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David Fernandez, Josep Llados and Alicia Fornes. 2011. Handwritten Word Spotting in Old Manuscript Images Using a Pseudo-Structural Descriptor Organized in a Hash Structure. In Jordi Vitria, Joao Miguel Raposo and Mario Hernandez, eds. 5th Iberian Conference on Pattern Recognition and Image Analysis.628–635.
Abstract: There are lots of historical handwritten documents with information that can be used for several studies and projects. The Document Image Analysis and Recognition community is interested in preserving these documents and extracting all the valuable information from them. Handwritten word-spotting is the pattern classification task which consists in detecting handwriting word images. In this work, we have used a query-by-example formalism: we have matched an input image with one or multiple images from handwritten documents to determine the distance that might indicate a correspondence. We have developed an approach based in characteristic Loci Features stored in a hash structure. Document images of the marriage licences of the Cathedral of Barcelona are used as the benchmarking database.
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Dimosthenis Karatzas, Sergi Robles, Joan Mas, Farshad Nourbakhsh and Partha Pratim Roy. 2011. ICDAR 2011 Robust Reading Competition – Challege 1: Reading Text in Born-Digital Images (Web and Email). 11th International Conference on Document Analysis and Recognition.1485–1490.
Abstract: This paper presents the results of the first Challenge of ICDAR 2011 Robust Reading Competition. Challenge 1 is focused on the extraction of text from born-digital images, specifically from images found in Web pages and emails. The challenge was organized in terms of three tasks that look at different stages of the process: text localization, text segmentation and word recognition. In this paper we present the results of the challenge for all three tasks, and make an open call for continuous participation outside the context of ICDAR 2011.
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Jaume Gibert, Ernest Valveny and Horst Bunke. 2011. Dimensionality Reduction for Graph of Words Embedding. In Xiaoyi Jiang, Miquel Ferrer and Andrea Torsello, eds. 8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition.22–31. (LNCS.)
Abstract: The Graph of Words Embedding consists in mapping every graph of a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. While it shows good properties in classification problems, it suffers from high dimensionality and sparsity. These two issues are addressed in this article. Two well-known techniques for dimensionality reduction, kernel principal component analysis (kPCA) and independent component analysis (ICA), are applied to the embedded graphs. We discuss their performance compared to the classification of the original vectors on three different public databases of graphs.
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Jaume Gibert, Ernest Valveny and Horst Bunke. 2011. Vocabulary Selection for Graph of Words Embedding. In Vitria, J., J.M.R. Sanches and M. Hernández, eds. 5th Iberian Conference on Pattern Recognition and Image Analysis. Berlin, Springer, 216–223. (LNCS.)
Abstract: The Graph of Words Embedding consists in mapping every graph in a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. It has been shown to perform well for graphs with discrete label alphabets. In this paper we extend the methodology to graphs with n-dimensional continuous attributes by selecting node representatives. We propose three different discretization procedures for the attribute space and experimentally evaluate the dependence on both the selector and the number of node representatives. In the context of graph classification, the experimental results reveal that on two out of three public databases the proposed extension achieves superior performance over a standard reference system.
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Jaume Gibert, Ernest Valveny, Oriol Ramos Terrades and Horst Bunke. 2011. Multiple Classifiers for Graph of Words Embedding. In Carlo Sansone, Josef Kittler and Fabio Roli, eds. 10th International Conference on Multiple Classifier Systems.36–45. (LNCS.)
Abstract: During the last years, there has been an increasing interest in applying the multiple classifier framework to the domain of structural pattern recognition. Constructing base classifiers when the input patterns are graph based representations is not an easy problem. In this work, we make use of the graph embedding methodology in order to construct different feature vector representations for graphs. The graph of words embedding assigns a feature vector to every graph by counting unary and binary relations between node representatives and combining these pieces of information into a single vector. Selecting different node representatives leads to different vectorial representations and therefore to different base classifiers that can be combined. We experimentally show how this methodology significantly improves the classification of graphs with respect to single base classifiers.
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Jon Almazan, Alicia Fornes and Ernest Valveny. 2011. A Non-Rigid Feature Extraction Method for Shape Recognition. 11th International Conference on Document Analysis and Recognition.987–991.
Abstract: This paper presents a methodology for shape recognition that focuses on dealing with the difficult problem of large deformations. The proposed methodology consists in a novel feature extraction technique, which uses a non-rigid representation adaptable to the shape. This technique employs a deformable grid based on the computation of geometrical centroids that follows a region partitioning algorithm. Then, a feature vector is extracted by computing pixel density measures around these geometrical centroids. The result is a shape descriptor that adapts its representation to the given shape and encodes the pixel density distribution. The validity of the method when dealing with large deformations has been experimentally shown over datasets composed of handwritten shapes. It has been applied to signature verification and shape recognition tasks demonstrating high accuracy and low computational cost.
