Sergio Escalera, Oriol Pujol, & Petia Radeva. (2010). Re-coding ECOCs without retraining. PRL - Pattern Recognition Letters, 31(7), 555–562.
Abstract: A standard way to deal with multi-class categorization problems is by the combination of binary classifiers in a pairwise voting procedure. Recently, this classical approach has been formalized in the Error-Correcting Output Codes (ECOC) framework. In the ECOC framework, the one-versus-one coding demonstrates to achieve higher performance than the rest of coding designs. The binary problems that we train in the one-versus-one strategy are significantly smaller than in the rest of designs, and usually easier to be learnt, taking into account the smaller overlapping between classes. However, a high percentage of the positions coded by zero of the coding matrix, which implies a high sparseness degree, does not codify meta-class membership information. In this paper, we show that using the training data we can redefine without re-training, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information helps the system to increase its generalization capability. Moreover, the new re-coding strategy is generalized to be applied over any binary code. The results over several UCI Machine Learning repository data sets and two real multi-class problems show that performance improvements can be obtained re-coding the classical one-versus-one and Sparse random designs compared to different state-of-the-art ECOC configurations.
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Antonio Hernandez, Miguel Reyes, Sergio Escalera, & Petia Radeva. (2010). Spatio-Temporal GrabCut human segmentation for face and pose recovery. In IEEE International Workshop on Analysis and Modeling of Faces and Gestures (33–40).
Abstract: In this paper, we present a full-automatic Spatio-Temporal GrabCut human segmentation methodology. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model for seed initialization. Spatial information is included by means of Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, human segmentation is combined with Shape and Active Appearance Models to perform full face and pose recovery. Results over public data sets as well as proper human action base show a robust segmentation and recovery of both face and pose using the presented methodology.
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Francesco Ciompi, Oriol Pujol, & Petia Radeva. (2010). A meta-learning approach to Conditional Random Fields using Error-Correcting Output Codes. In 20th International Conference on Pattern Recognition (710–713).
Abstract: We present a meta-learning framework for the design of potential functions for Conditional Random Fields. The design of both node potential and edge potential is formulated as a classification problem where margin classifiers are used. The set of state transitions for the edge potential is treated as a set of different classes, thus defining a multi-class learning problem. The Error-Correcting Output Codes (ECOC) technique is used to deal with the multi-class problem. Furthermore, the point defined by the combination of margin classifiers in the ECOC space is interpreted in a probabilistic manner, and the obtained distance values are then converted into potential values. The proposed model exhibits very promising results when applied to two real detection problems.
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Francesco Ciompi, Oriol Pujol, E Fernandez-Nofrerias, J. Mauri, & Petia Radeva. (2010). Conditional Random Fields for image segmentation in Intravascular Ultrasound. In Medical Image Computing in Catalunya: Graduate Student Workshop (13–14).
Abstract: We present a Conditional Random Fields based approach for segmenting Intravascular Ultrasond (IVUS) images. The presented method uses a contextual discriminative graphical model to deal with the presence of distorsions and artifacts in IVUS images, that turns the segmentation of interesting regions into a difficult task. An accurate lumen segmentation on IVUS longitudinal images is achieved.
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Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2010). Embedding Random Projections in Regularized Gradient Boosting Machines. In Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (44–53).
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Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2010). Classyfing Agitation in Sedated ICU Patients. In Medical Image Computing in Catalunya: Graduate Student Workshop (19–20).
Abstract: Agitation is a serious problem in sedated intensive care unit (ICU) patients. In this work, standard machine learning techniques working on wearable accelerometer data have been used to classifying agitation levels achieving very good classification performances.
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Antonio Hernandez, Carlo Gatta, Petia Radeva, Laura Igual, R. Letaz, & Sergio Escalera. (2010). Automatic Vessel Segmentation For Angiography and CT Registration. In Medical Image Computing in Catalunya: Graduate Student Workshop (1–2).
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Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2012). Personalization and User Verification in Wearable Systems using Biometric Walking Patterns. PUC - Personal and Ubiquitous Computing, 16(5), 563–580.
Abstract: In this article, a novel technique for user’s authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a ‘wild’ scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies.
