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Vassileios Balntas, Edgar Riba, Daniel Ponsa, & Krystian Mikolajczyk. (2016). Learning local feature descriptors with triplets and shallow convolutional neural networks. In 27th British Machine Vision Conference.
Abstract: It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives.
We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets.
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Maria Salamo, Inmaculada Rodriguez, Maite Lopez, Anna Puig, Simone Balocco, & Mariona Taule. (2016). Recurso docente para la atención de la diversidad en el aula mediante la predicción de notas. ReVision.
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.
Keywords: Aprendizaje automatico; Sistema de prediccion de notas; Herramienta docente
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Simone Balocco, Maria Zuluaga, Guillaume Zahnd, Su-Lin Lee, & Stefanie Demirci. (2016). Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting. Elsevier.
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Jose Marone, Simone Balocco, Marc Bolaños, Jose Massa, & Petia Radeva. (2016). Learning the Lumen Border using a Convolutional Neural Networks classifier. In 19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshop.
Abstract: IntraVascular UltraSound (IVUS) is a technique allowing the diagnosis of coronary plaque. An accurate (semi-)automatic assessment of the luminal contours could speed up the diagnosis. In most of the approaches, the information on the vessel shape is obtained combining a supervised learning step with a local refinement algorithm. In this paper, we explore for the first time, the use of a Convolutional Neural Networks (CNN) architecture that on one hand is able to extract the optimal image features and at the same time can serve as a supervised classifier to detect the lumen border in IVUS images. The main limitation of CNN, relies on the fact that this technique requires a large amount of training data due to the huge amount of parameters that it has. To
solve this issue, we introduce a patch classification approach to generate an extended training-set from a few annotated images. An accuracy of 93% and F-score of 71% was obtained with this technique, even when it was applied to challenging frames containig calcified plaques, stents and catheter shadows.
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Dena Bazazian, Raul Gomez, Anguelos Nicolaou, Lluis Gomez, Dimosthenis Karatzas, & Andrew Bagdanov. (2016). Improving Text Proposals for Scene Images with Fully Convolutional Networks. In 23rd International Conference on Pattern Recognition Workshops.
Abstract: Text Proposals have emerged as a class-dependent version of object proposals – efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text
recognition. In this paper we propose an improvement over the original Text Proposals algorithm of [1], combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art.
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Baiyu Chen, Sergio Escalera, Isabelle Guyon, Victor Ponce, N. Shah, & Marc Oliu. (2016). Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits. In 14th European Conference on Computer Vision Workshops.
Abstract: We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly dicult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p = N (N-1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is a ordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge.
Keywords: Calibration of labels; Label bias; Ordinal labeling; Variance Models; Bradley-Terry-Luce model; Continuous labels; Regression; Personality traits; Crowd-sourced labels
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Petia Radeva. (2016). Can Deep Learning and Egocentric Vision for Visual Lifelogging Help Us Eat Better? In 19th International Conference of the Catalan Association for Artificial Intelligence (Vol. 4).
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Alvaro Peris, Marc Bolaños, Petia Radeva, & Francisco Casacuberta. (2016). Video Description Using Bidirectional Recurrent Neural Networks. In 25th International Conference on Artificial Neural Networks (Vol. 2, pp. 3–11).
Abstract: Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames.
Keywords: Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks
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Fatemeh Noroozi, Marina Marjanovic, Angelina Njegus, Sergio Escalera, & Gholamreza Anbarjafari. (2016). Fusion of Classifier Predictions for Audio-Visual Emotion Recognition. In 23rd International Conference on Pattern Recognition Workshops.
Abstract: In this paper is presented a novel multimodal emotion recognition system which is based on the analysis of audio and visual cues. MFCC-based features are extracted from the audio channel and facial landmark geometric relations are
computed from visual data. Both sets of features are learnt separately using state-of-the-art classifiers. In addition, we summarise each emotion video into a reduced set of key-frames, which are learnt in order to visually discriminate emotions by means of a Convolutional Neural Network. Finally, confidence
outputs of all classifiers from all modalities are used to define a new feature space to be learnt for final emotion prediction, in a late fusion/stacking fashion. The conducted experiments on eNTERFACE’05 database show significant performance improvements of our proposed system in comparison to state-of-the-art approaches.
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Pejman Rasti, Tonis Uiboupin, Sergio Escalera, & Gholamreza Anbarjafari. (2016). Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring. In 9th Conference on Articulated Motion and Deformable Objects.
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Mark Philip Philipsen, Anders Jorgensen, Thomas B. Moeslund, & Sergio Escalera. (2016). RGB-D Segmentation of Poultry Entrails. In 9th Conference on Articulated Motion and Deformable Objects.
Abstract: Best commercial paper award.
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Antonio Esteban Lansaque, Carles Sanchez, Agnes Borras, Marta Diez-Ferrer, Antoni Rosell, & Debora Gil. (2016). Stable Airway Center Tracking for Bronchoscopic Navigation. In 28th Conference of the international Society for Medical Innovation and Technology.
Abstract: Bronchoscopists use X‐ray fluoroscopy to guide bronchoscopes to the lesion to be biopsied without any kind of incisions. Reducing exposure to X‐ray is important for both patients and doctors but alternatives like electromagnetic navigation require specific equipment and increase the cost of the clinical procedure. We propose a guiding system based on the extraction of airway centers from intra‐operative videos. Such anatomical landmarks could be
matched to the airway centerline extracted from a pre‐planned CT to indicate the best path to the lesion. We present an extraction of lumen centers
from intra‐operative videos based on tracking of maximal stable regions of energy maps.
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Sergio Escalera, Jordi Gonzalez, Xavier Baro, Fernando Alonso, & Martha Mackay. (2016). Care Respite: a remote monitoring eHealth system for improving ambient assisted living. In Human Motion Analysis for Healthcare Applications.
Abstract: Advances in technology that capture human motion have been quite remarkable during the last five years. New sensors have been developed, such as the Microsoft Kinect, Asus Xtion Pro live, PrimeSense Carmine and Leap Motion. Their main advantages are their non-intrusive nature, low cost and widely available support for developers offered by large corporations or Open Communities. Although they were originally developed for computer games, they have inspired numerous healthcare related ideas and projects in areas such as Medical Disorder Diagnosis, Assisted Living, Rehabilitation and Surgery.
In Assisted Living, human motion analysis allows continuous monitoring of elderly and vulnerable people and their activities to potentially detect life-threatening events such as falls. Human motion analysis in rehabilitation provides the opportunity for motivating patients through gamification, evaluating prescribed programmes of exercises and assessing patients’ progress. In operating theatres, surgeons may use a gesture-based interface to access medical information or control a tele-surgery system. Human motion analysis may also be used to diagnose a range of mental and physical diseases and conditions.
This event will discuss recent advances in human motion sensing and provide an application to healthcare for networking and exploring potential synergies and collaborations.
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Jose Ramirez Moreno, Juan R Revilla, Miguel Reyes, & Sergio Escalera. (2016). Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal. In 4th Congreso WCPT-SAR.
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Fernando Alonso, Xavier Baro, Sergio Escalera, Jordi Gonzalez, Martha Mackay, & Anna Serrahima. (2016). CARE RESPITE: TAKING CARE OF THE CAREGIVERS, Theme 5 The Strategic use of Mobile and Digital Health and Care Solutions. In 16th International Conference for Integrated Care.
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