Examinando por Autor "Delrieux, Claudio"
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Ítem Automatic feature extraction and classification of Iberian ceramics based on deep convolutional networks(Elsevier, 2020-02) Cintas, Celia; Lucena López, Manuel José; Fuertes García, José Manuel; Delrieux, Claudio; Navarro, Pablo; González José, Rolando; Molinos Molinos, ManuelAccurate classification of pottery vessels is a key aspect in several archaeological inquiries, including documentation of changes in style and ornaments, inference of chronological and ethnic groups, trading routes analyses, and many other matters. We present an unsupervised method for automatic feature extraction and classification of wheel-made vessels. A convolutional neural network was trained with a profile image database from Iberian wheel made pottery vessels found in the upper valley of the Guadalquivir River (Spain). During the design of the model, data augmentation and regularization techniques were implemented to obtain better generalization outcomes. The resulting model is able to provide classification on profile images automatically, with an accuracy mean score of 0.9013. Such computation methods will enhance and complement research on characterization and classification of pottery assemblages based on fragments.Ítem IberianVoxel: Automatic Completion of Iberian Ceramics for Cultural Heritage Studies(International Joint Conferences on Artificial Intelligence Organization., 2023-08) Navarro, Pablo; Cintas, Celia; Lucena, Manuel; Fuertes, José M.; Rueda, Antonio; Segura, Rafael; Ogáyar-Anguita, Carlos; González-José, Rolando; Delrieux, ClaudioAccurate completion of archaeological artifacts is a critical aspect in several archaeological studies, including documentation of variations in style, inference of chronological and ethnic groups, and trading routes trends, among many others. However, most available pottery is fragmented, leading to missing textural and morphological cues. Currently, the reassembly and completion of fragmented ceramics is a daunting and time-consuming task, done almost exclusively by hand, which requires the physical manipulation of the fragments. To overcome the challenges of manual reconstruction, reduce the materials’ exposure and deterioration, and improve the quality of reconstructed samples, we present IberianVoxel, a novel 3D Autoencoder Generative Adversarial Network (3D AE-GAN) framework tested on an extensive database with complete and fragmented references. We generated a collection of 1001 3D voxelized samples and their fragmented references from Iberian wheel-made pottery profiles. The fragments generated are stratified into different size groups and across multiple pottery classes. Lastly, we provide quantitative and qualitative assessments to measure the quality of the reconstructed voxelized samples by our proposed method and archaeologists’ evaluation.Ítem Learning Feature Representation of Iberian Ceramics with Automatic Classification Models(Elsevier, 2021-03) Navarro, Pablo; Cintas, Celia; Lucena, Manuel; Fuertes, José M.; Delrieux, Claudio; Molinos, ManuelIn Cultural Heritage inquiries, a common requirement is to establish time-based trends between archaeological artifacts belonging to different periods of a given culture, enabling among other things to determine chronological inferences with higher precision. Among these artifacts, pottery vessels are significantly useful, given their relative abundance in most archaeological sites. However, this very abundance makes difficult and complex an accurate representation, since no two of these artifacts are identical, and therefore classification criteria must be justified and applied. For this purpose, in this work we propose the use of deep learning architectures to extract automatically learnedfeatures without prior knowledge or engineered features. By means of transfer learning, a Residual Neural Network was retrained with a binary image database of Iberian wheel-made pottery vessels’ profiles. These vessels pertain to archaeological sites located in the upper valley of the Guadalquivir River (Spain). The resulting model is able to provide an accurate feature representation space, which is able to classify profile images automatically, achieving a mean accuracy of 0.98. This accuracy is remarkably higher as compared with other state of the art machine learning approaches, where several feature extraction techniques were applied together with multiple classifier models. Furthermore, we show the relevance of introspection in our automatic feature extraction method prior to classification, and the effects of poor feature selection. These results provide novel approaches to current research in automatic feature representation and classification of different objects of study within the Archaeology domain.Ítem Noise Reduction using Novel Loss Functions to Compute Tissue Mineral Density and Trabecular Bone Volume Fraction on Low Resolution QCT(Elsevier, 2020-12) Thomsen, Felix; Delrieux, Claudio; Pisula, Juan Ignacio; Fuertes-García, José Manuel; Lucena-López, Manuel; de-Luis-García, Rodrigo; Borggrefe, JanMicro-structural parameters of the thoracic or lumbar spine generally carry insufficient accuracy and precision for clinical in vivo studies when assessed on quantitative computed tomography (QCT). We propose a 3D convolutional neural network with specific loss functions for QCT noise reduction to compute micro-structural parameters such as tissue mineral density (TMD) and bone volume ratio (BV/TV) with significantly higher accuracy than using no or standard noise reduction filters. The vertebra-phantom study contained high resolution peripheral and clinical CT scans with simulated in vivo CT noise and nine repetitions of three different tube currents (100, 250 and 360 mAs). Five-fold cross validation was performed on 20466 purely spongy pairs of noisy and ground-truth patches. Comparison of training and test errors revealed high robustness against over-fitting. While not showing effects for the assessment of BMD and voxel-wise densities, the filter improved thoroughly the computation of TMD and BV/TV with respect to the unfiltered data. Root-mean-square and accuracy errors of low resolution TMD and BV/TV decreased to less than $17%$ of the initial values. Furthermore filtered low resolution scans revealed still more TMD- and BV/TV-relevant information than high resolution CT scans, either unfiltered or filtered with two state-of-the-art standard denoising methods. The proposed architecture is threshold and rotational invariant, applicable on a wide range of image resolutions at once, and likely serves for an accurate computation of further micro-structural parameters. Furthermore, it is less prone for over-fitting than neural networks that compute structural parameters directly. In conclusion, the method is potentially important for the diagnosis of osteoporosis and other bone diseases since it allows to assess relevant 3D micro-structural information from standard low exposure CT protocols such as 100mAs and 120kVp.Ítem Reconstruction of Iberian ceramic potteries using generative adversarial networks(Nature Research, 2022-06-23) Navarro, Pablo; Cintas, Celia; Lucena, Manuel; Fuertes, José Manuel; Segura, Rafael; Delrieux, Claudio; González-José, RolandoSeveral aspects of past culture, including historical trends, are inferred from time-based patterns observed in archaeological artifacts belonging to different periods. The presence and variation of these objects provides important clues about the Neolithic revolution and, given their relative abundance in most archaeological sites, ceramic potteries are significantly helpful in this purpose. Nonetheless, most available pottery is fragmented, leading to missing morphological information. Currently, the reassembly of fragmented objects from a collection of thousands of mixed fragments is a daunting and time-consuming task done almost exclusively by hand, which requires the physical manipulation of the fragments. To overcome the challenges of manual reconstruction and improve the quality of reconstructed samples, we present IberianGAN, a customized Generative Adversarial Network (GAN) tested on an extensive database with complete and fragmented references. We trained the model with 1072 samples corresponding to Iberian wheel-made pottery profiles belonging to archaeological sites located in the upper valley of the Guadalquivir River (Spain). Furthermore, we provide quantitative and qualitative assessments to measure the quality of the reconstructed samples, along with domain expert evaluation with archaeologists. The resulting framework is a possible way to facilitate pottery reconstruction from partial fragments of an original piece.