Persona: Carmona Suárez, Enrique J.
Cargando...
Dirección de correo electrónico
ORCID
0000-0002-7487-745X
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
Carmona Suárez
Nombre de pila
Enrique J.
Nombre
16 resultados
Resultados de la búsqueda
Mostrando 1 - 10 de 16
Publicación A new video segmentation method of moving objects based on blob-level knowledge(Elsevier, 2008-02-01) Martínez Campos, Javier; Mira Mira, José; Carmona Suárez, Enrique J.Variants of the background subtraction method are broadly used for the detection of moving objects in video sequences in different applications. In this work we propose a new approach to the background subtraction method which operates in the colour space and manages the colour information in the segmentation process to detect and eliminate noise. This new method is combined with blob-level knowledge associated with different types of blobs that may appear in the foreground. The idea is to process each pixel differently according to the category to which it belongs: real moving objects, shadows, ghosts, reflections, fluctuation or background noise. Thus, the foreground resulting from processing each image frame is refined selectively, applying at each instant the appropriate operator according to the type of noise blob we wish to eliminate. The approach proposed is adaptive, because it allows both the background model and threshold model to be updated. On the one hand, the results obtained confirm the robustness of the method proposed in a wide range of different sequences and, on the other hand, these results underline the importance of handling three colour components in the segmentation process rather than just the one grey-level component.Publicación Using genetic algorithms to improve the thermodynamic efficiency of gas turbines designed by traditional methods(Elsevier, 2012-11) Chaquet Ulldemolins, José María; Corral, Roque; Carmona Suárez, Enrique J.A method for optimizing the thermodynamic efficiency of aeronautical gas turbines designed by classical methods is presented. This method is based in the transformation of the original constrained optimization problem into a new constrained free optimization problem which is solved by a genetic algorithm. Basically, a set of geometric, aerodynamic and acoustic noise constraints must be fulfilled during the optimization process. As a case study, the thermodynamic efficiency of an already optimized by traditional methods real aeronautical low pressure turbine design of 13 rows has been successfully improved, increasing the turbine efficiency by 0.047% and reducing the total number of airfoils by 1.61%. In addition, experimental evidence of a strong correlation between the total number of airfoils and the turbine efficiency has been observed. This result would allow us to use the total number of airfoils as a cheap substitute of the turbine efficiency for a coarse optimization at the first design steps.Publicación Fast detection of the main anatomical structures in digital retinal images based on intra-and inter-structure relational knowledge(Elsevier, 2017-10) Molina Casado, José María; García Feijoó, Julián; Carmona Suárez, Enrique J.Background and objective: The anatomical structure detection in retinal images is an open problem. However, most of the works in the related literature are oriented to the detection of each structure individually or assume the previous detection of a structure which is used as a reference. The objective of this paper is to obtain simultaneous detection of the main retinal structures (optic disc, macula, network of vessels and vascular bundle) in a fast and robust way. Methods: We propose a new methodology oriented to accomplish the mentioned objective. It consists of two stages. In an initial stage, a set of operators is applied to the retinal image. Each operator uses intra-structure relational knowledge in order to produce a set of candidate blobs that belongs to the desired structure. In a second stage, a set of tuples is created, each of which contains a different combination of the candidate blobs. Next, filtering operators, using inter-structure relational knowledge, are used in order to find the winner tuple. A method using template matching and mathematical morphology is implemented following the proposed methodology. Results: A success is achieved if the distance between the automatically detected blob center and the actual structure center is less than or equal to one optic disc radius. The success rates obtained in the different public databases analyzed were: MESSIDOR (99.33%, 98.58%, 97.92%), DIARETDB1 (96.63%, 100%, 97.75%), DRIONS (100%, n/a, 100%) and ONHSD (100%, 98.85%, 97.70%) for optic disc (OD), macula (M) and vascular bundle (VB), respectively. Finally, the overall success rate obtained in this study for each structure was: 99.26% (OD), 98.69% (M) and 98.95% (VB). The average time of processing per image was 4.16 ± 0.72 s. Conclusions: The main advantage of the use of inter-structure relational knowledge was the reduction of the number of false