|
|
Oriol Pujol, Petia Radeva, & Jordi Vitria. (2005). Traffic sign recognition using an adaptive boosting multiclass framework.
|
|
|
|
Oriol Pujol, Petia Radeva, & Jordi Vitria. (2006). Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6): 1007–1012.
|
|
|
|
Oriol Pujol, Petia Radeva, J. Mauri, & E Fernandez-Nofrerias. (2002). Automatic segmentation of lumen in Intravascular Ultrasound Images: An evaluation of texture feature extractors..
|
|
|
|
Oriol Pujol, & Petia Radeva. (2002). Lumen Detection in Ivus Image Using Snakes in a Statical Framework..
|
|
|
|
Oriol Pujol, & Petia Radeva. (2003). Texture Segmentation by Statistic Deformable Models. International Journal of Image and Graphics (IJIG).
|
|
|
|
Oriol Pujol, & Petia Radeva. (2004). Texture Segmentation by Statistical Deformable Models. IJIG - International Journal of Image and Graphics, 433–452.
Abstract: Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model.
Keywords: Texture segmentation, parametric active contours, statistic snakes
|
|
|
|
Oriol Pujol, & Petia Radeva. (2005). Solving Particularization with Supervised Clustering Competition Scheme. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 11–18.
|
|
|
|
Oriol Pujol, & Petia Radeva. (2005). On the assessment of texture descriptors in intravascular ultrasound images: A boosting approach to a feasible plaque classification. In Plaque Imaging: Pixel to Molecular Level, IOS Press, J. Suri et al. (Eds.), 113: 276–299, ISBN: 1–58603–516–9.
|
|
|
|
Oriol Pujol, & Petia Radeva. (2006). Optimal extension of Error Correcting Output Codes.
|
|
|
|
Oriol Pujol, O. Rodriguez-Leor, J. Mauri, E. Fernandez, V. Valle, & Petia Radeva. (2004). Automatic segmentation and characterization of IVUS images by texture analysis.
|
|
|
|
Oriol Pujol, Misael Rosales, Petia Radeva, & E Fernandez-Nofrerias. (2003). Intravascular Ultrasound Images Vessel Characterization using AdaBoost.
|
|
|
|
Oriol Pujol, Eloi Puertas, & Carlo Gatta. (2009). Multi-scale Stacked Sequential Learning. In 8th International Workshop of Multiple Classifier Systems (Vol. 5519, 262–271). Springer Berlin Heidelberg.
Abstract: One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions.
|
|
|
|
Oriol Pujol, Debora Gil, & Petia Radeva. (2005). Fundamentals of Stop and Go active models. Image and Vision Computing, 23(8), 681–691.
Abstract: An efficient snake formulation should conform to the idea of picking the smoothest curve among all the shapes approximating an object of interest. In current geodesic snakes, the regularizing curvature also affects the convergence stage, hindering the latter at concave regions. In the present work, we make use of characteristic functions to define a novel geodesic formulation that decouples regularity and convergence. This term decoupling endows the snake with higher adaptability to non-convex shapes. Convergence is ensured by splitting the definition of the external force into an attractive vector field and a repulsive one. In our paper, we propose to use likelihood maps as approximation of characteristic functions of object appearance. The better efficiency and accuracy of our decoupled scheme are illustrated in the particular case of feature space-based segmentation.
Keywords: Deformable models; Geodesic snakes; Region-based segmentation
|
|
|
|
Oriol Pujol, David Rotger, Petia Radeva, O. Rodriguez, & J. Mauri. (2003). Near Real Time Plaque Segmentation of IVUS.
|
|
|
|
Oriol Pujol, & David Masip. (2009). Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6), 1140–1146.
Abstract: This article introduces a novel binary discriminative learning technique based on the approximation of the non-linear decision boundary by a piece-wise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points – points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and non-linear behavior is obtained. The simplicity of the method allows its extension to cope with some of nowadays machine learning challenges, such as online learning, large scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database. Finally, we apply our technique in online and large scale scenarios, and in six real life computer vision and pattern recognition problems: gender recognition, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease severity detection, clef classification and action recognition using a 3D accelerometer data. The results are promising and this paper opens a line of research that deserves further attention
|
|
