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Jose Carlos Rubio, Joan Serrat, & Antonio Lopez. (2012). Unsupervised co-segmentation through region matching. In 25th IEEE Conference on Computer Vision and Pattern Recognition (pp. 749–756). IEEE Xplore.
Abstract: Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database.
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Alicia Fornes, & Gemma Sanchez. (2014). Analysis and Recognition of Music Scores. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. E, pp. 749–774). Springer London.
Abstract: The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented.
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Anjan Dutta, Josep Llados, & Umapada Pal. (2013). A symbol spotting approach in graphical documents by hashing serialized graphs. PR - Pattern Recognition, 46(3), 752–768.
Abstract: In this paper we propose a symbol spotting technique in graphical documents. Graphs are used to represent the documents and a (sub)graph matching technique is used to detect the symbols in them. We propose a graph serialization to reduce the usual computational complexity of graph matching. Serialization of graphs is performed by computing acyclic graph paths between each pair of connected nodes. Graph paths are one-dimensional structures of graphs which are less expensive in terms of computation. At the same time they enable robust localization even in the presence of noise and distortion. Indexing in large graph databases involves a computational burden as well. We propose a graph factorization approach to tackle this problem. Factorization is intended to create a unified indexed structure over the database of graphical documents. Once graph paths are extracted, the entire database of graphical documents is indexed in hash tables by locality sensitive hashing (LSH) of shape descriptors of the paths. The hashing data structure aims to execute an approximate k-NN search in a sub-linear time. We have performed detailed experiments with various datasets of line drawings and compared our method with the state-of-the-art works. The results demonstrate the effectiveness and efficiency of our technique.
Keywords: Symbol spotting; Graphics recognition; Graph matching; Graph serialization; Graph factorization; Graph paths; Hashing
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Ferran Poveda, Enric Marti, Debora Gil, Francesc Carreras, & Manel Ballester. (2012). Helical Structure of Ventricular Anatomy by Diffusion Tensor Cardiac MR Tractography. JACC - Journal of American College of Cardiology, 5(7), 754–755.
Abstract: It is widely accepted that myocardial fiber architecture plays a critical role in myocardial contractility and relaxation (1). However, there is a lack of consensus about the distribution of the myocardial fibers and their spatial arrangement in the left and right ventricles. An understanding of the cardiac architecture should benefit the ventricular functional assessment, left ventricular reconstructive surgery planning, or resynchronization therapy in heart failure. Researchers have proposed several conceptual models to describe the architecture of the heart, ranging from gross dissection to histological presentation. The cardiac mesh model (2) proposes that the myocytes are arranged longitudinally and radially change their angulation along the myocardial depth. By contrast, the helical ventricular myocardial model states that the ventricular myocardium is a continuous anatomical helical layout of myocardial fibers (1
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Sergi Garcia Bordils, George Tom, Sangeeth Reddy, Minesh Mathew, Marçal Rusiñol, C.V. Jawahar, et al. (2022). Read While You Drive-Multilingual Text Tracking on the Road. In 15th IAPR International workshop on document analysis systems (Vol. 13237, 756–770). LNCS.
Abstract: Visual data obtained during driving scenarios usually contain large amounts of text that conveys semantic information necessary to analyse the urban environment and is integral to the traffic control plan. Yet, research on autonomous driving or driver assistance systems typically ignores this information. To advance research in this direction, we present RoadText-3K, a large driving video dataset with fully annotated text. RoadText-3K is three times bigger than its predecessor and contains data from varied geographical locations, unconstrained driving conditions and multiple languages and scripts. We offer a comprehensive analysis of tracking by detection and detection by tracking methods exploring the limits of state-of-the-art text detection. Finally, we propose a new end-to-end trainable tracking model that yields state-of-the-art results on this challenging dataset. Our experiments demonstrate the complexity and variability of RoadText-3K and establish a new, realistic benchmark for scene text tracking in the wild.
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Sezer Karaoglu, Jan van Gemert, & Theo Gevers. (2013). Con-text: text detection using background connectivity for fine-grained object classification. In 21ST ACM International Conference on Multimedia (pp. 757–760).
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A. Martinez, & Jordi Vitria. (2000). Learning mixture models using a genetic version of the EM algorithm. PRL - Pattern Recognition Letters, 21(8), 759–769.
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J. Garcia, J.M. Sanchez, X. Orriols, & X. Binefa. (2000). Chromatic aberration and depth extraction. In 15 th International Conference on Pattern Recognition (Vol. 1, pp. 762–765).
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Francesco Ciompi, Oriol Pujol, Carlo Gatta, Oriol Rodriguez-Leor, J. Mauri, & Petia Radeva. (2010). Fusing in-vitro and in-vivo intravascular ultrasound data for plaque characterization. IJCI - International Journal of Cardiovascular Imaging, 26(7), 763–779.
