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. (2003). Texture Segmentation by Statistic Deformable Models. International Journal of Image and Graphics (IJIG).
|
|
Oriol Pujol, & Petia Radeva. (2002). Lumen Detection in Ivus Image Using Snakes in a Statical Framework..
|
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
|
|
Oriol Pujol. (2004). A semi-Supervised Statistical Framework and Generative Snakes for IVUS Analysis (Petia Radeva, Ed.). Ph.D. thesis, , .
|
|
Oriol Pujol. (1999). Model-based three dimensional interpolation of IVUS images.
|
|
F.X. Perez, F. Javier Sanchez, Xavier Binefa, Xavier Roca, Jordi Vitria, & Juan J. Villanueva. (1993). A mathematical morphology-based system for IC´s inspection and analysis. In Institute of Physics Conferences Series (Vol. 135, 381–384). Institute of Physics.
|
|
F. Pla, Petia Radeva, & Jordi Vitria. (2008). Non-parametric distance-based classification techniques and their applications. Pattern Analysis and Applications, Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications, 223–225.
|
|
F. Pla, Petia Radeva, & Jordi Vitria. (2006). Pattern Recognition: Progress, Directions and Applications.
|
|
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, & Jordi Vitria. (2005). Traffic sign recognition using an adaptive boosting multiclass framework.
|
|
Oriol Pujol, Petia Radeva, Jordi Vitria, & J. Mauri. (2004). Adaboost to Classify Plaque Appearance in IVUS Images.
|
|
Xose M. Pardo, Petia Radeva, & Juan J. Villanueva. (1999). Self-Training Statistic Snake for Image Segmentation and Tracking..
|
|
Oriol Pujol, David Rotger, Petia Radeva, O. Rodriguez, & J. Mauri. (2003). Near Real Time Plaque Segmentation of IVUS.
|
|
Oriol Pujol, Misael Rosales, Petia Radeva, & E Fernandez-Nofrerias. (2003). Intravascular Ultrasound Images Vessel Characterization using AdaBoost.
|
