Examinando por Autor "Lucena, Manuel"
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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 LiDAR attribute based point cloud labeling using CNNs with 3D Convolution Layers(Elsevier, 2023-11) Díaz-Medina, Miguel; Fuertes, José Manuel; Segura-Sánchez, Rafael; Lucena, Manuel; Ogáyar-Anguita, Carlos J.The goal of this work is to study how semantic segmentation techniques can be adapted to use LiDAR point clouds in their original format as input. Until now, almost all the methods that have been proposed to apply deep learning models on point clouds keep their focus on generic point clouds, making use only of geometric or color information.The deep learning architecture proposed in this work acts as an alternative to classical convolution models with other convolutional operators, applied to 3D data, such as EdgeConv, which use their nearest neighbors to extract local features. We show the results of an experimentation that reveals the influence of additional LiDAR information channels on the performance of the neural network. Unlike other related works, this one is based in the study of semantic segmentation applied to outdoor environments.Ítem Multimodal speaker diarization for meetings using volume-evaluated SRP-PHAT and video analysis(Springer, 2018-04-11) Cabañas-Molero, Pablo Antonio; Lucena, Manuel; Fuertes, José Manuel; Vera-Candeas, Pedro; Ruiz-Reyes, NicolásSpeaker diarization is traditionally defined as the problem of determining “who speaks when” given an audio or video stream. This is an important task in many applications for meeting rooms, including automatic transcription of conversations, camera steering or content summarization. When the room is equipped with microphone arrays and cameras, speakers can be distinguished according to their location and the problem can be addressed through localization techniques. This article proposes a multimodal speaker diarization system for meeting environments based on a modified SRP-PHAT function evaluated on space volumes rather than discrete points. In our system, this function is used in combination with a circular array, enabling audio-based localization based on the selection of local maxima. Voicing detection is used to detect speech frames, whereas video analysis is introduced to aid in the decision when users move or simultaneously speak. The approach is evaluated on the well-known AMI dataset with approximately 100 hours of realistic meeting recordings and shows an average diarization error rate of 21% – 25%.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.