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Ferran Poveda, Jaume Garcia, Enric Marti, & Debora Gil. (2010). Validation of the myocardial architecture in DT-MRI tractography. In Medical Image Computing in Catalunya: Graduate Student Workshop (pp. 29–30). Girona (Spain).
Abstract: Deep understanding of myocardial structure may help to link form and funcion of the heart unraveling crucial knowledge for medical and surgical clinical procedures and studies. In this work we introduce two visualization techniques based on DT-MRI streamlining able to decipher interesting properties of the architectural organization of the heart.
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Partha Pratim Roy, Umapada Pal, & Josep Llados. (2010). Seal Object Detection in Document Images using GHT of Local Component Shapes. In 10th ACM Symposium On Applied Computing (23–27).
Abstract: Due to noise, overlapped text/signature and multi-oriented nature, seal (stamp) object detection involves a difficult challenge. This paper deals with automatic detection of seal from documents with cluttered background. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors (distance and angular position) computed from recognition result of individual connected components (characters). Recognition of multi-scale and multi-oriented component is done using Support Vector Machine classifier. Generalized Hough Transform (GHT) is used to detect the seal and a voting is casted for finding possible location of the seal object in a document based on these spatial feature descriptor of components pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal object in a document. Experimental results show that, the method is efficient to locate seal instance of arbitrary shape and orientation in documents.
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Antonio Clavelli, Dimosthenis Karatzas, & Josep Llados. (2010). A framework for the assessment of text extraction algorithms on complex colour images. In 9th IAPR International Workshop on Document Analysis Systems (19–26).
Abstract: The availability of open, ground-truthed datasets and clear performance metrics is a crucial factor in the development of an application domain. The domain of colour text image analysis (real scenes, Web and spam images, scanned colour documents) has traditionally suffered from a lack of a comprehensive performance evaluation framework. Such a framework is extremely difficult to specify, and corresponding pixel-level accurate information tedious to define. In this paper we discuss the challenges and technical issues associated with developing such a framework. Then, we describe a complete framework for the evaluation of text extraction methods at multiple levels, provide a detailed ground-truth specification and present a case study on how this framework can be used in a real-life situation.
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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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Carolina Malagelada, F.De Lorio, Fernando Azpiroz, Santiago Segui, Petia Radeva, Anna Accarino, et al. (2010). Intestinal Dysmotility in Patients with Functional Intestinal Disorders Demonstrated by Computer Vision Analysis of Capsule Endoscopy Images. In 18th United European Gastroenterology Week (Vol. 56, pp. A19–20).
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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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Thierry Brouard, A. Delaplace, Muhammad Muzzamil Luqman, H. Cardot, & Jean-Yves Ramel. (2010). Design of Evolutionary Methods Applied to the Learning of Bayesian Nerwork Structures. In Ahmed Rebai (Ed.), Bayesian Network (pp. 13–37). Sciyo.
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Sergio Escalera, Oriol Pujol, Petia Radeva, Jordi Vitria, & Maria Teresa Anguera. (2010). Automatic Detection of Dominance and Expected Interest. EURASIPJ - EURASIP Journal on Advances in Signal Processing, , 12.
Abstract: Article ID 491819
Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems.
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Ariel Amato, Mikhail Mozerov, Xavier Roca, & Jordi Gonzalez. (2010). Robust Real-Time Background Subtraction Based on Local Neighborhood Patterns. EURASIPJ - EURASIP Journal on Advances in Signal Processing, , 7.
Abstract: Article ID 901205
This paper describes an efficient background subtraction technique for detecting moving objects. The proposed approach is able to overcome difficulties like illumination changes and moving shadows. Our method introduces two discriminative features based on angular and modular patterns, which are formed by similarity measurement between two sets of RGB color vectors: one belonging to the background image and the other to the current image. We show how these patterns are used to improve foreground detection in the presence of moving shadows and in the case when there are strong similarities in color between background and foreground pixels. Experimental results over a collection of public and own datasets of real image sequences demonstrate that the proposed technique achieves a superior performance compared with state-of-the-art methods. Furthermore, both the low computational and space complexities make the presented algorithm feasible for real-time applications.
