Connected image processing with multivariate attributes: an unsupervised Markovian classification approach

International audienceThis article presents a new approach for constructing connected operators for image processing and analysis. It relies on a hierarchical Markovian unsupervised algorithm in order to classify the nodes of the traditional Max-Tree. This approach enables to naturally handle multivariate attributes in a robust non-local way. The technique is demonstrated on several image analysis tasks: filtering, segmentation, and source detection, on astronomical and biomedical images. The obtained results show that the method is competitive despite its general formulation. This article provides also a new insight in the field of hierarchical Markovian image processing showing that morphological trees can advantageously replace traditional quadtrees

Toward a new axiomatic for hyper-connections

International audienceWe propose an evolution of the hyper-connection axiomatic in order to improve the consistency of hyper-connected filters and to simplify their design. Our idea relies on the principle that the decomposition of an image into h-components must be necessary and sufficient. We propose a set of three equivalent axioms to achieve this goal. We show that an existing h-connection already fulfills these axioms and we propose a new h-connection based on flat functions that also fulfills these axioms. Finally we show that these new axioms bring several new interesting properties that simplify the use of h-connections and guarantee the consistency of h-connected filters as they ensure that: 1) every deletion of image components will effectively modify the filtered image 2) a deleted component can not reappear in the filtered image

Building Integrated Photovoltaics (BIPV): Review, Potentials, Barriers and Myths

To date, none of the predictions that have been made about the emerging BIPV industry have really hit the target. The anticipated boom has so far stalled and despite developing and promoting a number of excellent systems and products, many producers around the world have been forced to quit on purely economic grounds. The authors believe that after this painful cleansing of the market, a massive counter trend will follow, enlivened and carried forward by more advanced PV technologies and ever-stricter climate policies designed to achieve energy neutrality in a cost-effective way. As a result, the need for BIPV products for use in construction will undergo first a gradual and then a massive increase. The planning of buildings with multifunctional, integrated roof and façade elements capable of fulfilling the technical and legal demands will become an essential, accepted part of the architectonic mainstream and will also contribute to an aesthetic valorisation. Until then, various barriers need to be overcome in order to facilitate and accelerate BIPV. Besides issues related to mere cost-efficiency ratio, psychological and social factors also play an evident role. The goal of energy change linked to greater use of renewables can be successfully achieved only when all aspects are taken into account and when visual appeal and energy efficiency thus no longer appear to be an oxymoro

From hyperconnections to hypercomponent tree: Application to document image binarization

International audienceIn this paper, we propose an extension of the component tree based on at zones to hyperconnections (h-connections). The tree is dened by a special order on the h-connection and allows non at nodes. We apply this method to a particular fuzzy h-connection and we give an ecient algorithm to transform the component tree into the new fuzzy h-component tree. Finally, we propose a method to binarize document images based on the h-component tree and we evaluate it on the DIBCO 2009 benchmarking dataset: our novel method places rst or second according to the dierent evaluation measures. Hierarchical and tree based representations have become very topical in image processing. In particular, the component tree (or Max-Tree) has been the subject of many studies and practical works. Nevertheless, the component tree inherits the weaknesses of the at zone approach, namely its high sensitivity to noise, gradients and diculty to manage disconnected objects. Even if some solutions have been proposed to preserve the component tree [5, 4], it seems that a more general framework for grayscale component tree [1] based on non at zones becomes necessary. In this article, we propose a method to design grayscale component tree based on h-connections. The h-connection theory has been proposed in [7] and developed in [1, 3, 4, 8, 9]. It provides a general denition of the notion of connected component in arbitrary lattices. In Sec. 2, we present the h-connection theory and a method to generate a related hierarchical representation. This method is applied to a fuzzy h-connection in Sec. 3 where an algorithm is given to transform a Max-Tree into the new grayscale component tree. In Sec. 4, we illustrate the interest of this tree with an application on document image binarization. 2 H-component Tree This section presents the basis of the h-connection theory [7, 1] and gives a denition of the h-component tree. The construction of the tree is based on the z-zones [1] of the h-connection, together with a special partial ordering. Z-zones are particular regions where all points generate the same set of hyperconnected (h-connected) components and the entire image can be divided into such zones. Under a given condition, the Hasse diagram obtained in this way is acyclic and provides a tree representation. Let L be a complete lattice furnished with the partial ordering ≤, the inmum , the supremum. The least element of L is denoted by ⊥ = L. We assume the existence of a sup-generatin

Connected component trees for multivariate image processing applications in astronomy

International audienceIn this paper, we investigate the possibilities offered by the extension of the connected component trees (cc-trees) to multivariate images. We propose a general framework for image processing using the cc-tree based on the lattice theory and we discuss the possible applications depending on the properties of the underlying ordered set. This theoretical reflexion is illustrated by two applications in mul-tispectral astronomical imaging: source separation and object detection

