R. Bertrand, P. Gomez-Krämer, Oriol Ramos Terrades, P. Franco, & Jean-Marc Ogier. (2013). A System Based On Intrinsic Features for Fraudulent Document Detection. In 12th International Conference on Document Analysis and Recognition (pp. 106–110).
Abstract: Paper documents still represent a large amount of information supports used nowadays and may contain critical data. Even though official documents are secured with techniques such as printed patterns or artwork, paper documents suffer froma lack of security.
However, the high availability of cheap scanning and printing hardware allows non-experts to easily create fake documents. As the use of a watermarking system added during the document production step is hardly possible, solutions have to be proposed to distinguish a genuine document from a forged one.
In this paper, we present an automatic forgery detection method based on document’s intrinsic features at character level. This method is based on the one hand on outlier character detection in a discriminant feature space and on the other hand on the detection of strictly similar characters. Therefore, a feature set iscomputed for all characters. Then, based on a distance between characters of the same class.
Keywords: paper document; document analysis; fraudulent document; forgery; fake
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Javier Marin, David Vazquez, Antonio Lopez, Jaume Amores, & Bastian Leibe. (2013). Random Forests of Local Experts for Pedestrian Detection. In 15th IEEE International Conference on Computer Vision (pp. 2592–2599). IEEE.
Abstract: Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one.
Keywords: ADAS; Random Forest; Pedestrian Detection
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Jon Almazan, Albert Gordo, Alicia Fornes, & Ernest Valveny. (2013). Handwritten Word Spotting with Corrected Attributes. In 15th IEEE International Conference on Computer Vision (pp. 1017–1024).
Abstract: We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results.
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Francisco Cruz, & Oriol Ramos Terrades. (2013). Handwritten Line Detection via an EM Algorithm. In 12th International Conference on Document Analysis and Recognition (pp. 718–722).
Abstract: In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with promising results that overcome most of the presented methods. In addition, we prove the usability of the presented method by performing line segmentation on the George Washington database obtaining encouraging results.
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2013). Document noise removal using sparse representations over learned dictionary. In Symposium on Document engineering (pp. 161–168).
Abstract: best paper award
In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental
results on several datasets demonstrate the robustness of our method compared with the state-of-the-art.
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Jon Almazan, Alicia Fornes, & Ernest Valveny. (2013). A Deformable HOG-based Shape Descriptor. In 12th International Conference on Document Analysis and Recognition (pp. 1022–1026).
Abstract: In this paper we deal with the problem of recognizing handwritten shapes. We present a new deformable feature extraction method that adapts to the shape to be described, dealing in this way with the variability introduced in the handwriting domain. It consists in a selection of the regions that best define the shape to be described, followed by the computation of histograms of oriented gradients-based features over these points. Our results significantly outperform other descriptors in the literature for the task of hand-drawn shape recognition and handwritten word retrieval
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Lluis Pere de las Heras, David Fernandez, Ernest Valveny, Josep Llados, & Gemma Sanchez. (2013). Unsupervised wall detector in architectural floor plan. In 12th International Conference on Document Analysis and Recognition (pp. 1245–1249).
Abstract: Wall detection in floor plans is a crucial step in a complete floor plan recognition system. Walls define the main structure of buildings and convey essential information for the detection of other structural elements. Nevertheless, wall segmentation is a difficult task, mainly because of the lack of a standard graphical notation. The existing approaches are restricted to small group of similar notations or require the existence of pre-annotated corpus of input images to learn each new notation. In this paper we present an automatic wall segmentation system, with the ability to handle completely different notations without the need of any annotated dataset. It only takes advantage of the general knowledge that walls are a repetitive element, naturally distributed within the plan and commonly modeled by straight parallel lines. The method has been tested on four datasets of real floor plans with different notations, and compared with the state-of-the-art. The results show its suitability for different graphical notations, achieving higher recall rates than the rest of the methods while keeping a high average precision.
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Lluis Pere de las Heras, David Fernandez, Alicia Fornes, Ernest Valveny, Gemma Sanchez, & Josep Llados. (2013). Perceptual retrieval of architectural floor plans. In 10th IAPR International Workshop on Graphics Recognition.