Abstract: Accurate detection of in-vivo vulnerable plaque in coronary arteries is still an open problem. Recent studies show that it is highly related to tissue structure and composition. Intravascular Ultrasound (IVUS) is a powerful imaging technique that gives a detailed cross-sectional image of the vessel, allowing to explore arteries morphology. IVUS data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue. The main drawback of this method is the few number of available case studies and validated data due to the complex procedure of histological analysis of the tissue. On the other hand, IVUS data from in-vivo cases is easy to obtain but it can not be histologically validated. In this work, we propose to enhance the in-vitro training data set by selectively including examples from in-vivo plaques. For this purpose, a Sequential Floating Forward Selection method is reformulated in the context of plaque characterization. The enhanced classifier performance is validated on in-vitro data set, yielding an overall accuracy of 91.59% in discriminating among fibrotic, lipidic and calcified plaques, while reducing the gap between in-vivo and in-vitro data analysis. Experimental results suggest that the obtained classifier could be properly applied on in-vivo plaque characterization and also demonstrate that the common hypothesis of assuming the difference between in-vivo and in-vitro as negligible is incorrect.
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David Fernandez, Josep Llados, Alicia Fornes, & R.Manmatha. (2012). On Influence of Line Segmentation in Efficient Word Segmentation in Old Manuscripts. In 13th International Conference on Frontiers in Handwriting Recognition (pp. 763–768).
Abstract: he objective of this work is to show the importance of a good line segmentation to obtain better results in the segmentation of words of historical documents. We have used the approach developed by Manmatha and Rothfeder [1] to segment words in old handwritten documents. In their work the lines of the documents are extracted using projections. In this work, we have developed an approach to segment lines more efficiently. The new line segmentation algorithm tackles with skewed, touching and noisy lines, so it is significantly improves word segmentation. Experiments using Spanish documents from the Marriages Database of the Barcelona Cathedral show that this approach reduces the error rate by more than 20%
Keywords: document image processing;handwritten character recognition;history;image segmentation;Spanish document;historical document;line segmentation;old handwritten document;old manuscript;word segmentation;Bifurcation;Dynamic programming;Handwriting recognition;Image segmentation;Measurement;Noise;Skeleton;Segmentation;document analysis;document and text processing;handwriting analysis;heuristics;path-finding
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Albert Clapes, Alex Pardo, Oriol Pujol, & Sergio Escalera. (2018). Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly. MVAP - Machine Vision and Applications, 29(5), 765–788.
Abstract: We present a vision-inertial system which combines two RGB-Depth devices together with a wearable inertial movement unit in order to detect activities of the daily living. From multi-view videos, we extract dense trajectories enriched with a histogram of normals description computed from the depth cue and bag them into multi-view codebooks. During the later classification step a multi-class support vector machine with a RBF- 2 kernel combines the descriptions at kernel level. In order to perform action detection from the videos, a sliding window approach is utilized. On the other hand, we extract accelerations, rotation angles, and jerk features from the inertial data collected by the wearable placed on the user’s dominant wrist. During gesture spotting, a dynamic time warping is applied and the aligning costs to a set of pre-selected gesture sub-classes are thresholded to determine possible detections. The outputs of the two modules are combined in a late-fusion fashion. The system is validated in a real-case scenario with elderly from an elder home. Learning-based fusion results improve the ones from the single modalities, demonstrating the success of such multimodal approach.
Keywords: Multimodal activity detection; Computer vision; Inertial sensors; Dense trajectories; Dynamic time warping; Assistive technology
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Debora Gil, Aura Hernandez-Sabate, Oriol Rodriguez, J. Mauri, & Petia Radeva. (2006). Statistical Strategy for Anisotropic Adventitia Modelling in IVUS. IEEE Transactions on Medical Imaging, 25(6), 768–778.
Abstract: Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and mediaadventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders. Index Terms–-Anisotropic processing, intravascular ultrasound (IVUS), vessel border segmentation, vessel structure classification.
Keywords: Corners; T-junctions; Wavelets
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A. Pujol, Jordi Vitria, Felipe Lumbreras, & Juan J. Villanueva. (2001). Topological principal component analysis for face encoding and recognition. PRL - Pattern Recognition Letters, 22(6-7), 769–776.
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Sergio Escalera, Alicia Fornes, Oriol Pujol, Josep Llados, & Petia Radeva. (2007). Multi-class Binary Object Categorization using Blurred Shape Models. In Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern (Vol. 4756, 773–782). LCNS.
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Ruben Tito, Dimosthenis Karatzas, & Ernest Valveny. (2021). Document Collection Visual Question Answering. In 16th International Conference on Document Analysis and Recognition (Vol. 12822, pp. 778–792). LNCS.
Abstract: Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task.
Keywords: Document collection; Visual Question Answering
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