Distilling Structure from Imagery : Graph-based Models for the Interpretation of Document Images

From its early stages, the community of Pattern Recognition and Computer Vision has considered the importance of leveraging the structural information when understanding images. Usually, graphs have been proposed as a suitable model to represent this kind of information due to their flexibility and representational power able to codify both, the components, objects, or entities and their pairwise relationship. Even though graphs have been successfully applied to a huge variety of tasks, as a result of their symbolic and relational nature, graphs have always suffered from some limitations compared to statistical approaches. Indeed, some trivial mathematical operations do not have an equivalence in the graph domain. For instance, in the core of many pattern recognition applications, there is a need to compare two objects. This operation, which is trivial when considering feature vectors defined in ℝn, is not properly defined for graphs. In this thesis, we have investigated the importance of the structural information from two perspectives, the traditional graph-based methods and the new advances on Geometric Deep Learning. On the one hand, we explore the problem of defining a graph representation and how to deal with it on a large scale and noisy scenario. On the other hand, Graph Neural Networks are proposed to first redefine a Graph Edit Distance methodologies as a metric learning problem, and second, to apply them in a real use case scenario for the detection of repetitive patterns which define tables in invoice documents. As experimental framework, we have validated the different methodological contributions in the domain of Document Image Analysis and Recognition

Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition

Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step.Comment: Accepted to WACV 202

Experimental study of visual corona under aeronautic pressure conditions using low-cost imaging sensors

Visual corona tests have been broadly applied for identifying the critical corona points of diverse high-voltage devices, although other approaches based on partial discharge or radio interference voltage measurements are also widely applied to detect corona activity. Nevertheless, these two techniques must be applied in screened laboratories, which are scarce and expensive, require sophisticated instrumentation, and typically do not allow location of the discharge points. This paper describes the detection of the visual corona and location of the critical corona points of a sphere-plane gap configurations under different pressure conditions ranging from 100 to 20 kPa, covering the pressures typically found in aeronautic environments. The corona detection is made with a low-cost CMOS imaging sensor from both the visible and ultraviolet (UV) spectrum, which allows detection of the discharge points and their locations, thus significantly reducing the complexity and costs of the instrumentation required while preserving the sensitivity and accuracy of the measurements. The approach proposed in this paper can be applied in aerospace applications to prevent the arc tracking phenomenon, which can lead to catastrophic consequences since there is not a clear protection solution, due to the low levels of leakage current involved in the pre-arc phenomenon.Peer ReviewedPostprint (published version

Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval

In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognizes two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended, that consists of 330,000 sketches and 204,000 photos spanning across 110 categories. Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic. We then formulate a ZS-SBIR framework to jointly model sketches and photos into a common embedding space. A novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap. External semantic knowledge is further embedded to aid semantic transfer. We show that, rather surprisingly, retrieval performance significantly outperforms that of state-of-the-art on existing datasets that can already be achieved using a reduced version of our model. We further demonstrate the superior performance of our full model by comparing with a number of alternatives on the newly proposed dataset. The new dataset, plus all training and testing code of our model, will be publicly released to facilitate future researchComment: Oral paper in CVPR 201

Reduced scale feasibility of temperature rise tests in substation connectors

Due to the important increase of the power of electrical transmission and distribution grids expected for the following years, especially in developing countries such as Kenya, Brazil, Philippines or Mexico among others, that have planes of generating energy from clean sources far away from the centres of consumption [1] it becomes a matter of special importance adapting and developing new substation connectors’ testing methods according to the power and temperature regimes at which they are expected to work. The international normative frame of substation connectors established both by the International Electrotechnical Committee (IEC) [2] and the National Electrical Manufacturers Association (NEMA) [3] sets standardized tests for the evaluation of high voltage connectors. These tests are routinely done within the quality plans of the manufacturers. At the moment, testing of substation connectors –and in general switchgear and fittings- is time demanding and costly due to the energy consumed by such tests. The expectations for the following years are that the power consumption of these tests will not do nothing but grow due to expected increase of power of worldwide overhead lines. For instance, today temperature rise tests in substation connectors involve power ranges up to 100 kVA, which are applied in cycles that can last several weeks. These tests are only feasible in few laboratories and at a very high cost: temporary, monetary, energetic and environmental. For this reason, following the line of other technologies such as aeronautics, naval engineering, or automotive as well as other studies done in the field of electrical engineering specially related to the corona effect [4], this study proposes to develop a reduced scale test system to perform temperature rise tests for substation connectors. Both, a theoretical framework based on analytical formulas, finite element method (FEM) simulations and experimental data has been developed to conduct reduced scale temperature rise tests and to set the conditions at which they provide comparable results to those attained in the original scale tests. Firstly, two circular loops (original and reduced scale loops) composed of a power conductor and two terminal connectors were analysed. The aim of this first study was to determine in an easy and trustful way the voltage and current values to be applied in experimental reduced scale tests to achieve the same steady-state temperature as in the original scale temperature rise test. The scale relationship between tests was set in 1:1.8, although the method proposed in this study can deal with any other scale factor. This study was useful in order to have a first sight of the final results of the procedure using substation connectors.Postprint (published version

Simplification and cost reduction of visual corona tests

Peer ReviewedPostprint (author's final draft

Transient thermal modelling of substation connectors by means of dimensionality reduction

This paper proposes a simple, fast and accurate simulation approach based on one-dimensional reduction and the application of the finite difference method (FDM) to determine the temperatures rise in substation connectors. The method discretizes the studied three-dimensional geometry in a finite number of one-dimensional elements or regions in which the energy rate balance is calculated. Although a one-dimensional reduction is applied, to ensure the accuracy of the proposed transient method, it takes into account the three-dimensional geometry of the analyzed system to determine for all analyzed elements and at each time step different parameters such as the incremental resistance of each element or the convective coefficient. The proposed approach allows fulfilling both accuracy and low computational burden criteria, providing similar accuracy than the three-dimensional finite element method but with much lower computational requirements. Experimental results conducted in a high-current laboratory validate the accuracy and effectiveness of the proposed method and its usefulness to design substation connectors and other power devices and components with an optimal thermal behavior.Postprint (published version

Use of DSLR and sonic cameras to detect and locate high-voltage corona discharges

Corona discharges are a concern in high-voltage applications. It is of utmost importance to detect and locate the discharges at an early stage using simple methods for this purpose. This paper evaluates and compares the sensitivity of two methods for detecting and locating the source of discharges, which are based on a digital single-lens reflex (DSLR) camera and a portable wideband sonic camera incorporating a matrix of micro-electromechanical systems (MEMS) microphones. Both cameras can generate an image of the studied area where the discharge sites are identified. The study is carried out with different electrode geometries, 50 Hz alternating current (ac) and positive and negative direct current (dc) supplies, and the effect of the distance between the sensor and the discharge sites is also analyzed. The presented results show that the sonic camera enables fast, simple, and sensitive detection and localization of the source of corona discharges even at a very early stage in daylight conditions, regardless of the type of power supply, that is, ac or positive/negative dc, and at distance of several meters from the discharge source.This research was funded by Ministerio de Ciencia e Innovación de España, grant number PID2020-114240RB-I00 and by the Generalitat de Catalunya, grant number 2017 SGR 967.Peer ReviewedPostprint (author's final draft