Aportación al estudio de la Santa Iglesia Catedral de Baeza (Jaén)

La Catedral de Baeza está emplazada en la parte S. de la ciudad -corazón del viejo recinto murado- y circuída de callejas que recuerdan tiempos renacentistas. Su fachada principal domina una plaza, triste y evocadora, en cuyo silencio duerme el alma del siglo XVI. Y aunque la nota de impresión que alcanza el visitante se contiene en las palabras pobreza y olvido, he creído de cierto interés divulgar aquellos datos de su historia que pude lograr, buscando en los legajos de su archivo, amén de aquellos otros que ya rezaban en libros generalmente antiguos -pocos fueron los que tuve a mi alcance-, cuyas noticias no siempre me han resultado exactas

Aerial obstacle detection with 3D mobile devices

In this paper, we present a novel approach for aerial obstacle detection (e.g. branches or awnings) using a 3D smartphone in the context of the visually impaired (VI) people assistance. This kind of obstacles are especially challenging because they cannot be detected by the walking stick or the guide dog. The algorithm captures the 3D data of the scene through stereo vision. To our knowledge, this is the first work that presents a technology able to obtain real 3D measures with smartphones in real time. The orientation sensors of the device (magnetometer and accelerometer) are used to approximate the walking direction of the user, in order to look for the obstacles only in such direction. The obtained 3D data are compressed and then linearized for detecting the potential obstacles. Potential obstacles are tracked in order to accumulate enough evidence to alert the user only when a real obstacle is found. In the experimental section, we show the results of the algorithm in several situations using real data and helped by VI users.J.M. Sáez and M.A. Lozano are supported by the University of Alicante research grant GRE10-21. F. Escolano is supported by the project TIN2012-32839 of the Spanish Government

Accurate and efficient 3D hand pose regression for robot hand teleoperation using a monocular RGB camera

In this paper, we present a novel deep learning-based architecture, which is under the scope of expert and intelligent systems, to perform accurate real-time tridimensional hand pose estimation using a single RGB frame as an input, so there is no need to use multiple cameras or points of view, or RGB-D devices. The proposed pipeline is composed of two convolutional neural network architectures. The first one is in charge of detecting the hand in the image. The second one is able to accurately infer the tridimensional position of the joints retrieving, thus, the full hand pose. To do this, we captured our own large-scale dataset composed of images of hands and the corresponding 3D joints annotations. The proposal achieved a 3D hand pose mean error of below 5 mm on both the proposed dataset and Stereo Hand Pose Tracking Benchmark, which is a public dataset. Our method also outperforms the state-of-the-art methods. We also demonstrate in this paper the application of the proposal to perform a robotic hand teleoperation with high success.This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with Feder funds. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/24

The mutual information between graphs

The estimation of mutual information between graphs has been an elusive problem until the formulation of graph matching in terms of manifold alignment. Then, graphs are mapped to multi-dimensional sets of points through structure preserving embeddings. Point-wise alignment algorithms can be exploited in this context to re-cast graph matching in terms of point matching. Methods based on bypass entropy estimation must be deployed to render the estimation of mutual information computationally tractable. In this paper the novel contribution is to show how manifold alignment can be combined with copula-based entropy estimators to efficiently estimate the mutual information between graphs. We compare the empirical copula with an Archimedean copula (the independent one) in terms of retrieval/recall after graph comparison. Our experiments show that mutual information built in both choices improves significantly state-of-the art divergences.Funding. F. Escolano, M.A. Lozano: Project TIN2012-32839 (Spanish Gov.). M. Curado: BES-2013-064482 (Spanish Gov.). E. R. Hancock: Royal Society Wolfson Research Merit Award