Hamdi Dibeklioglu, Albert Ali Salah, & Theo Gevers. (2013). Like Father, Like Son: Facial Expression Dynamics for Kinship Verification. In 15th IEEE International Conference on Computer Vision (pp. 1497–1504).
Abstract: Kinship verification from facial appearance is a difficult problem. This paper explores the possibility of employing facial expression dynamics in this problem. By using features that describe facial dynamics and spatio-temporal appearance over smile expressions, we show that it is possible to improve the state of the art in this problem, and verify that it is indeed possible to recognize kinship by resemblance of facial expressions. The proposed method is tested on different kin relationships. On the average, 72.89% verification accuracy is achieved on spontaneous smiles.
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Hamdi Dibeklioglu, Albert Ali Salah, & Theo Gevers. (2012). A Statistical Method for 2D Facial Landmarking. TIP - IEEE Transactions on Image Processing, 21(2), 844–858.
Abstract: IF = 3.32
Many facial-analysis approaches rely on robust and accurate automatic facial landmarking to correctly function. In this paper, we describe a statistical method for automatic facial-landmark localization. Our landmarking relies on a parsimonious mixture model of Gabor wavelet features, computed in coarse-to-fine fashion and complemented with a shape prior. We assess the accuracy and the robustness of the proposed approach in extensive cross-database conditions conducted on four face data sets (Face Recognition Grand Challenge, Cohn-Kanade, Bosphorus, and BioID). Our method has 99.33% accuracy on the Bosphorus database and 97.62% accuracy on the BioID database on the average, which improves the state of the art. We show that the method is not significantly affected by low-resolution images, small rotations, facial expressions, and natural occlusions such as beard and mustache. We further test the goodness of the landmarks in a facial expression recognition application and report landmarking-induced improvement over baseline on two separate databases for video-based expression recognition (Cohn-Kanade and BU-4DFE).
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H.M.G. Stokman, & Theo Gevers. (2007). Selection and Fusion of Color Models for Image Feature Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.29(3):371–381.
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H. Martin Kjer, Jens Fagertuna, Sergio Vera, Debora Gil, Miguel Angel Gonzalez Ballester, & Rasmus R. Paulsena. (2016). Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior. PRL - Patter Recognition Letters, 76(1), 76–82.
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H. Martin Kjer, Jens Fagertun, Sergio Vera, & Debora Gil. (2017). Medial structure generation for registration of anatomical structures. In Skeletonization, Theory, Methods and Applications (Vol. 11).
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H. Emrah Tasli, Jan van Gemert, & Theo Gevers. (2013). Spot the differences: from a photograph burst to the single best picture. In 21ST ACM International Conference on Multimedia (pp. 729–732).
Abstract: With the rise of the digital camera, people nowadays typically take several near-identical photos of the same scene to maximize the chances of a good shot. This paper proposes a user-friendly tool for exploring a personal photo gallery for selecting or even creating the best shot of a scene between its multiple alternatives. This functionality is realized through a graphical user interface where the best viewpoint can be selected from a generated panorama of the scene. Once the viewpoint is selected, the user is able to go explore possible alternatives coming from the other images. Using this tool, one can explore a photo gallery efficiently. Moreover, additional compositions from other images are also possible. With such additional compositions, one can go from a burst of photographs to the single best one. Even funny compositions of images, where you can duplicate a person in the same image, are possible with our proposed tool.
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H. Emrah Tasli, Cevahir Çigla, Theo Gevers, & A. Aydin Alatan. (2013). Super pixel extraction via convexity induced boundary adaptation. In 14th IEEE International Conference on Multimedia and Expo (pp. 1–6).
Abstract: This study presents an efficient super-pixel extraction algorithm with major contributions to the state-of-the-art in terms of accuracy and computational complexity. Segmentation accuracy is improved through convexity constrained geodesic distance utilization; while computational efficiency is achieved by replacing complete region processing with boundary adaptation idea. Starting from the uniformly distributed rectangular equal-sized super-pixels, region boundaries are adapted to intensity edges iteratively by assigning boundary pixels to the most similar neighboring super-pixels. At each iteration, super-pixel regions are updated and hence progressively converging to compact pixel groups. Experimental results with state-of-the-art comparisons, validate the performance of the proposed technique in terms of both accuracy and speed.
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H. Chouaib, Salvatore Tabbone, Oriol Ramos Terrades, F. Cloppet, N. Vincent, & A.T. Thierry Paquet. (2008). Sélection de Caractéristiques à partir d'un algorithme génétique et d'une combinaison de classifieurs Adaboost. In Colloque International Francophone sur l'Ecrit et le Document (pp. 181–186).
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H. Chouaib, Oriol Ramos Terrades, Salvatore Tabbone, F. Cloppet, & N. Vincent. (2008). Feature Selection Combining Genetic Algorithm and Adaboost Classifiers. In 19th International Conference on Pattern Recognition (pp. 1–4).
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Guim Perarnau, Joost Van de Weijer, Bogdan Raducanu, & Jose Manuel Alvarez. (2016). Invertible conditional gans for image editing. In 30th Annual Conference on Neural Information Processing Systems Worshops.
Abstract: Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.
Additionally, we evaluate the design of cGANs. The combination of an encoder
with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real
images with deterministic complex modifications.
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Guillermo Torres, Sonia Baeza, Carles Sanchez, Ignasi Guasch, Antoni Rosell, & Debora Gil. (2022). An Intelligent Radiomic Approach for Lung Cancer Screening. APPLSCI - Applied Sciences, 12(3), 1568.
Abstract: The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed
forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection.
Keywords: Lung cancer; Early diagnosis; Screening; Neural networks; Image embedding; Architecture optimization
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Guillermo Torres, Jan Rodríguez Dueñas, Sonia Baeza, Antoni Rosell, Carles Sanchez, & Debora Gil. (2023). Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images. In 7th Workshop on Digital Image Processing for Medical and Automotive Industry in the framework of SYNASC 2023.
Abstract: This study shows the generation process and the subsequent study of the representation space obtained by extracting GLCM texture features from computer-aided tomography (CT) scans of pulmonary nodules (PN). For this, data from 92 patients from the Germans Trias i Pujol University Hospital were used. The workflow focuses on feature extraction using Pyradiomics and the VGG16 Convolutional Neural Network (CNN). The aim of the study is to assess whether the data obtained have a positive impact on the diagnosis of lung cancer (LC). To design a machine learning (ML) model training method that allows generalization, we train SVM and neural network (NN) models, evaluating diagnosis performance using metrics defined at slice and nodule level.
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Guillermo Torres, Debora Gil, Antonio Rosell, Sonia Baeza, & Carles Sanchez. (2023). A radiomic biopsy for virtual histology of pulmonary nodules. In IEEE International Symposium on Biomedical Imaging.
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Guillermo Torres, Debora Gil, Antoni Rosell, S. Mena, & Carles Sanchez. (2023). Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project. IJCARS - International Journal of Computer Assisted Radiology and Surgery, .
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Guillermo Torres, Debora Gil, Antoni Rosell, S. Mena, & Carles Sanchez. (2023). Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules. In 37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery.
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