Nataliya Shapovalova, Wenjuan Gong, Marco Pedersoli, Xavier Roca, & Jordi Gonzalez. (2011). On Importance of Interactions and Context in Human Action Recognition. In and M. Hernandez J. M. S. J. Vitria (Ed.), 5th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 6669, pp. 58–66). LNCS. Springer Berlin Heidelberg.
Abstract: This paper is focused on the automatic recognition of human events in static images. Popular techniques use knowledge of the human pose for inferring the action, and the most recent approaches tend to combine pose information with either knowledge of the scene or of the objects with which the human interacts. Our approach makes a step forward in this direction by combining the human pose with the scene in which the human is placed, together with the spatial relationships between humans and objects. Based on standard, simple descriptors like HOG and SIFT, recognition performance is enhanced when these three types of knowledge are taken into account. Results obtained in the PASCAL 2010 Action Recognition Dataset demonstrate that our technique reaches state-of-the-art results using simple descriptors and classifiers.
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Carlo Gatta, Simone Balocco, Victoria Martin Yuste, Ruben Leta, & Petia Radeva. (2011). Non-rigid Multi-modal Registration of Coronary Arteries Using SIFTflow. In Jordi Vitria, Joao Miguel Sanches, & Mario Hernandez (Eds.), 5th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 6669, pp. 159–166). LNCS. Berlin: Springer Berlin Heidelberg.
Abstract: The fusion of clinically relevant information coming from different image modalities is an important topic in medical imaging. In particular, different cardiac imaging modalities provides complementary information for the physician: Computer Tomography Angiography (CTA) provides reliable pre-operative information on arteries geometry, even in the presence of chronic total occlusions, while X-Ray Angiography (XRA) allows intra-operative high resolution projections of a specific artery. The non-rigid registration of arteries between these two modalities is a difficult task. In this paper we propose the use of SIFTflow, in registering CTA and XRA images. At the best of our knowledge, this paper proposed SIFTflow as a XRay-CTA registration method for the first time in the literature. To highlight the arteries, so to guide the registration process, the well known Vesselness method has been employed. Results confirm that, to the aim of registration, the arteries must be highlighted and background objects removed as much as possible. Moreover, the comparison with the well known Free Form Deformation technique, suggests that SIFTflow has a great potential in the registration of multi-modal medical images.
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Xavier Perez Sala, Cecilio Angulo, & Sergio Escalera. (2011). Biologically Inspired Turn Control in Robot Navigation. In 14th Congrès Català en Intel·ligencia Artificial (pp. 187–196).
Abstract: An exportable and robust system for turn control using only camera images is proposed for path execution in robot navigation. Robot motion information is extracted in the form of optical flow from SURF robust descriptors of consecutive frames in the image sequence. This information is used to compute the instantaneous rotation angle. Finally, control loop is closed correcting robot displacements when it is requested for a turn command. The proposed system has been successfully tested on the four-legged Sony Aibo robot.
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Antonio Hernandez, Carlo Gatta, Laura Igual, Sergio Escalera, & Petia Radeva. (2011). Automatic Angiography Segmentation Based on Improved Graph-cut. In Jornada TIC Salut Girona.
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Laura Igual, Antonio Hernandez, Sergio Escalera, Miguel Reyes, Josep Moya, Joan Carles Soliva, et al. (2011). Automatic Techniques for Studying Attention-Deficit/Hyperactivity Disorder. In Jornada TIC Salut Girona.
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David Vazquez, Antonio Lopez, Daniel Ponsa, & Javier Marin. (2011). Cool world: domain adaptation of virtual and real worlds for human detection using active learning. In NIPS Domain Adaptation Workshop: Theory and Application. Granada, Spain.
Abstract: Image based human detection is of paramount interest for different applications. The most promising human detectors rely on discriminatively learnt classifiers, i.e., trained with labelled samples. However, labelling is a manual intensive task, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, in Marin et al. we have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera and the same type of scenario. Accordingly, in Vazquez et al. we cast the problem as one of supervised domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we use an active learning technique. Thus, ultimately our human model is learnt by the combination of virtual- and real-world labelled samples which, to the best of our knowledge, was not done before. Here, we term such combined space cool world. In this extended abstract we summarize our proposal, and include quantitative results from Vazquez et al. showing its validity.
Keywords: Pedestrian Detection; Virtual; Domain Adaptation; Active Learning
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Mario Rojas, David Masip, & Jordi Vitria. (2011). Predicting Dominance Judgements Automatically: A Machine Learning Approach. In IEEE International Workshop on Social Behavior Analysis (pp. 939–944).
Abstract: The amount of multimodal devices that surround us is growing everyday. In this context, human interaction and communication have become a focus of attention and a hot topic of research. A crucial element in human relations is the evaluation of individuals with respect to facial traits, what is called a first impression. Studies based on appearance have suggested that personality can be expressed by appearance and the observer may use such information to form judgments. In the context of rapid facial evaluation, certain personality traits seem to have a more pronounced effect on the relations and perceptions inside groups. The perception of dominance has been shown to be an active part of social roles at different stages of life, and even play a part in mate selection. The aim of this paper is to study to what extent this information is learnable from the point of view of computer science. Specifically we intend to determine if judgments of dominance can be learned by machine learning techniques. We implement two different descriptors in order to assess this. The first is the histogram of oriented gradients (HOG), and the second is a probabilistic appearance descriptor based on the frequencies of grouped binary tests. State of the art classification rules validate the performance of both descriptors, with respect to the prediction task. Experimental results show that machine learning techniques can predict judgments of dominance rather accurately (accuracies up to 90%) and that the HOG descriptor may characterize appropriately the information necessary for such task.
