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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2012). Three-Dimensional Design of Error Correcting Output Codes. In 8th International Conference on Machine Learning and Data Mining (pp. 29–).
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Miguel Oliveira, V.Santos, & Angel Sappa. (2012). Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition. In IEEE 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles.
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Fernando Barrera, Felipe Lumbreras, Cristhian Aguilera, & Angel Sappa. (2012). Planar-Based Multispectral Stereo. In 11th Quantitative InfraRed Thermography.
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Cristhian Aguilera, Fernando Barrera, Angel Sappa, & Ricardo Toledo. (2012). A Novel SIFT-Like-Based Approach for FIR-VS Images Registration. In 11th Quantitative InfraRed Thermography.
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Muhammad Muzzamil Luqman, Jean-Yves Ramel, & Josep Llados. (2012). Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 243–253). LNCS. Springer Berlin Heidelberg.
Abstract: Graphs are the most powerful, expressive and convenient data structures but there is a lack of efficient computational tools and algorithms for processing them. The embedding of graphs into numeric vector spaces permits them to access the state-of-the-art computational efficient statistical models and tools. In this paper we take forward our work on explicit graph embedding and present an improvement to our earlier proposed method, named “fuzzy multilevel graph embedding – FMGE”, through feature selection technique. FMGE achieves the embedding of attributed graphs into low dimensional vector spaces by performing a multilevel analysis of graphs and extracting a set of global, structural and elementary level features. Feature selection permits FMGE to select the subset of most discriminating features and to discard the confusing ones for underlying graph dataset. Experimental results for graph classification experimentation on IAM letter, GREC and fingerprint graph databases, show improvement in the performance of FMGE.
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Volkmar Frinken, Alicia Fornes, Josep Llados, & Jean-Marc Ogier. (2012). Bidirectional Language Model for Handwriting Recognition. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 611–619). LNCS. Springer Berlin Heidelberg.
Abstract: In order to improve the results of automatically recognized handwritten text, information about the language is commonly included in the recognition process. A common approach is to represent a text line as a sequence. It is processed in one direction and the language information via n-grams is directly included in the decoding. This approach, however, only uses context on one side to estimate a word’s probability. Therefore, we propose a bidirectional recognition in this paper, using distinct forward and a backward language models. By combining decoding hypotheses from both directions, we achieve a significant increase in recognition accuracy for the off-line writer independent handwriting recognition task. Both language models are of the same type and can be estimated on the same corpus. Hence, the increase in recognition accuracy comes without any additional need for training data or language modeling complexity.
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Klaus Broelemann, Anjan Dutta, Xiaoyi Jiang, & Josep Llados. (2012). Hierarchical graph representation for symbol spotting in graphical document images. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 529–538). LNCS. Springer Berlin Heidelberg.
Abstract: Symbol spotting can be defined as locating given query symbol in a large collection of graphical documents. In this paper we present a hierarchical graph representation for symbols. This representation allows graph matching methods to deal with low-level vectorization errors and, thus, to perform a robust symbol spotting. To show the potential of this approach, we conduct an experiment with the SESYD dataset.
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Jaume Gibert, Ernest Valveny, Horst Bunke, & Alicia Fornes. (2012). On the Correlation of Graph Edit Distance and L1 Distance in the Attribute Statistics Embedding Space. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 135–143). LNCS. Springer-Berlag, Berlin.
Abstract: Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected.
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Fadi Dornaika, A.Assoum, & Bogdan Raducanu. (2012). Automatic Dimensionality Estimation for Manifold Learning through Optimal Feature Selection. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 575–583). LNCS. Springer Berlin Heidelberg.
Abstract: A very important aspect in manifold learning is represented by automatic estimation of the intrinsic dimensionality. Unfortunately, this problem has received few attention in the literature of manifold learning. In this paper, we argue that feature selection paradigm can be used to the problem of automatic dimensionality estimation. Besides this, it also leads to improved recognition rates. Our approach for optimal feature selection is based on a Genetic Algorithm. As a case study for manifold learning, we have considered Laplacian Eigenmaps (LE) and Locally Linear Embedding (LLE). The effectiveness of the proposed framework was tested on the face recognition problem. Extensive experiments carried out on ORL, UMIST, Yale, and Extended Yale face data sets confirmed our hypothesis.
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Bogdan Raducanu, & Fadi Dornaika. (2012). Out-of-Sample Embedding by Sparse Representation. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop (Vol. 7626, pp. 336–344). Springer Berlin Heidelberg.
