86,508 research outputs found
Segmentación semántica multiclase en la digitalización del patrimonio mueble utilizando técnicas de aprendizaje profundo
No full text[EN] Digitisation processes of movable heritage are becoming increasingly popular to document the artworks stored in our museums. An increasing number of strategies for the three-dimensional (3D) acquisition and modelling of these invaluable assets have been developed in the last few years, to efficiently respond to this documentation need and contribute to deepening the knowledge of the masterpieces investigated constantly by researchers operating in many fieldworks. Nowadays, one of the most effective solutions is represented by the development of image-based techniques, usually connected to a Structure-from-Motion (SfM) photogrammetric approach. However, while the acquisition of the images is relatively rapid, it is the processes connected to the data processing that are very time-consuming and require substantial manual involvement of the operator. The development of deep learning-based strategies can be an effective solution to enhance the level of automatism. In the case of the current research, which has been carried out in the framework of the digitisation of a collection of wooden maquettes stored in the ‘Museo Egizio di Torino’ using a photogrammetric approach, an automatic masking strategy using deep learning techniques is proposed, to increase the level of automatism and therefore, optimise the photogrammetric pipeline. Starting from a manually annotated dataset a neural network has been trained to automatically perform a semantic classification with the aim to isolate the maquettes from the background. The proposed methodology has allowed obtaining automatically segmented masks with a high degree of accuracy. The followed workflow is described (as regards acquisition strategies, dataset processing, and neural network training), and the accuracy of the results is evaluated and discussed. In addition, the possibility of performing a multiclass segmentation on the digital images to recognise different categories of objects in the images and define a semantic hierarchy is proposed to perform automatic classification of different elements in the acquired images.[ES] Los procesos de digitalización del patrimonio mueble son cada vez más populares para documentar las obras de arte almacenadas en nuestros museos. En los últimos años se han desarrollado un número creciente de estrategias de adquisición y modelado tridimensional (3D) de estos activos de valor incalculable, que responden de manera eficiente a esta necesidad de documentación y contribuyen a profundizar en el conocimiento de las obras maestras investigadas constantemente por investigadores que operan en muchos trabajos de campo. Hoy en día, una de las soluciones más efectivas está relacionada con el desarrollo de técnicas basadas en imágenes, generalmente conectadas a un enfoque fotogramétrico de estructura-y-movimiento (SfM). Sin embargo, si bien la adquisición de las imágenes es relativamente rápida, son los procesos relacionados con el procesamiento de los datos los que consumen mucho tiempo y requieren una participación manual sustancial del operador. El desarrollo de estrategias basadas en el aprendizaje profundo puede ser una solución eficaz para mejorar el nivel de automatismo. En el caso de la presente investigación, que se ha llevado a cabo en el marco de la digitalización de una colección de maquetas de madera almacenadas en el 'Museo Egizio di Torino' mediante un enfoque fotogramétrico, se propone una estrategia de enmascaramiento automático mediante técnicas de aprendizaje profundo, que incrementa el nivel de automatismo y por tanto optimiza el flujo fotogramétrico. A partir de un conjunto de datos anotados manualmente, se ha entrenado una red neuronal que realiza automáticamente una clasificación semántica con el objetivo de aislar las maquetas del fondo. La metodología propuesta ha permitido obtener más caras segmentadas automáticamente con alto grado de precisión. Se describe el flujo de trabajo seguido (en cuanto a estrategias de toma, procesamiento del conjuntos de datos y entrenamiento de las redes neuronales), y se evalúa y discute la precisión de los resultados. Además, se propone la posibilidad de realizar una segmentación multiclase sobre las imágenes digitales que permitan reconocer diferentes categorías de objetos en las imágenes y definir una jerarquía semántica que clasifique automáticamente diferentes elementos en la toma de las imágenes.The authors thank Volta® A.I. (and in particular Silvio Revelli) for the contribution to this work and for providing high-end hardware for neural network training.
In addition, they would like to thank Alessia Fassone of Museo Egizio di Torino and all the people involved in the B.A.C.K. TO T.H.E. F.U.T.U.RE. project (in particular, Fulvio Rinaudo, who coordinated the Geomatic team).