positives in the detection process. The implemented method is able to simultaneously detect four structures. It is fast, robust and its detection results are competitive in relation to other methods of the recent literature.Publicación Simultaneous segmentation of the optic disc and fovea in retinal images using evolutionary algorithms(Springer, 2021-03) Molina Casado, José María; Carmona Suárez, Enrique J.In this work, we present a new methodology to simultaneously segment anatomical structures in medical images. Additionally, this methodology is instantiated in a method that is used to simultaneously segment the optic disc (OD) and fovea in retinal images. The OD and fovea are important anatomical structures that must be previously identified in any image-based computer-aided diagnosis system dedicated to diagnosing retinal pathologies that cause vision problems. Basically, the simultaneous segmentation method uses an OD-fovea model and an evolutionary algorithm. On the one hand, the model is built using the intra-structure relational knowledge, associated with each structure, and the inter-structure relational knowledge existing between both and other retinal structures. On the other hand, the evolutionary algorithm (differential evolution) allows us to automatically adjust the instance parameters that best approximate the OD-fovea model in a given retinal image. The method is evaluated in the MESSIDOR public database. Compared with other recent segmentation methods in the related literature, competitive segmentation results are achieved. In particular, a sensitivity and specificity of 0.9072 and 0.9995 are respectively obtained for the OD. Considering a success when the distance between the detected and actual center is less than or equal to η times the OD radius, the success rates obtained for the fovea are 97.3% and 99.0% for η = 1=2 and η = 1 and, respectively. The segmentation average time per image is 29.35 s.Publicación Using covariance matrix adaptation evolution strategies for solving different types of differential equations(Springer, 2019-03-15) Chaquet Ulldemolins, José María; Carmona Suárez, Enrique J.A novel mesh-free heuristic method for solving differential equations is proposed. The new approach can cope with linear, nonlinear, and partial differential equations (DE), and systems of DEs. Candidate solutions are expressed using a linear combination of kernel functions. Thus, the original problem is transformed into an optimization problem that consists in finding the parameters that define each kernel. The new optimization problem is solved applying a Covariance Matrix Adaptation Evolution Strategy. To increase the accuracy of the results, a Downhill Simplex local search is applied to the best solution found by the mentioned evolutionary algorithm. Our method is applied to 32 differential equations extracted from the literature. All problems are successfully solved, achieving competitive accuracy levels when compared to other heuristic methods. A simple comparison with numerical methods is performed using two partial differential equations to show the pros and cons of the proposed algorithm. To verify the potential of this approach with a more practical problem, an electric circuit is analyzed in depth. The method can obtain the dynamic behavior of the circuit in a parametric way, taking into account different component values.Publicación Solving differential equations with Fourier series and Evolution Strategies(Elsevier, 2012-09) Chaquet Ulldemolins, José María ; Carmona Suárez, Enrique J.A novel mesh-free approach for solving differential equations based on Evolution Strategies (ESs) is presented. Any structure is assumed in the equations making the process general and suitable for linear and nonlinear ordinary and partial differential equations (ODEs and PDEs), as well as systems of ordinary differential equations (SODEs). Candidate solutions are expressed as partial sums of Fourier series. Taking advantage of the decreasing absolute value of the harmonic coefficients with the harmonic order, several ES steps are performed. Harmonic coefficients are taken into account one by one starting with the lower order ones. Experimental results are reported on several problems extracted from the literature to illustrate the potential of the proposed approach. Two cases (an initial value problem and a boundary condition problem) have been solved using numerical methods and a quantitative comparative is performed. In terms of accuracy and storing requirements the proposed approach outperforms the numerical algorithm.Publicación Deformable registration of multimodal retinal images using a weakly supervised deep learning approach(Springer, 2023-03-28) Martínez Río, Javier; Cancelas, Daniel; Novo, Jorge; Ortega, Marcos; Carmona Suárez, Enrique J.There are different retinal vascular imaging modalities widely used in clinical practice to diagnose different retinal pathologies. The joint analysis of these multimodal images is of increasing interest since each of them provides common