Abstract: A critical aspect of non-linear dimensionality reduction techniques is represented by the construction of the adjacency graph. The difficulty resides in finding the optimal parameters, a process which, in general, is heuristically driven. Recently, sparse representation has been proposed as a non-parametric solution to overcome this problem. In this paper, we demonstrate that this approach not only serves for the graph construction, but also represents an efficient and accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. Experimental results conducted on some challenging datasets confirmed the robustness of our approach and its superiority when compared to existing techniques.
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Ferran Poveda, Debora Gil, & Enric Marti. (2012). Multi-resolution DT-MRI cardiac tractography. In Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges (Vol. 7746, pp. 270–277). LNCS. Springer Berlin Heidelberg.
Abstract: Even using objective measures from DT-MRI no consensus about myocardial architecture has been achieved so far. Streamlining provides good reconstructions at low level of detail, but falls short to give global abstract interpretations. In this paper, we present a multi-resolution methodology that is able to produce simplified representations of cardiac architecture. Our approach produces a reduced set of tracts that are representative of the main geometric features of myocardial anatomical structure. Experiments show that fiber geometry is preserved along reductions, which validates the simplified model for interpretation of cardiac architecture.
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Debora Gil, Agnes Borras, Ruth Aris, Mariano Vazquez, Pierre Lafortune, & Guillame Houzeaux. (2012). What a difference in biomechanics cardiac fiber makes. In Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges (Vol. 7746, pp. 253–260). Springer Berlin Heidelberg.
Abstract: Computational simulations of the heart are a powerful tool for a comprehensive understanding of cardiac function and its intrinsic relationship with its muscular architecture. Cardiac biomechanical models require a vector field representing the orientation of cardiac fibers. A wrong orientation of the fibers can lead to a
non-realistic simulation of the heart functionality. In this paper we explore the impact of the fiber information on the simulated biomechanics of cardiac muscular anatomy. We have used the John Hopkins database to perform a biomechanical simulation using both a synthetic benchmark fiber distribution and the data obtained experimentally from DTI. Results illustrate how differences in fiber orientation affect heart deformation along cardiac cycle.
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Sergio Vera, Miguel Angel Gonzalez Ballester, & Debora Gil. (2012). Optimal Medial Surface Generation for Anatomical Volume Representations. In MichaelW. David and Vannier H. and H. Yoshida (Ed.), Abdominal Imaging. Computational and Clinical Applications (Vol. 7601, pp. 265–273). Lecture Notes in Computer Science. Springer Berlin Heidelberg.
Abstract: Medial representations are a widely used technique in abdominal organ shape representation and parametrization. Those methods require good medial manifolds as a starting point. Any medial
surface used to parametrize a volume should be simple enough to allow an easy manipulation and complete enough to allow an accurate reconstruction of the volume. Obtaining good quality medial
surfaces is still a problem with current iterative thinning methods. This forces the usage of generic, pre-calculated medial templates that are adapted to the final shape at the cost of a drop in volume reconstruction.
This paper describes an operator for generation of medial structures that generates clean and complete manifolds well suited for their further use in medial representations of abdominal organ volumes. While being simpler than thinning surfaces, experiments show its high performance in volume reconstruction and preservation of medial surface main branching topology.
Keywords: Medial surface representation; volume reconstruction
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Simeon Petkov, Adriana Romero, Xavier Carrillo, Petia Radeva, & Carlo Gatta. (2012). Robust and accurate diaphragm border detection in cardiac X-Ray angiographies. In Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges (Vol. 7746, pp. 225–234). LNCS.
Abstract: Workshop STACOM, dins del MICCAI
X-ray angiography is the most common imaging modality employed in the diagnosis of coronary diseases prior to or during a catheter-based intervention. The analysis of the patient X-Ray sequence can provide useful information about the degree of arterial stenosis, the myocardial perfusion and other clinical parameters. If the sequence has been acquired to evaluate the perfusion grade, the opacity due to the diaphragm could potentially hinder any kind of visual inspection and make more difficult a computer aided measurements. In this paper we propose an accurate and robust method to automatically identify the diaphragm border in each frame. Quantitative evaluation on a set of 11 sequences shows that the proposed algorithm outperforms previous methods.
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Rui Hua, Oriol Pujol, Francesco Ciompi, Marina Alberti, Simone Balocco, Josepa Mauri, et al. (2012). Stent Strut Detection by Classifying a Wide Set of IVUS Features. In Computed Assisted Stenting Workshop.
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