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Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2011). Approximate Convex Hulls Family for One-Class Cassification. In Carlo Sansone, Josef Kittler, & Fabio Roli (Eds.), 10th International Workshop on Multiple Classifier Systems (Vol. 6713, pp. 106–115). LNCS. Springer Berlin Heidelberg.
Abstract: In this work, a new method for one-class classification based on the Convex Hull geometric structure is proposed. The new method creates a family of convex hulls able to fit the geometrical shape of the training points. The increased computational cost due to the creation of the convex hull in multiple dimensions is circumvented using random projections. This provides an approximation of the original structure with multiple bi-dimensional views. In the projection planes, a mechanism for noisy points rejection has also been elaborated and evaluated. Results show that the approach performs considerably well with respect to the state the art in one-class classification.
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Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2011). User Verification From Walking Activity. First Steps Towards a Personal Verification System. In 1st International Conference on Pervasive and Embedded Computing and Communication Systems.
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Jon Almazan, Alicia Fornes, & Ernest Valveny. (2011). A Non-Rigid Feature Extraction Method for Shape Recognition. In 11th International Conference on Document Analysis and Recognition (pp. 987–991).
Abstract: This paper presents a methodology for shape recognition that focuses on dealing with the difficult problem of large deformations. The proposed methodology consists in a novel feature extraction technique, which uses a non-rigid representation adaptable to the shape. This technique employs a deformable grid based on the computation of geometrical centroids that follows a region partitioning algorithm. Then, a feature vector is extracted by computing pixel density measures around these geometrical centroids. The result is a shape descriptor that adapts its representation to the given shape and encodes the pixel density distribution. The validity of the method when dealing with large deformations has been experimentally shown over datasets composed of handwritten shapes. It has been applied to signature verification and shape recognition tasks demonstrating high accuracy and low computational cost.
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Marco Pedersoli, Andrea Vedaldi, & Jordi Gonzalez. (2011). A Coarse-to-fine Approach for fast Deformable Object Detection. In IEEE conference on Computer Vision and Pattern Recognition (pp. 1353–1360).
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Miguel Oliveira, Angel Sappa, & V.Santos. (2011). Unsupervised Local Color Correction for Coarsely Registered Images. In IEEE conference on Computer Vision and Pattern Recognition (pp. 201–208).
Abstract: The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time.
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Francesco Ciompi, Oriol Pujol, Simone Balocco, Xavier Carrillo, J. Mauri, & Petia Radeva. (2011). Automatic Key Frames Detection in Intravascular Ultrasound Sequences. In In MICCAI 2011 Workshop on Computing and Visualization for Intra Vascular Imaging.
Abstract: We present a method for the automatic detection of key frames in Intravascular Ultrasound (IVUS) sequences. The key frames are markers delimiting morphological changes along the vessel. The aim of defining key frames is two-fold: (1) they allow to summarize the content of the pullback into few representative frames; (2) they represent the basis for the automatic detection of clinical events in IVUS. The proposed approach achieved a compression ratio of 0.016 with respect to the original sequence and an average inter-frame distance of 61.76 frame, minimizing the number of missed clinical events.
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Miguel Reyes, Jose Ramirez Moreno, Juan R Revilla, Petia Radeva, & Sergio Escalera. (2011). ADiBAS: Sistema Multisensor de Adquisicion Automatica de Datos Corporales Objetivos, Robustos y Fiables para el Analisis de la Postura y el Movimiento. In 6th Congreso Iberoamericano de Tecnologia de Apoyo a la Discapacidad (pp. 939–944).
Abstract: El análisis de la postura y del rango de movimiento son fundamentales para conocer la optimización del gesto y mejorar, de este modo, el rendimiento y la detección de posibles lesiones. Esta cuantificación es especialmente interesante en deportistas o en pacientes que presentan alguna lesión neurológica o del sistema musculo-esquelético, ya que permite conocer el proceso evolutivo de estos pacientes, evaluar la eficacia de la terapia aplicada y proponer, en caso necesario, una modificación del protocolo de tratamiento.
En este trabajo presentamos un sistema automático que permite, mediante una tecnología no invasiva, la captación automática de marcadores LED situados sobre el paciente y su posterior análisis con el fin de mostrar al especialista datos objetivos que permitan un mejor soporte diagnóstico. También se describe un
sistema analítico de la postura corporal sin marcadores, donde su ejecución durante secuencias dinámicas aporta un alto grado de naturalidad al paciente a la hora de realizar los ejercicios funcionales.
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Antonio Hernandez, Carlo Gatta, Sergio Escalera, Laura Igual, Victoria Martin Yuste, & Petia Radeva. (2011). Accurate and Robust Fully-Automatic QCA: Method and Numerical Validation. In 14th International Conference on Medical Image Computing and Computer Assisted Intervention (Vol. 14, pp. 496–503). Springer.
Abstract: The Quantitative Coronary Angiography (QCA) is a methodology used to evaluate the arterial diseases and, in particular, the degree of stenosis. In this paper we propose AQCA, a fully automatic method for vessel segmentation based on graph cut theory. Vesselness, geodesic paths and a new multi-scale edgeness map are used to compute a globally optimal artery segmentation. We evaluate the method performance in a rigorous numerical way on two datasets. The method can detect an artery with precision 92.9 +/- 5% and sensitivity 94.2 +/- 6%. The average absolute distance error between detected and ground truth centerline is 1.13 +/- 0.11 pixels (about 0.27 +/- 0.025 mm) and the absolute relative error in the vessel caliber estimation is 2.93% with almost no bias. Moreover, the method can discriminate between arteries and catheter with an accuracy of 96.4%.
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