Abstract: Desde la implantación del Espacio Europeo de Educación Superior (EEES) en los diferentes grados, se ha puesto de manifiesto la necesidad de utilizar diversos mecanismos que permitan tratar la diversidad en el aula, evaluando automáticamente y proporcionando una retroalimentación rápida tanto al alumnado como al profesorado sobre la evolución de los alumnos en una asignatura. En este artículo se presenta la evaluación de la exactitud en las predicciones de GRADEFORESEER, un recurso docente para la predicción de notas basado en técnicas de aprendizaje automático que permite evaluar la evolución del alumnado y estimar su nota final al terminar el curso. Este recurso se ha complementado con una interfaz de usuario para el profesorado que puede ser usada en diferentes plataformas software (sistemas operativos) y en cualquier asignatura de un grado en la que se utilice evaluación continuada. Además de la descripción del recurso, este artículo presenta los resultados obtenidos al aplicar el sistema de predicción en cuatro asignaturas de disciplinas distintas: Programación I (PI), Diseño de Software (DSW) del grado de Ingeniería Informática, Tecnologías de la Información y la Comunicación (TIC) del grado de Lingüística y la asignatura Fundamentos de Tecnología (FDT) del grado de Información y Documentación, todas ellas impartidas en la Universidad de Barcelona.
La capacidad predictiva se ha evaluado de forma binaria (aprueba o no) y según un criterio de rango (suspenso, aprobado, notable o sobresaliente), obteniendo mejores predicciones en los resultados evaluados de forma binaria.

Abstract: This paper presents a computational model to recover the most likely interpretation
of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth.
Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling.

Abstract: Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution.

Abstract: Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size.

Abstract: Object detection with deep neural networks is often performed by passing a few
thousand candidate bounding boxes through a deep neural network for each image.
These bounding boxes are highly correlated since they originate from the same
image. In this paper we investigate how to exploit feature occurrence at the image scale to prune the neural network which is subsequently applied to all bounding boxes. We show that removing units which have near-zero activation in the image allows us to significantly reduce the number of parameters in the network. Results on the PASCAL 2007 Object Detection Challenge demonstrate that up to 40% of units in some fully-connected layers can be entirely eliminated with little change in the detection result.

Abstract: In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework.In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements.

Abstract: Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams.

Abstract: Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective.
Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with
high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art
methods in unconstrained scenarios.