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Petia Radeva, & Jordi Vitria. (2001). Region Based Approach for Discriminant Snakes..
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Petia Radeva, & Jordi Vitria. (2003). “Inteligencia artificial” Centre de Visio per Computador.
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Petia Radeva, & Jordi Vitria. (2001). Region-Based Approach for Discriminant Snakes.
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Petia Radeva, & Jordi Vitria. (2004). Discriminant Projections Embedding for Nearest Neighbor Classification.
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Petia Radeva, J. Guerrero, & C. Molina. (1998). A Physics-Based Kohonen Ring..
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Petia Radeva, & Enric Marti. (1995). Facial Features Segmentation by Model-Based Snakes..
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Petia Radeva, Cristina Cañero, Juan J. Villanueva, J. Mauri, & E Fernandez-Nofrerias. (2001). 3D Reconstruction of a Stent by Deformable Models..
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Petia Radeva, A.F. Sole, Antonio Lopez, & Joan Serrat. (1998). Detecting Nets of Linear Structures in Satellite Images..
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Petia Radeva, A.F. Sole, Antonio Lopez, & Joan Serrat. (1999). Detecting Nets of Linear Structures in Satellite Images..
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Petia Radeva. (1993). A Rule-Based Approach to Hand X-Ray image Segmentation..
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Petia Radeva. (1993). Segmentacion de Imagenes Radiograficas con Snakes. Aplicacion a la Determinacion de la Madurez Osea..
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Petia Radeva. (2003). On the Role of Intravascular Ultrasound Image Analysis.
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Pau Riba, Sounak Dey, Ali Furkan Biten, & Josep Llados. (2021). Localizing Infinity-shaped fishes: Sketch-guided object localization in the wild.
Abstract: This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images. In this cross-modal setting, we first contribute with a tough-to-beat baseline that without any specific SGOL training is able to outperform the previous works on a fixed set of classes. The baseline is useful to analyze the performance of SGOL approaches based on available simple yet powerful methods. We advance prior arts by proposing a sketch-conditioned DETR (DEtection TRansformer) architecture which avoids a hard classification and alleviates the domain gap between sketches and images to localize object instances. Although the main goal of SGOL is focused on object detection, we explored its natural extension to sketch-guided instance segmentation. This novel task allows to move towards identifying the objects at pixel level, which is of key importance in several applications. We experimentally demonstrate that our model and its variants significantly advance over previous state-of-the-art results. All training and testing code of our model will be released to facilitate future researchhttps://github.com/priba/sgol_wild.
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Pau Riba, Andreas Fischer, Josep Llados, & Alicia Fornes. (2020). Learning Graph Edit Distance by Graph NeuralNetworks.
Abstract: The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset.
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Pau Cano, Alvaro Caravaca, Debora Gil, & Eva Musulen. (2023). Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images.
Abstract: This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori.
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