Intelligent System
To carry out a State of the Art of cognitive computing algorithms.
To identify realistic scenarios in which a digital assistant is likely to bring benefits to flight operations.
To determine the shortcomings or related risks that could prevent this technology from being applied successfully in real life.
To demonstrate the digital assistance concept in realistic scenarios and define guidance for its adoption.
Flight movements are growing significantly in Europe, with no trend reversal expected. The integration of unmanned aircraft into the air space will make traffic management even more complex.
A significant impact on pilots’ job is inevitable, with increasing information to deal with and new tasks to accomplish. Increasing automation is expected, with a view to support pilots and to prevent peak workload conditions.
Framing the human-machine interaction in terms of partnership will help building capacity in machines to better understand humans, and in people to engage collaboratively with them. In the cockpit, this partnership will lead to pilots using a set of new technologies, capable of self-learning, to anticipate needs and to adapt to pilots’ mental states. The versatility and problem-solving of humans will combine with the precision and repeatability of high-tech solutions.
In this context, the overall objective of the HARVIS Project is to identify how cognitive computing algorithms, implemented in a digital assistant, could support the decision-making of single pilots in complex situations. A future Artificial Intelligence in the cockpit concept will be demonstrated, and a roadmap providing guidance for its adoption by 2035+ will be delivered.
Nondestrucctive Inspection Services for digitally Enhanced Zero Waste Manofactturing
ZDZW aims to develop non-destructive inspection (NDI) services to improve industrial manufacturing and help reduce defects and the waste generated throughout this process. In fact, ZDZW owes its name to its commitment to promoting zero-defect (ZD) and zero-waste (ZW) solutions in the industrial field.
Most of the quality control methods currently used in the quality measurement of manufacturing processes are still destructive, thus generating considerable amounts of waste and reducing productivity. Indeed, this type of inspection service entails time-consuming rework stages and expensive defect repairs, sometimes even leading to the rejection of entire batches. To mitigate these challenges, non-destructive inspection (NDI) techniques have emerged to pave the road to reduced waste production during manufacturing and quality control. However, current NDI technologies still entail some limitations such as their high purchasing cost or their complex technical and digital integration.
To tackle these limitations while achieving zero-defect and sustainable manufacturing through the optimization of product quality control and monitoring, ZDZW proposes to provide advanced inspection technologies that would be compatible with digitally enabled manufacturing processes. Specifically, ZDZW will leverage artificial intelligence techniques to create comprehensive machine learning and digital twin-enabled solutions for enhanced manufacturing.
All in all, ZDZW aims to offer a set of digitally enabled NDI services through the creation of three inspection suites for product integrity, visual properties, and thermal processes monitoring, which will be validated in five different industrial sectors.