Abstract: This paper proposes a runlength histogram signature as a percetual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query,
similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Preliminary results show the interest of the proposed approach and opens a challenging research line in graphics recognition.
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Lluis Pere de las Heras, Ernest Valveny, & Gemma Sanchez. (2013). Combining structural and statistical strategies for unsupervised wall detection in floor plans. In 10th IAPR International Workshop on Graphics Recognition.
Abstract: This paper presents an evolution of the first unsupervised wall segmentation method in floor plans, that was presented by the authors in [1]. This first approach, contrarily to the existing ones, is able to segment walls independently to their notation and without the need of any pre-annotated data
to learn their visual appearance. Despite the good performance of the first approach, some specific cases, such as curved shaped walls, were not correctly segmented since they do not agree the strict structural assumptions that guide the whole methodology in order to be able to learn, in an unsupervised way, the structure of a wall. In this paper, we refine this strategy by dividing the
process in two steps. In a first step, potential wall segments are extracted unsupervisedly using a modification of [1], by restricting even more the areas considered as walls in a first moment. In a second step, these segments are used to learn and spot lost instances based on a modified version of [2], also presented by the authors. The presented combined method have been tested on
4 datasets with different notations and compared with the stateof-the-art applyed on the same datasets. The results show its adaptability to different wall notations and shapes, significantly outperforming the original approach.
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Dimosthenis Karatzas, Faisal Shafait, Seiichi Uchida, Masakazu Iwamura, Lluis Gomez, Sergi Robles, et al. (2013). ICDAR 2013 Robust Reading Competition. In 12th International Conference on Document Analysis and Recognition (pp. 1484–1493).
Abstract: This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.
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Lluis Gomez, & Dimosthenis Karatzas. (2013). Multi-script Text Extraction from Natural Scenes. In 12th International Conference on Document Analysis and Recognition (pp. 467–471).
Abstract: Scene text extraction methodologies are usually based in classification of individual regions or patches, using a priori knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organisation through which text emerges as a perceptually significant group of atomic objects. Therefore humans are able to detect text even in languages and scripts never seen before. In this paper, we argue that the text extraction problem could be posed as the detection of meaningful groups of regions. We present a method built around a perceptual organisation framework that exploits collaboration of proximity and similarity laws to create text-group hypotheses. Experiments demonstrate that our algorithm is competitive with state of the art approaches on a standard dataset covering text in variable orientations and two languages.
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David Fernandez, Simone Marinai, Josep Llados, & Alicia Fornes. (2013). Contextual Word Spotting in Historical Manuscripts using Markov Logic Networks. In 2nd International Workshop on Historical Document Imaging and Processing (pp. 36–43).
Abstract: Natural languages can often be modelled by suitable grammars whose knowledge can improve the word spotting results. The implicit contextual information is even more useful when dealing with information that is intrinsically described as one collection of records. In this paper, we present one approach to word spotting which uses the contextual information of records to improve the results. The method relies on Markov Logic Networks to probabilistically model the relational organization of handwritten records. The performance has been evaluated on the Barcelona Marriages Dataset that contains structured handwritten records that summarize marriage information.
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A. M. Here, B. C. Lopez, Debora Gil, J. J. Camarero, & Jordi Martinez-Vilalta. (2013). A new software to analyse wood anatomical features in conifer species. In International Symposium on Wood Structure in Plant Biology and Ecology.
Abstract: International Symposium on Wood Structure in Plant Biology and Ecology
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Enric Marti, Ferran Poveda, Antoni Gurgui, Jaume Rocarias, & Debora Gil. (2013). Una propuesta de seguimiento, tutorías on line y evaluación en la metodología de Aprendizaje Basado en Proyectos.
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Sergio Vera, Miguel Angel Gonzalez Ballester, & Debora Gil. (2013). Volumetric Anatomical Parameterization and Meshing for Inter-patient Liver Coordinate System Deffinition. In 16th International Conference on Medical Image Computing and Computer Assisted Intervention.
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