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Michal Drozdzal, Laura Igual, Jordi Vitria, Petia Radeva, Carolina Malagelada, & Fernando Azpiroz. (2010). SIFT flow-based Sequences Alignment. In Medical Image Computing in Catalunya: Graduate Student Workshop (7–8).
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Santiago Segui, Laura Igual, & Jordi Vitria. (2010). Weighted Bagging for Graph based One-Class Classifiers. In 9th International Workshop on Multiple Classifier Systems (Vol. 5997, pp. 1–10). LNCS. Springer Berlin Heidelberg.
Abstract: Most conventional learning algorithms require both positive and negative training data for achieving accurate classification results. However, the problem of learning classifiers from only positive data arises in many applications where negative data are too costly, difficult to obtain, or not available at all. Minimum Spanning Tree Class Descriptor (MSTCD) was presented as a method that achieves better accuracies than other one-class classifiers in high dimensional data. However, the presence of outliers in the target class severely harms the performance of this classifier. In this paper we propose two bagging strategies for MSTCD that reduce the influence of outliers in training data. We show the improved performance on both real and artificially contaminated data.
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Jose Seabra, F. Javier Sanchez, Francesco Ciompi, & Petia Radeva. (2010). Ultrasonographic Plaque Characterization using a Rayleigh Mixture Model. In 7th IEEE International Symposium on Biomedical Imaging (1–4).
Abstract: From Nano to Macro
A correct modelling of tissue morphology is determinant for the identification of vulnerable plaques. This paper aims at describing the plaque composition by means of a Rayleigh Mixture Model applied to ultrasonic data. The effectiveness of using a mixture of distributions is established through synthetic and real ultrasonic data samples. Furthermore, the proposed mixture model is used in a plaque classification problem in Intravascular Ultrasound (IVUS) images of coronary plaques. A classifier tested on a set of 67 in-vitro plaques, yields an overall accuracy of 86% and sensitivity of 92%, 94% and 82%, for fibrotic, calcified and lipidic tissues, respectively. These results strongly suggest that different plaques types can be distinguished by means of the coefficients and Rayleigh parameters of the mixture distribution.
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Herve Locteau, Sebastien Mace, Ernest Valveny, & Salvatore Tabbone. (2010). Extraction des pieces de un plan de habitation. In Colloque Internacional Francophone de l´Ecrit et le Document (1–12).
Abstract: In this article, a method to extract the rooms of an architectural floor plan image is described. We first present a line detection algorithm to extract long lines in the image. Those lines are analyzed to identify the existing walls. From this point, room extraction can be seen as a classical segmentation task for which each region corresponds to a room. The chosen resolution strategy consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines can also be rough. Thus, we take advantage of knowledge associated to architectural floor plans in order to obtain mainly rectangular rooms. Preliminary tests on a set of real documents show promising results.
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Xavier Otazu, C. Alejandro Parraga, & Maria Vanrell. (2010). Towards a unified chromatic inducction model. VSS - Journal of Vision, 10(12:5), 1–24.
Abstract: In a previous work (X. Otazu, M. Vanrell, & C. A. Párraga, 2008b), we showed how several brightness induction effects can be predicted using a simple multiresolution wavelet model (BIWaM). Here we present a new model for chromatic induction processes (termed Chromatic Induction Wavelet Model or CIWaM), which is also implemented on a multiresolution framework and based on similar assumptions related to the spatial frequency and the contrast surround energy of the stimulus. The CIWaM can be interpreted as a very simple extension of the BIWaM to the chromatic channels, which in our case are defined in the MacLeod-Boynton (lsY) color space. This new model allows us to unify both chromatic assimilation and chromatic contrast effects in a single mathematical formulation. The predictions of the CIWaM were tested by means of several color and brightness induction experiments, which showed an acceptable agreement between model predictions and psychophysical data.
Keywords: Visual system; Color induction; Wavelet transform
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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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