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have not yet been applied to system design. This paper aims to bridge this gap by studying the use of an RL-based method for joint design and control of a real-world PV and battery system. The design problem is first formulated as a mixed-integer linear programming problem (MILP). The optimal MILP solution is then used to evaluate the performance of an RL agent trained in a surrogate environment designed for applying an existing data-driven algorithm. The main difference between the two models lies in their optimization approaches: while MILP finds a solution that minimizes the total costs for a one-year operation given the deterministic historical data, RL is a stochastic method that searches for an optimal strategy over one week of data on expectation over all weeks in the historical dataset. Both methods were applied on a toy example using one-week data and on a case study using one-year data. In both cases, models were found to converge to similar control solutions, but their investment decisions differed. Overall, these outcomes are an initial step illustrating benefits and challenges of using RL for the joint design and control of energy systems

Building Integrated Photovoltaics (BIPV): Review, Potentials, Barriers and Myths

To date, none of the predictions that have been made about the emerging BIPV industry have really hit the target. The anticipated boom has so far stalled and despite developing and promoting a number of excellent systems and products, many producers around the world have been forced to quit on purely economic grounds. The authors believe that after this painful cleansing of the market, a massive counter trend will follow, enlivened and carried forward by more advanced PV technologies and ever- stricter climate policies designed to achieve energy neutrality in a cost-effective way. As a result, the need for BIPV products for use in construction will undergo first a gradual and then a massive increase. The planning of buildings with multifunctional, integrated roof and façade elements capable of fulfilling the technical and legal demands will become an essential, accepted part of the architectonic mainstream and will also contribute to an aesthetic valorisation. Until then, various barriers need to be overcome in order to facilitate and accelerate BIPV. Besides issues related to mere cost-efficiency ratio, psychological and social factors also play an evident role. The goal of energy change linked to greater use of renewables can be successfully achieved only when all aspects are taken into account and when visual appeal and energy efficiency thus no longer appear to be an oxymoron

Fast and non-destructive detection on the EVA gel content in photovoltaic modules by optical reflection

Poly(ethylene-co-vinyl acetate) (EVA) has been the dominating material in the photovoltaic (PV) encapsulant market for decades, owing to its superior cost-performance balance. To achieve its desired material properties, EVA undergoes a curing reaction during the module encapsulation process. The resulting EVA gel content after encapsulation is an important criterion for the module encapsulation quality control. Normally, the determination of gel content is achieved using a tedious solvent extraction method. In this paper, a fast and nondestructive detection method on the EVA gel content based on the optical reflection is explored. First, the homogeneity of the EVA gel content distribution after the standard EVA encapsulation process is studied. Then, the feasibility of the proposed optical approach applied to transparent modules is investigated. After that, a method is developed to apply it to opaque modules by incorporating a mirror into the module construction. It was found that the haze factor of the reflected light correlates well with the EVA gel content in the opaque modules. This proof-of-concept work could lead to the development of a fast and nondestructive tool for detecting the EVA gel content in both transparent and opaque PV modules, which is promising for integration as an inline diagnostic tool in the module manufacturing line

An Approach to Solving the Complex Clinicogenomic Data Landscape in Precision Oncology: Learnings From the Design of WAYFIND-R, a Global Precision Oncology Registry

Oncología de precisión; Datos clinicogenómicosOncologia de precisió; Dades clinicogenòmiquesPrecision oncology; Clinicogenomic dataPrecision oncology, where patients are given therapies based on their genomic profile and disease trajectory, is rapidly evolving to become a pivotal part of cancer management, supported by regulatory approvals of biomarker-matched targeted therapies and cancer immunotherapies. However, next-generation sequencing (NGS)–based technologies have revealed an increasing number of molecular-based cancer subtypes with rare patient populations, leading to difficulties in executing/recruiting for traditional clinical trials. Therefore, approval of novel therapeutics based on traditional interventional studies may be difficult and time consuming, with delayed access to innovative therapies. Real-world data (RWD) that describe the patient journey in routine clinical practice can help elucidate the clinical utility of NGS-based genomic profiling, multidisciplinary case discussions, and targeted therapies. We describe key learnings from the setup of WAYFIND-R (NCT04529122), a first-of-its-kind global cancer registry collecting RWD from patients with solid tumors who have undergone NGS-based genomic profiling. The meaning of ‘generalizability’ and ‘high quality’ for RWD across different geographic areas was revisited, together with patient recruitment processes, and data sharing and privacy. Inspired by these learnings, WAYFIND-R’s design will help physicians discuss patient treatment plans with their colleagues, improve understanding of the impact of treatment decisions/cancer care processes on patient outcomes, and provide a platform to support the design and conduct of further clinical/epidemiologic research. WAYFIND-R demonstrates user-friendly, electronic case report forms, standardized collection of molecular tumor board-based decisions, and a dashboard providing investigators with access to local cohort-level data and the ability to interact with colleagues or search the entire registry to find rare populations. Overall, WAYFIND-R will inform on best practice for NGS-based treatment decisions by clinicians, foster global collaborations between cancer centers and enable robust conclusions regarding outcome data to be drawn, improve understanding of disparities in patients’ access to advanced diagnostics and therapies, and ultimately drive advances in precision oncology