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ENHANCING AUTOMATION OF HERITAGE PROCESSES: GENERATION OF ARTIFICIAL TRAINING DATASETS FROM PHOTOGRAMMETRIC 3D MODELS
No full textNowadays, many efficient technologies have been developed with the aim of collecting digital images and other metric data, greatly optimising the acquisition procedures and techniques. However, processing this data can be onerous and time-consuming, and increasingly often, there is a need to develop new strategies to enhance the level of automation of these processes. Using artificial intelligence, and particularly Convolutional Neural Networks, it is possible to automate processing tasks such as classification and segmentation. However, a significant challenge is represented by the necessity of obtaining sufficient training data to properly train a deep learning model. These datasets are composed of a significant amount of data and need to be annotated, which sometimes represents an onerous and challenging task. Synthetic data can represent an effective solution to this problem, significantly reducing the time and effort required to manually create annotated datasets and can be particularly useful when studying objects characterised by specific features and high complexity, requiring tailored solutions and ad hoc training. The presented research explores the opportunity of using synthetic datasets – generated from photogrammetric 3D models – for deep-learning-based heritage digitisation applications. The use of synthetic data generated from textured 3D models derived from SfM photogrammetric processes is proposed, with the aim of enhancing automatic procedures in the framework of heritage processes
An unsupervised approach to the semantic description of the sound quality of violins
No full textIn this study we propose a set of semantic musical descriptors that can be used for describing the timbre of violins. The pro-posed semantic model follows a dimensional approach, which allows us to express the degree of intensity of each descrip-tor. A set of recordings of a number of violins (among them, Stradivari, Amati and Guarnieri instruments) were annotated with the descriptors through questionnaires. The recordings are processed with deep learning techniques, to learn salient features from the audio signal in an unsupervised fashion. In this study we propose an automatic annotation procedure based on a set of regression functions that model each seman-tic descriptor using the learned set of features. Index Terms — High-level music descriptor, violin, tim-bre, sound qualit
Training-based semantic descriptors modeling for violin quality sound characterization
No full textViolin makers and musicians describe the timbral qualities of violins using semantic terms coming from natural language. In this study we use regression techniques of machine intelligence and audio features to model in a training-based fashion a set of high-level (semantic) descriptors for the automatic annotation of musical instruments. The most relevant semantic descriptors are collected through interviews to violin makers. These descriptors are then correlated with objective features extracted from a set of violins from the historical and contemporary collections of the Museo del Violino and of the International School of Luthiery both in Cremona. As sound description can vary throughout a performance, our approach also enables the modelling of time-varying (evolutive) semantic annotation
Variations on the Author
No full text“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
[Newspaper Clipping: Author Claims Evidence of Second JFK Assassin #1]
No full textNewspaper article titled "Author Claims Evidence of Second JFK Assassin." The article states that author Richard J. Whalen concluded "that there is circumstantial evidence to support the theory of a second assassin in the shooting of President John F. Kennedy.
Also By The Same Author: AKTiveAuthor, a Citation Graph Approach to Name Disambiguation
No full textThe desire for definitive data and the semantic web drive for inference over heterogeneous data sources requires co-reference resolution to be performed on those data. In particular, name disambiguation is required to allow accurate publication lists, citation counts and impact measures to be determined. This paper describes a graph-based approach to author disambiguation on large-scale citation networks. Using self-citation, co-authorship and document source analyses, AKTiveAuthor clusters papers, achieving precision of 0.997 and recall of 0.818 over a test group of eight surname clusters
John F. Kennedy telegram to Roosevelt
No full textJersey Homesteads (later the Borough of Roosevelt) was established in the 1930s as an agro-industrial cooperative community. It was established specifically for urban Jewish garment workers, many of whom had emigrated from Europe. President John F. Kennedy sent a telegram to the citizens of Roosevelt, New Jersey, apologizing for not being able to attend the memorial dedication in honor of former President Franklin Delano Roosevelt. (Jersey Homesteads became Roosevelt in 1945 in honor of the president.) President Kennedy expressed his gratitude to the people of Roosevelt for constructing the memorial, and commented that it will serve as a constant reminder of Roosevelt's good works
Logarithmic variance profiles and the corresponding f-1 spectra of temperature fluctuations in turbulent Rayleigh-Bénard convection
No full textWe report experimental results for the temperature variance 2(z) and the corresponding frequency spectra P(f) in turbulent Rayleigh-Bénard convection (RBC) in a cylindrical sample of aspect ratioT= D/L = 1:00 (D = 1:12 m is the diameter and L = 1:12 m the height). The measurements were conducted in the Rayleigh-number range 1011 < Ra < 1:35 1014 and Pr ' 0:8. For Ra = 1:35x1014, 2(z) could be described well by a logarithmic dependence on the vertical position z in a range of z 1 < z < z 2 with z 1 ' 70 and z 2 = 0:1L. Here L=(2Nu) is the thickness of a thin thermal sublayer adjacent to the horizontal plate where the heat flux (denoted by the Nusselt number Nu) is carried mostly by thermal diffusion. In the log layer, we found that the temperature spectra had a significant frequency range over which P(f) f with close to 1. As Ra decreased, increased so that the log layer became thinner. At Ra = 2:05 1011, z 2 < z 1 and therefore there was no range for a log layer. Correspondingly, the temperature spectrum near the horizontal plate did not have the f1 scaling form either
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