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Michal Drozdzal, Santiago Segui, Petia Radeva, Jordi Vitria, & Laura Igual. (2011). System and Method for Displaying Motility Events in an in Vivo Image Stream.
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Patricia Marquez, Debora Gil, & Aura Hernandez-Sabate. (2012). Error Analysis for Lucas-Kanade Based Schemes. In 9th International Conference on Image Analysis and Recognition (Vol. 7324, pp. 184–191). LNCS. Springer-Verlag Berlin Heidelberg.
Abstract: Optical flow is a valuable tool for motion analysis in medical imaging sequences. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in medical sequences. This paper presents an error analysis of Lucas-Kanade schemes in terms of intrinsic design errors and numerical stability of the algorithm. Our analysis provides a confidence measure that is naturally correlated to the accuracy of the flow field. Our experiments show the higher predictive value of our confidence measure compared to existing measures.
Keywords: Optical flow, Confidence measure, Lucas-Kanade, Cardiac Magnetic Resonance
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Albert Andaluz, Francesc Carreras, Cristina Santa Marta, & Debora Gil. (2012). Myocardial torsion estimation with Tagged-MRI in the OsiriX platform. In Wiro Niessen(Erasmus MC) and Marc Modat(UCL) (Ed.), ISBI Workshop on Open Source Medical Image Analysis software. IEEE.
Abstract: Myocardial torsion (MT) plays a crucial role in the assessment of the functionality of the
left ventricle. For this purpose, the IAM group at the CVC has developed the Harmonic Phase Flow (HPF) plugin for the Osirix DICOM platform . We have validated its funcionalty on sequences acquired using different protocols and including healthy and pathological cases. Results show similar torsion trends for SPAMM acquisitions, with pathological cases introducing expected deviations from the ground truth. Finally, we provide the plugin free of charge at http://iam.cvc.uab.es
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Patricia Marquez, Debora Gil, & Aura Hernandez-Sabate. (2013). Evaluation of the Capabilities of Confidence Measures for Assessing Optical Flow Quality. In ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars (pp. 624–631).
Abstract: Assessing Optical Flow (OF) quality is essential for its further use in reliable decision support systems. The absence of ground truth in such situations leads to the computation of OF Confidence Measures (CM) obtained from either input or output data. A fair comparison across the capabilities of the different CM for bounding OF error is required in order to choose the best OF-CM pair for discarding points where OF computation is not reliable. This paper presents a statistical probabilistic framework for assessing the quality of a given CM. Our quality measure is given in terms of the percentage of pixels whose OF error bound can not be determined by CM values. We also provide statistical tools for the computation of CM values that ensures a given accuracy of the flow field.
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Francesco Ciompi, Rui Hua, Simone Balocco, Marina Alberti, Oriol Pujol, Carles Caus, et al. (2013). Learning to Detect Stent Struts in Intravascular Ultrasound. In 6th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 7887, pp. 575–583). Springer Berlin Heidelberg.
Abstract: In this paper we tackle the automatic detection of struts elements (metallic braces of a stent device) in Intravascular Ultrasound (IVUS) sequences. The proposed method is based on context-aware classification of IVUS images, where we use Multi-Class Multi-Scale Stacked Sequential Learning (M2SSL). Additionally, we introduce a novel technique to reduce the amount of required contextual features. The comparison with binary and multi-class learning is also performed, using a dataset of IVUS images with struts manually annotated by an expert. The best performing configuration reaches a F-measure F = 63.97% .
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Marçal Rusiñol, V. Poulain d'Andecy, Dimosthenis Karatzas, & Josep Llados. (2013). Classification of Administrative Document Images by Logo Identification. In 10th IAPR International Workshop on Graphics Recognition.
Abstract: This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier's graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents.
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Anjan Dutta, Josep Llados, Horst Bunke, & Umapada Pal. (2014). A Product Graph Based Method for Dual Subgraph Matching Applied to Symbol Spotting. In Bart Lamiroy, & Jean-Marc Ogier (Eds.), Graphics Recognition. Current Trends and Challenges (Vol. 8746, pp. 7–11). LNCS. Springer Berlin Heidelberg.
Abstract: Product graph has been shown as a way for matching subgraphs. This paper reports the extension of the product graph methodology for subgraph matching applied to symbol spotting in graphical documents. Here we focus on the two major limitations of the previous version of the algorithm: (1) spurious nodes and edges in the graph representation and (2) inefficient node and edge attributes. To deal with noisy information of vectorized graphical documents, we consider a dual edge graph representation on the original graph representing the graphical information and the product graph is computed between the dual edge graphs of the pattern graph and the target graph. The dual edge graph with redundant edges is helpful for efficient and tolerating encoding of the structural information of the graphical documents. The adjacency matrix of the product graph locates the pair of similar edges of two operand graphs and exponentiating the adjacency matrix finds similar random walks of greater lengths. Nodes joining similar random walks between two graphs are found by combining different weighted exponentials of adjacency matrices. An experimental investigation reveals that the recall obtained by this approach is quite encouraging.