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Jose Seabra, Francesco Ciompi, Oriol Pujol, J. Mauri, Petia Radeva, & Joao Sanchez. (2011). Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound. TBME - IEEE Transactions on Biomedical Engineering, 58(5), 1314–1324.
Abstract: Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology.
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Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2011). Human Activity Recognition from Accelerometer Data using a Wearable Device. In J. Vitria, J. M. R. Sanches, & M. Hernández (Eds.), 5th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 6669, pp. 289–296). LNCS. Springer Berlin Heidelberg.
Abstract: Activity Recognition is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Recently, recognizing everyday life activities becomes one of the challenges for pervasive computing. In our work, we developed a novel wearable system easy to use and comfortable to bring. Our wearable system is based on a new set of 20 computationally efficient features and the Random Forest classifier. We obtain very encouraging results with classification accuracy of human activities recognition of up to 94%.
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Francesco Ciompi, Oriol Pujol, Carlo Gatta, Xavier Carrillo, J. Mauri, & Petia Radeva. (2011). A Holistic Approach for the Detection of Media-Adventitia Border in IVUS. In 14th International Conference on Medical Image Computing and Computer Assisted Intervention (Vol. 6893, pp. 401–408). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we present a methodology for the automatic detection of media-adventitia border (MAb) in Intravascular Ultrasound. A robust computation of the MAb is achieved through a holistic approach where the position of the MAb with respect to other tissues of the vessel is used. A learned quality measure assures that the resulting MAb is optimal with respect to all other tissues. The mean distance error computed through a set of 140 images is 0.2164 (±0.1326) mm.
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Marina Alberti, Carlo Gatta, Simone Balocco, Francesco Ciompi, Oriol Pujol, Joana Silva, et al. (2011). Automatic Branching Detection in IVUS Sequences. In Jordi Vitria, Joao Miguel Raposo, & Mario Hernandez (Eds.), 5th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 6669, pp. 126–133). LNCS. Berlin: Springer Berlin Heidelberg.
Abstract: Atherosclerosis is a vascular pathology affecting the arterial walls, generally located in specific vessel sites, such as bifurcations. In this paper, for the first time, a fully automatic approach for the detection of bifurcations in IVUS pullback sequences is presented. The method identifies the frames and the angular sectors in which a bifurcation is visible. This goal is achieved by applying a classifier to a set of textural features extracted from each image of an IVUS pullback. A comparison between two state-of-the-art classifiers is performed, AdaBoost and Random Forest. A cross-validation scheme is applied in order to evaluate the performances of the approaches. The obtained results are encouraging, showing a sensitivity of 75% and an accuracy of 94% by using the AdaBoost algorithm.
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Simone Balocco, Carlo Gatta, Francesco Ciompi, Oriol Pujol, Xavier Carrillo, J. Mauri, et al. (2011). Combining Growcut and Temporal Correlation for IVUS Lumen Segmentation. In Jordi Vitria, Joao Miguel Raposo, & Mario Hernandez (Eds.), 5th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 6669, pp. 556–563). LNCS. Berlin: Springer Berlin Heidelberg.
Abstract: The assessment of arterial luminal area, performed by IVUS analysis, is a clinical index used to evaluate the degree of coronary artery disease. In this paper we propose a novel approach to automatically segment the vessel lumen, which combines model-based temporal information extracted from successive frames of the sequence, with spatial classification using the Growcut algorithm. The performance of the method is evaluated by an in vivo experiment on 300 IVUS frames. The automatic and manual segmentation performances in general vessel and stent frames are comparable. The average segmentation error in vessel, stent and bifurcation frames are 0.17±0.08 mm, 0.18±0.07 mm and 0.31±0.12 mm respectively.
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Sergio Escalera, David M.J. Tax, Oriol Pujol, Petia Radeva, & Robert P.W. Duin. (2011). Multi-Class Classification in Image Analysis Via Error-Correcting Output Codes. In H. Kawasnicka, & L.Jain (Eds.), Innovations in Intelligent Image Analysis (Vol. 339, pp. 7–29). Berlin: Springer Berlin Heidelberg.
Abstract: A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem.A classification decision is obtained by assigning the label of the class with the closest code. In this paper, we overview the state-of-the-art on ECOC designs and test them in real applications. Results on different multi-class data sets show the benefits of using the ensemble of classifiers when categorizing objects in images.
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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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