Arnau Ramisa, Shrihari Vasudevan, David Aldavert, Ricardo Toledo, & Ramon Lopez de Mantaras. (2009). Evaluation of the SIFT Object Recognition Method in Mobile Robots: Frontiers in Artificial Intelligence and Applications. In 12th International Conference of the Catalan Association for Artificial Intelligence (Vol. 202, pp. 9–18).
Abstract: General object recognition in mobile robots is of primary importance in order to enhance the representation of the environment that robots will use for their reasoning processes. Therefore, we contribute reduce this gap by evaluating the SIFT Object Recognition method in a challenging dataset, focusing on issues relevant to mobile robotics. Resistance of the method to the robotics working conditions was found, but it was limited mainly to well-textured objects.
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Fahad Shahbaz Khan, Joost Van de Weijer, & Maria Vanrell. (2009). Top-Down Color Attention for Object Recognition. In 12th International Conference on Computer Vision (pp. 979–986).
Abstract: Generally the bag-of-words based image representation follows a bottom-up paradigm. The subsequent stages of the process: feature detection, feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, combining multiple cues such as shape and color often provides below-expected results. This paper presents a novel method for recognizing object categories when using multiple cues by separating the shape and color cue. Color is used to guide attention by means of a top-down category-specific attention map. The color attention map is then further deployed to modulate the shape features by taking more features from regions within an image that are likely to contain an object instance. This procedure leads to a category-specific image histogram representation for each category. Furthermore, we argue that the method combines the advantages of both early and late fusion. We compare our approach with existing methods that combine color and shape cues on three data sets containing varied importance of both cues, namely, Soccer ( color predominance), Flower (color and shape parity), and PASCAL VOC Challenge 2007 (shape predominance). The experiments clearly demonstrate that in all three data sets our proposed framework significantly outperforms the state-of-the-art methods for combining color and shape information.
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Ivan Huerta, Michael Holte, Thomas B. Moeslund, & Jordi Gonzalez. (2009). Detection and Removal of Chromatic Moving Shadows in Surveillance Scenarios. In 12th International Conference on Computer Vision (pp. 1499–1506).
Abstract: Segmentation in the surveillance domain has to deal with shadows to avoid distortions when detecting moving objects. Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows. Consequently, umbra shadows are usually detected as part of moving objects. In this paper we present a novel technique based on gradient and colour models for separating chromatic moving cast shadows from detected moving objects. Firstly, both a chromatic invariant colour cone model and an invariant gradient model are built to perform automatic segmentation while detecting potential shadows. In a second step, regions corresponding to potential shadows are grouped by considering “a bluish effect” and an edge partitioning. Lastly, (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. Unlike other approaches, our method does not make any a-priori assumptions about camera location, surface geometries, surface textures, shapes and types of shadows, objects, and background. Experimental results show the performance and accuracy of our approach in different shadowed materials and illumination conditions.
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Francesco Ciompi, Oriol Pujol, E Fernandez-Nofrerias, J. Mauri, & Petia Radeva. (2009). ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences. In 12th International Conference on Medical Image and Computer Assisted Intervention (Vol. 5762). LNCS. Springer Berlin Heidelberg.
Abstract: The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers.
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Mehdi Mirza-Mohammadi, Sergio Escalera, & Petia Radeva. (2009). Contextual-Guided Bag-of-Visual-Words Model for Multi-class Object Categorization. In 13th International Conference on Computer Analysis of Images and Patterns (Vol. 5702, 748–756). LNCS. Springer Berlin Heidelberg.
Abstract: Bag-of-words model (BOW) is inspired by the text classification problem, where a document is represented by an unsorted set of contained words. Analogously, in the object categorization problem, an image is represented by an unsorted set of discrete visual words (BOVW). In these models, relations among visual words are performed after dictionary construction. However, close object regions can have far descriptions in the feature space, being grouped as different visual words. In this paper, we present a method for considering geometrical information of visual words in the dictionary construction step. Object interest regions are obtained by means of the Harris-Affine detector and then described using the SIFT descriptor. Afterward, a contextual-space and a feature-space are defined, and a merging process is used to fuse feature words based on their proximity in the contextual-space. Moreover, we use the Error Correcting Output Codes framework to learn the new dictionary in order to perform multi-class classification. Results show significant classification improvements when spatial information is taken into account in the dictionary construction step.
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Miquel Ferrer, Ernest Valveny, F. Serratosa, I. Bardaji, & Horst Bunke. (2009). Graph-based k-means clustering: A comparison of the set versus the generalized median graph. In 13th International Conference on Computer Analysis of Images and Patterns (Vol. 5702, 342–350). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph.
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Debora Gil, Aura Hernandez-Sabate, Mireia Burnat, Steven Jansen, & Jordi Martinez-Vilalta. (2009). Structure-Preserving Smoothing of Biomedical Images. In 13th International Conference on Computer Analysis of Images and Patterns (Vol. 5702, pp. 427–434). LNCS. Springer Berlin Heidelberg.
Abstract: Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images.
Keywords: non-linear smoothing; differential geometry; anatomical structures segmentation; cardiac magnetic resonance; computerized tomography.