Keywords: Product graph; Dual edge graph; Subgraph matching; Random walks; Graph kernel
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Marçal Rusiñol, Dimosthenis Karatzas, & Josep Llados. (2013). Spotting Graphical Symbols in Camera-Acquired Documents in Real Time. In 10th IAPR International Workshop on Graphics Recognition.
Abstract: In this paper we present a system devoted to spot graphical symbols in camera-acquired document images. The system is based on the extraction and further matching of ORB compact local features computed over interest key-points. Then, the FLANN indexing framework based on approximate nearest neighbor search allows to efficiently match local descriptors between the captured scene and the graphical models. Finally, the RANSAC algorithm is used in order to compute the homography between the spotted symbol and its appearance in the document image. The proposed approach is efficient and is able to work in real time.
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Marçal Rusiñol, T.Benkhelfallah, & V. Poulain d'Andecy. (2013). Field Extraction from Administrative Documents by Incremental Structural Templates. In 12th International Conference on Document Analysis and Recognition (pp. 1100–1104).
Abstract: In this paper we present an incremental framework aimed at extracting field information from administrative document images in the context of a Digital Mail-room scenario. Given a single training sample in which the user has marked which fields have to be extracted from a particular document class, a document model representing structural relationships among words is built. This model is incrementally refined as the system processes more and more documents from the same class. A reformulation of the tf-idf statistic scheme allows to adjust the importance weights of the structural relationships among words. We report in the experimental section our results obtained with a large dataset of real invoices.
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Albert Gordo, Marçal Rusiñol, Dimosthenis Karatzas, & Andrew Bagdanov. (2013). Document Classification and Page Stream Segmentation for Digital Mailroom Applications. In 12th International Conference on Document Analysis and Recognition (pp. 621–625).
Abstract: In this paper we present a method for the segmentation of continuous page streams into multipage documents and the simultaneous classification of the resulting documents. We first present an approach to combine the multiple pages of a document into a single feature vector that represents the whole document. Despite its simplicity and low computational cost, the proposed representation yields results comparable to more complex methods in multipage document classification tasks. We then exploit this representation in the context of page stream segmentation. The most plausible segmentation of a page stream into a sequence of multipage documents is obtained by optimizing a statistical model that represents the probability of each segmented multipage document belonging to a particular class. Experimental results are reported on a large sample of real administrative multipage documents.
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L. Rothacker, Marçal Rusiñol, & G.A. Fink. (2013). Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents. In 12th International Conference on Document Analysis and Recognition (pp. 1305–1309).
Abstract: Recent HMM-based approaches to handwritten word spotting require large amounts of learning samples and mostly rely on a prior segmentation of the document. We propose to use Bag-of-Features HMMs in a patch-based segmentation-free framework that are estimated by a single sample. Bag-of-Features HMMs use statistics of local image feature representatives. Therefore they can be considered as a variant of discrete HMMs allowing to model the observation of a number of features at a point in time. The discrete nature enables us to estimate a query model with only a single example of the query provided by the user. This makes our method very flexible with respect to the availability of training data. Furthermore, we are able to outperform state-of-the-art results on the George Washington dataset.
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Ernest Valveny, Oriol Ramos Terrades, Joan Mas, & Marçal Rusiñol. (2013). Interactive Document Retrieval and Classification. In Angel Sappa, & Jordi Vitria (Eds.), Multimodal Interaction in Image and Video Applications (Vol. 48, pp. 17–30). Springer Berlin Heidelberg.
Abstract: In this chapter we describe a system for document retrieval and classification following the interactive-predictive framework. In particular, the system addresses two different scenarios of document analysis: document classification based on visual appearance and logo detection. These two classical problems of document analysis are formulated following the interactive-predictive model, taking the user interaction into account to make easier the process of annotating and labelling the documents. A system implementing this model in a real scenario is presented and analyzed. This system also takes advantage of active learning techniques to speed up the task of labelling the documents.
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Jiaolong Xu, Sebastian Ramos, Xu Hu, David Vazquez, & Antonio Lopez. (2013). Multi-task Bilinear Classifiers for Visual Domain Adaptation. In Advances in Neural Information Processing Systems Workshop.
Abstract: We propose a method that aims to lessen the significant accuracy degradation
that a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation.
The bilinear classifier encodes the feature transformation and classification
parameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines.
Keywords: Domain Adaptation; Pedestrian Detection; ADAS
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Hany Salah Eldeen. (2009). Colour Naming in Context through a Perceptual Model (Vol. 130). Master's thesis, , Bellaterra, Barcelona.
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Naila Murray. (2009). Perceptual Feature Detection (Vol. 131). Master's thesis, , Bellaterra, Barcelona.
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