and complementary visual information. However, if we want to facilitate the comparison of two images, obtained with different techniques and containing the same retinal region of interest, it will be necessary to make a previous registration of both images. Here, we present a weakly supervised deep learning methodology for robust deformable registration of multimodal retinal images, which is applied to implement a method for the registration of fluorescein angiography (FA) and optical coherence tomography angiography (OCTA) images. This methodology is strongly inspired by VoxelMorph, a general unsupervised deep learning framework of the state of the art for deformable registration of unimodal medical images. The method was evaluated in a public dataset with 172 pairs of FA and superficial plexus OCTA images. The degree of alignment of the common information (blood vessels) and preservation of the non-common information (image background) in the transformed image were measured using the Dice coefficient (DC) and zero-normalized cross-correlation (ZNCC), respectively. The average values of the mentioned metrics, including the standard deviations, were DC = 0.72 ± 0.10 and ZNCC = 0.82 ± 0.04. The time required to obtain each pair of registered images was 0.12 s. These results outperform rigid and deformable registration methods with which our method was compared.Publicación Semantic-guided generative latent diffusion augmentation approaches for improving the neovascularization diagnosis in OCT-A imaging(ELSEVIER, 2025-01-13) Iglesias Morís, Daniel; Moura, Joaquim de; Novo, Jorge; Ortega, Marcos; Carmona Suárez, Enrique J.Age-related Macular Degeneration (AMD) presents an enormous challenge in Western Societies due to the increase in life expectancy. AMD is characterized for causing Macular Neovascularization. Optical Coherence Tomography Angiography (OCT-A) represents an advanced method to help find evidence of the disease. In this context, deep learning algorithms are suitable to make a screening of the disease. However, biomedical imaging domains are usually affected by the data scarcity issue. The mitigation of this problem can be achieved with the support of generative latent diffusion models. This represents a powerful strategy to artificially augment the cardinality of the original dataset. In this work, we present a novel fully automatic methodology to generate OCT-A images, guided by semantic information, to reduce the impact of data scarcity and to enable an accurate neovascularization diagnosis. The evaluation has been performed with a specific dataset composed of two different fields of view commonly used by clinicians. The results demonstrate a top accuracy of 96.50% 1.37%, using 3 × 3 scans, and 95.79% 1.44%, when using 6 × 6 scans. The proposed methodology has great potential to be extrapolated to other imaging modalities and domains.Publicación A Survey of Video Datasets for Human Action and Activity Recognition(Elsevier, 2013-06) Chaquet Ulldemolins, José María; Fernández Caballero, Antonio; Carmona Suárez, Enrique J.Vision-based human action and activity recognition has an increasing importance among the computer vision community with applications to visual surveillance, video retrieval and human–computer interaction. In recent years, more and more datasets dedicated to human action and activity recognition have been created. The use of these datasets allows us to compare different recognition systems with the same input data. The survey introduced in this paper tries to cover the lack of a complete description of the most important public datasets for video-based human activity and action recognition and to guide researchers in the election of the most suitable dataset for benchmarking their algorithms.Publicación A block-based model for monitoring of human activity(Elsevier, 2011-03) Folgado Zuñiga, Encarnación; Rincón Zamorano, Mariano; Bachiller Mayoral, Margarita; Carmona Suárez, Enrique J.The study of human activity is applicable to a large number of science and technology fields, such as surveillance, biomechanics or sports applications. This article presents BB6-HM, a block-based human model for real-time monitoring of a large number of visual events and states related to human activity analysis, which can be used as components of a library to describe more complex activities in such important areas as surveillance, for example, luggage at airports, clients’ behaviour in banks and patients in hospitals. BB6-HM is inspired by the proportionality rules commonly used in Visual Arts, i.e., for dividing the human silhouette into six rectangles of the same height. The major advantage of this proposal is that analysis of the human can be easily broken down into regions, so that we can obtain information of activities. The computational load is very low, so it is possible to define a very fast implementation. Finally, this model has been applied to build classifiers for the detection of primitive events and visual attributes using heuristic rules and machine learning techniques.