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Lluis Pere de las Heras. (2010). Syntactic Model for Semantic Document Analysis (Vol. 158).
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Anjan Dutta. (2010). Symbol Spotting in Graphical Documents by Serialized Subgraph Matching (Vol. 159). Master's thesis, , .
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Ekain Artola. (2010). Human Attention Map Prediction Combining Visual Features (Vol. 160). Bachelor's thesis, , .
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David Fernandez. (2010). Handwritten Word Spotting in Old Manuscript Images using Shape Descriptors (Vol. 161). Master's thesis, , .
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Jon Almazan. (2010). Deforming the Blurred Shape Model for Shape Description and Recognition (Vol. 163). Master's thesis, , .
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Nataliya Shapovalova. (2010). On Importance of Interaction and Context (Vol. 155). Master's thesis, , .
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Zhanwu Xiong. (2010). A Pompd Model for Active Camera Control (Vol. 156). Master's thesis, , .
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David Geronimo, & Antonio Lopez. (2010). Deteccion de Peatones para Sistemas Avanzados de Asistencia al Conductor.
Abstract: Los sistemas de asistencia al conductor, y particularmente los sistemas de protección de peatones, representan uno de los campos de investigación más activos dedicados a la mejora de la seguridad vial. El mayor desafío es el desarrollo de sistemas a bordo fiables de detección de peatones. En esta revisión del estado de la técnica de la detección de peatones, se divide el problema en diferentes etapas, cada una con responsabilidades propias dentro del sistema. Esta división facilita el posterior análisis y discusión de cada uno de los métodos en la literatura, favoreciendo la comparación entre ellos. Finalmente se discuten los temas más importantes de este campo poniendo especial énfasis en las necesidades actuales y los desafíos futuros.
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Sergio Escalera, Petia Radeva, Jordi Vitria, Xavier Baro, & Bogdan Raducanu. (2010). Modelling and Analyzing Multimodal Dyadic Interactions Using Social Networks. In 12th International Conference on Multimodal Interfaces and 7th Workshop on Machine Learning for Multimodal Interaction..
Abstract: Social network analysis became a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from
multimodal dyadic interactions. First, speech detection is performed through an audio/visual fusion scheme based on stacked sequential learning. In the audio domain, speech is detected through clusterization of audio features. Clusters
are modelled by means of an One-state Hidden Markov Model containing a diagonal covariance Gaussian Mixture Model. In the visual domain, speech detection is performed through differential-based feature extraction from the segmented
mouth region, and a dynamic programming matching procedure. Second, in order to model the dyadic interactions, we employed the Influence Model whose states
encode the previous integrated audio/visual data. Third, the social network is extracted based on the estimated influences. For our study, we used a set of videos belonging to New York Times’ Blogging Heads opinion blog. The results
are reported both in terms of accuracy of the audio/visual data fusion and centrality measures used to characterize the social network.
Keywords: Social interaction; Multimodal fusion, Influence model; Social network analysis
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Jaume Amores, David Geronimo, & Antonio Lopez. (2010). Multiple instance and active learning for weakly-supervised object-class segmentation. In 3rd IEEE International Conference on Machine Vision.
Abstract: In object-class segmentation, one of the most tedious tasks is to manually segment many object examples in order to learn a model of the object category. Yet, there has been little research on reducing the degree of manual annotation for
object-class segmentation. In this work we explore alternative strategies which do not require full manual segmentation of the object in the training set. In particular, we study the use of bounding boxes as a coarser and much cheaper form of segmentation and we perform a comparative study of several Multiple-Instance Learning techniques that allow to obtain a model with this type of weak annotation. We show that some of these methods can be competitive, when used with coarse
segmentations, with methods that require full manual segmentation of the objects. Furthermore, we show how to use active learning combined with this weakly supervised strategy.
As we see, this strategy permits to reduce the amount of annotation and optimize the number of examples that require full manual segmentation in the training set.
Keywords: Multiple Instance Learning; Active Learning; Object-class segmentation.
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Joan Serrat, & Antonio Lopez. (2010). Deteccion automatica de lineas de carril para la asistencia a la conduccion.
Abstract: La detección por cámara de las líneas de carril en las carreteras puede ser una solución asequible a los riesgos de conducción generados por los adelantamientos o las salidas de carril. Este trabajo propone un sistema que funciona en tiempo real y que obtiene muy buenos resultados. El sistema está preparado para identificar las líneas en condiciones de visibilidad poco favorables, como puede ser la conducción nocturna o con otros vehículos que dificulten la visión.
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Joan Mas, Gemma Sanchez, & Josep Llados. (2009). SSP: Sketching slide Presentations, a Syntactic Approach. In 8th IAPR International Workshop on Graphics Recognition.
Abstract: The design of a slide presentation is a creative process. In this process first, humans visualize in their minds what they want to explain. Then, they have to be able to represent this knowledge in an understandable way. There exists a lot of commercial software that allows to create our own slide presentations but the creativity of the user is rather limited. In this article we present an application that allows the user to create and visualize a slide presentation from a sketch. A slide may be seen as a graphical document or a diagram where its elements are placed in a particular spatial arrangement. To describe and recognize slides a syntactic approach is proposed. This approach is based on an Adjacency Grammar and a parsing methodology to cope with this kind of grammars. The experimental evaluation shows the performance of our methodology from a qualitative and a quantitative point of view. Six different slides containing different number of symbols, from 4 to 7, have been given to the users and they have drawn them without restrictions in the order of the elements. The quantitative results give an idea on how suitable is our methodology to describe and recognize the different elements in a slide.
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Salim Jouili, Salvatore Tabbone, & Ernest Valveny. (2009). Comparing Graph Similarity Measures for Graphical Recognition. In 8th IAPR International Workshop on Graphics Recognition. LNCS. Springer.
Abstract: In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique.
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Marçal Rusiñol, K. Bertet, Jean-Marc Ogier, & Josep Llados. (2009). Symbol Recognition Using a Concept Lattice of Graphical Patterns. In 8th IAPR International Workshop on Graphics Recognition.
Abstract: In this paper we propose a new approach to recognize symbols by the use of a concept lattice. We propose to build a concept lattice in terms of graphical patterns. Each model symbol is decomposed in a set of composing graphical patterns taken as primitives. Each one of these primitives is described by boundary moment invariants. The obtained concept lattice relates which symbolic patterns compose a given graphical symbol. A Hasse diagram is derived from the context and is used to recognize symbols affected by noise. We present some preliminary results over a variation of the dataset of symbols from the GREC 2005 symbol recognition contest.
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Partha Pratim Roy, Umapada Pal, & Josep Llados. (2009). Touching Text Character Localization in Graphical Documents using SIFT. In In proceedings 8th IAPR International Workshop on Graphics Recognition.
Abstract: Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text and symbol overlapped/touched. Intersection of text and symbols with graphical lines and curves occur frequently in graphical documents and hence separation of such symbols is very difficult.
Several pattern recognition and classification techniques exist to recognize isolated text/symbol. But, the touching/overlapping text and symbol recognition has not yet been dealt successfully. An interesting technique, Scale Invariant Feature Transform (SIFT), originally devised for object recognition can take care of overlapping problems. Even if SIFT features have emerged as a very powerful object descriptors, their employment in graphical documents context has not been investigated much. In this paper we present the adaptation of the SIFT approach in the context of text character localization (spotting) in graphical documents. We evaluate the applicability of this technique in such documents and discuss the scope of improvement by combining some state-of-the-art approaches.
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