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Jon Almazan, Ernest Valveny and Alicia Fornes. 2011. Deforming the Blurred Shape Model for Shape Description and Recognition. In Jordi Vitria, Joao Miguel Raposo and Mario Hernandez, eds. 5th Iberian Conference on Pattern Recognition and Image Analysis. Berlin, Springer-Verlag, 1–8. (LNCS.)
Abstract: This paper presents a new model for the description and recognition of distorted shapes, where the image is represented by a pixel density distribution based on the Blurred Shape Model combined with a non-linear image deformation model. This leads to an adaptive structure able to capture elastic deformations in shapes. This method has been evaluated using thee different datasets where deformations are present, showing the robustness and good performance of the new model. Moreover, we show that incorporating deformation and flexibility, the new model outperforms the BSM approach when classifying shapes with high variability of appearance.
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Kaida Xiao, Chenyang Fu, Dimosthenis Karatzas and Sophie Wuerger. 2011. Visual Gamma Correction for LCD Displays. DIS, 32(1), 17–23.
Abstract: An improved method for visual gamma correction is developed for LCD displays to increase the accuracy of digital colour reproduction. Rather than utilising a photometric measurement device, we use observ- ers’ visual luminance judgements for gamma correction. Eight half tone patterns were designed to gen- erate relative luminances from 1/9 to 8/9 for each colour channel. A psychophysical experiment was conducted on an LCD display to find the digital signals corresponding to each relative luminance by visually matching the half-tone background to a uniform colour patch. Both inter- and intra-observer vari- ability for the eight luminance matches in each channel were assessed and the luminance matches proved to be consistent across observers (DE00 < 3.5) and repeatable (DE00 < 2.2). Based on the individual observer judgements, the display opto-electronic transfer function (OETF) was estimated by using either a 3rd order polynomial regression or linear interpolation for each colour channel. The performance of the proposed method is evaluated by predicting the CIE tristimulus values of a set of coloured patches (using the observer-based OETFs) and comparing them to the expected CIE tristimulus values (using the OETF obtained from spectro-radiometric luminance measurements). The resulting colour differences range from 2 to 4.6 DE00. We conclude that this observer-based method of visual gamma correction is useful to estimate the OETF for LCD displays. Its major advantage is that no particular functional relationship between digital inputs and luminance outputs has to be assumed.
Keywords: Display calibration; Psychophysics ; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration
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Kaida Xiao, Sophie Wuerger, Chenyang Fu and Dimosthenis Karatzas. 2011. Unique Hue Data for Colour Appearance Models. Part i: Loci of Unique Hues and Hue Uniformity. CRA, 36(5), 316–323.
Abstract: Psychophysical experiments were conducted to assess unique hues on a CRT display for a large sample of colour-normal observers (n 1⁄4 185). These data were then used to evaluate the most commonly used colour appear- ance model, CIECAM02, by transforming the CIEXYZ tris- timulus values of the unique hues to the CIECAM02 colour appearance attributes, lightness, chroma and hue angle. We report two findings: (1) the hue angles derived from our unique hue data are inconsistent with the commonly used Natural Color System hues that are incorporated in the CIECAM02 model. We argue that our predicted unique hue angles (derived from our large dataset) provide a more reliable standard for colour management applications when the precise specification of these salient colours is im- portant. (2) We test hue uniformity for CIECAM02 in all four unique hues and show significant disagreements for all hues, except for unique red which seems to be invariant under lightness changes. Our dataset is useful to improve the CIECAM02 model as it provides reliable data for benchmarking.
Keywords: unique hues; colour appearance models; CIECAM02; hue uniformity
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Lluis Pere de las Heras and Gemma Sanchez. 2011. And-Or Graph Grammar for Architectural Floorplan Representation, Learning and Recognition. A Semantic, Structural and Hierarchical Model. 5th Iberian Conference on Pattern Recognition and Image Analysis.17–24.
Abstract: This paper presents a syntactic model for architectural floor plan interpretation. A stochastic image grammar over an And-Or graph is inferred to represent the hierarchical, structural and semantic relations between elements of all possible floor plans. This grammar is augmented with three different probabilistic models, learnt from a training set, to account the frequency of that relations. Then, a Bottom-Up/Top-Down parser with a pruning strategy has been used for floor plan recognition. For a given input, the parser generates the most probable parse graph for that document. This graph not only contains the structural and semantic relations of its elements, but also its hierarchical composition, that allows to interpret the floor plan at different levels of abstraction.
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