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Sergio Escalera, Alicia Fornes, Oriol Pujol, & Petia Radeva. (2009). Multi-class Binary Symbol Classification with Circular Blurred Shape Models. In 15th International Conference on Image Analysis and Processing (Vol. 5716, 1005–1014). LNCS. Springer Berlin Heidelberg.
Abstract: Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements.
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L.Tarazon, D. Perez, N. Serrano, V. Alabau, Oriol Ramos Terrades, A. Sanchis, et al. (2009). Confidence Measures for Error Correction in Interactive Transcription of Handwritten Text. In 15th International Conference on Image Analysis and Processing (Vol. 5716, pp. 567–574). LNCS. Springer Berlin Heidelberg.
Abstract: An effective approach to transcribe old text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the human supervisor, and the supervisor is assisted by the system to complete the transcription task as efficiently as possible. In this paper, we focus on a particular system prototype called GIDOC, which can be seen as a first attempt to provide user-friendly, integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. More specifically, we focus on the handwriting recognition part of GIDOC, for which we propose the use of confidence measures to guide the human supervisor in locating possible system errors and deciding how to proceed. Empirical results are reported on two datasets showing that a word error rate not larger than a 10% can be achieved by only checking the 32% of words that are recognised with less confidence.
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Enric Marti, Jaume Rocarias, Ricardo Toledo, & Aura Hernandez-Sabate. (2009). Caronte: plataforma Moodle con gestion flexible de grupos. Primeras experiencias en asignaturas de Ingenieria Informatica.
Abstract: En este artículo se presenta Caronte, entorno LMS (Learning Management System) basado en Moodle. Una característica importante del entorno es la gestión flexible de grupos en una asignatura. Entendemos por grupo un conjunto de alumnos que realizan un trabajo y uno de ellos entrega la actividad propuesta (práctica, encuesta, etc.) en representación del grupo. Hemos trabajado en la confección de estos grupos, implementando un sistema de inscripción por contraseña.
Caronte ofrece un conjunto de actividades basadas en este concepto de grupo: encuestas, tareas (entrega de trabajos o prácticas), encuestas de autoevaluación y cuestionarios, entre otras.
Basada en nuestra actividad de encuesta, hemos definido una actividad de Control, que permite un cierto feedback electrónico del profesor sobre la actividad de los alumnos.
Finalmente, se presenta un resumen de las experiencias de uso de Caronte sobre asignaturas de Ingeniería Informática en el curso 2007-08.
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Joan Oliver, Ricardo Toledo, J. Pujol, J. Sorribes, & E. Valderrama. (2009). Un ABP basado en la robotica para las ingenierias informaticas.
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Enric Marti, Debora Gil, Marc Vivet, & Carme Julia. (2009). Aprendizaje Basado en Proyectos en la asignatura de Gráficos por Computador en Ingeniería Informática. Balance de cuatro años de experiencia. Barcelona, Spain.
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Enric Marti, Debora Gil, Marc Vivet, & Carme Julia. (2009). Uso de recursos virtuales en Aprendizaje Basado en Proyectos. Una experiencia en la asignatura de Gráficos por Computador. Octava Jornada sobre Aprendizaje Cooperativo. Lleida.
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Sergio Escalera, Alicia Fornes, Oriol Pujol, Alberto Escudero, & Petia Radeva. (2009). Circular Blurred Shape Model for Symbol Spotting in Documents. In 16th IEEE International Conference on Image Processing (pp. 1985–1988).
Abstract: Symbol spotting problem requires feature extraction strategies able to generalize from training samples and to localize the target object while discarding most part of the image. In the case of document analysis, symbol spotting techniques have to deal with a high variability of symbols' appearance. In this paper, we propose the Circular Blurred Shape Model descriptor. Feature extraction is performed capturing the spatial arrangement of significant object characteristics in a correlogram structure. Shape information from objects is shared among correlogram regions, being tolerant to the irregular deformations. Descriptors are learnt using a cascade of classifiers and Abadoost as the base classifier. Finally, symbol spotting is performed by means of a windowing strategy using the learnt cascade over plan and old musical score documents. Spotting and multi-class categorization results show better performance comparing with the state-of-the-art descriptors.
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Jose Manuel Alvarez, Ferran Diego, Joan Serrat, & Antonio Lopez. (2009). Automatic Ground-truthing using video registration for on-board detection algorithms. In 16th IEEE International Conference on Image Processing (pp. 4389–4392).
Abstract: Ground-truth data is essential for the objective evaluation of object detection methods in computer vision. Many works claim their method is robust but they support it with experiments which are not quantitatively assessed with regard some ground-truth. This is one of the main obstacles to properly evaluate and compare such methods. One of the main reasons is that creating an extensive and representative ground-truth is very time consuming, specially in the case of video sequences, where thousands of frames have to be labelled. Could such a ground-truth be generated, at least in part, automatically? Though it may seem a contradictory question, we show that this is possible for the case of video sequences recorded from a moving camera. The key idea is transferring existing frame segmentations from a reference sequence into another video sequence recorded at a different time on the same track, possibly under a different ambient lighting. We have carried out experiments on several video sequence pairs and quantitatively assessed the precision of the transformed ground-truth, which prove that our approach is not only feasible but also quite accurate.
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