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Mathieu Nicolas Delalandre, Jean-Yves Ramel, Ernest Valveny, & Muhammad Muzzamil Luqman. (2010). A Performance Characterization Algorithm for Symbol Localization. In Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers (Vol. 6020, 260–271). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols).
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Marçal Rusiñol, K. Bertet, Jean-Marc Ogier, & Josep Llados. (2010). Symbol Recognition Using a Concept Lattice of Graphical Patterns. In Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers (Vol. 6020, pp. 187–198). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we propose a new approach to recognize symbols by the use of a concept lattice. We propose to build a concept lattice in terms of graphical patterns. Each model symbol is decomposed in a set of composing graphical patterns taken as primitives. Each one of these primitives is described by boundary moment invariants. The obtained concept lattice relates which symbolic patterns compose a given graphical symbol. A Hasse diagram is derived from the context and is used to recognize symbols affected by noise. We present some preliminary results over a variation of the dataset of symbols from the GREC 2005 symbol recognition contest.
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Partha Pratim Roy, Umapada Pal, & Josep Llados. (2010). Touching Text Character Localization in Graphical Documents using SIFT. In Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers (Vol. 6020, pp. 199–211). LNCS. Springer Berlin Heidelberg.
Abstract: Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text and symbol overlapped/touched. Intersection of text and symbols with graphical lines and curves occur frequently in graphical documents and hence separation of such symbols is very difficult.
Several pattern recognition and classification techniques exist to recognize isolated text/symbol. But, the touching/overlapping text and symbol recognition has not yet been dealt successfully. An interesting technique, Scale Invariant Feature Transform (SIFT), originally devised for object recognition can take care of overlapping problems. Even if SIFT features have emerged as a very powerful object descriptors, their employment in graphical documents context has not been investigated much. In this paper we present the adaptation of the SIFT approach in the context of text character localization (spotting) in graphical documents. We evaluate the applicability of this technique in such documents and discuss the scope of improvement by combining some state-of-the-art approaches.
Keywords: Support Vector Machine; Text Component; Graphical Line; Document Image; Scale Invariant Feature Transform
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Miquel Ferrer, I. Bardaji, Ernest Valveny, Dimosthenis Karatzas, & Horst Bunke. (2013). Median Graph Computation by Means of Graph Embedding into Vector Spaces. In Yun Fu, & Yungian Ma (Eds.), Graph Embedding for Pattern Analysis (pp. 45–72). Springer New York.
Abstract: In pattern recognition [8, 14], a key issue to be addressed when designing a system is how to represent input patterns. Feature vectors is a common option. That is, a set of numerical features describing relevant properties of the pattern are computed and arranged in a vector form. The main advantages of this kind of representation are computational simplicity and a well sound mathematical foundation. Thus, a large number of operations are available to work with vectors and a large repository of algorithms for pattern analysis and classification exist. However, the simple structure of feature vectors might not be the best option for complex patterns where nonnumerical features or relations between different parts of the pattern become relevant.
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A.Kesidis, & Dimosthenis Karatzas. (2014). Logo and Trademark Recognition. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. D, pp. 591–646). Springer London.
Abstract: The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images.
Keywords: Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems
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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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Juan Ramon Terven Salinas, Joaquin Salas, & Bogdan Raducanu. (2014). Robust Head Gestures Recognition for Assistive Technology. In Pattern Recognition (Vol. 8495, pp. 152–161). LNCS. Springer International Publishing.
Abstract: This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.
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C. Alejandro Parraga. (2014). Color Vision, Computational Methods for. In Dieter Jaeger, & Ranu Jung (Eds.), Encyclopedia of Computational Neuroscience (pp. 1–11). Springer-Verlag Berlin Heidelberg.
Abstract: The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments.
Keywords: Color computational vision; Computational neuroscience of color
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Svebor Karaman, Giuseppe Lisanti, Andrew Bagdanov, & Alberto del Bimbo. (2014). From re-identification to identity inference: Labeling consistency by local similarity constraints. In Person Re-Identification (Vol. 2, pp. 287–307). Springer London.
Abstract: In this chapter, we introduce the problem of identity inference as a generalization of person re-identification. It is most appropriate to distinguish identity inference from re-identification in situations where a large number of observations must be identified without knowing a priori that groups of test images represent the same individual. The standard single- and multishot person re-identification common in the literature are special cases of our formulation. We present an approach to solving identity inference by modeling it as a labeling problem in a Conditional Random Field (CRF). The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space. Experimental results are given on the ETHZ, i-LIDS and CAVIAR datasets. Our approach yields state-of-the-art performance for multishot re-identification, and our results on the more general identity inference problem demonstrate that we are able to infer the identity of very many examples even with very few labeled images in the gallery.
Keywords: re-identification; Identity inference; Conditional random fields; Video surveillance
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Lluis Pere de las Heras, Ernest Valveny, & Gemma Sanchez. (2014). Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies. In Graphics Recognition. Current Trends and Challenges (Vol. 8746, pp. 109–121). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we present a wall segmentation approach in floor plans that is able to work independently to the graphical notation, does not need any pre-annotated data for learning, and is able to segment multiple-shaped walls such as beams and curved-walls. This method results from the combination of the wall segmentation approaches [3, 5] presented recently by the authors. Firstly, potential straight wall segments are extracted in an unsupervised way similar to [3], but restricting even more the wall candidates considered in the original approach. Then, based on [5], these segments are used to learn the texture pattern of walls and spot the lost instances. The presented combination of both methods has been tested on 4 available datasets with different notations and compared qualitatively and quantitatively to the state-of-the-art applied on these collections. Additionally, some qualitative results on floor plans directly downloaded from the Internet are reported in the paper. The overall performance of the method demonstrates either its adaptability to different wall notations and shapes, and to document qualities and resolutions.
Keywords: Graphics recognition; Floor plan analysis; Object segmentation
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Lluis Pere de las Heras, David Fernandez, Alicia Fornes, Ernest Valveny, Gemma Sanchez, & Josep Llados. (2014). Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans. In Graphics Recognition. Current Trends and Challenges (Vol. 8746, pp. 135–146). LNCS. Springer Berlin Heidelberg.
Abstract: This paper proposes a runlength histogram signature as a perceptual 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. Additional retrieval results on sketched building’s facades are reported qualitatively in this paper. Its good description and its adaptability to two different sketch drawings despite its simplicity shows the interest of the proposed approach and opens a challenging research line in graphics recognition.
Keywords: Graphics recognition; Graphics retrieval; Image classification
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Alicia Fornes, V.C.Kieu, M. Visani, N.Journet, & Anjan Dutta. (2014). The ICDAR/GREC 2013 Music Scores Competition: Staff Removal. In B.Lamiroy, & J.-M. Ogier (Eds.), Graphics Recognition. Current Trends and Challenges (Vol. 8746, pp. 207–220). LNCS. Springer Berlin Heidelberg.
Abstract: The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations.
Keywords: Competition; Graphics recognition; Music scores; Writer identification; Staff removal
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C. Alejandro Parraga. (2015). Perceptual Psychophysics. In G.Cristobal, M.Keil, & L.Perrinet (Eds.), Biologically-Inspired Computer Vision: Fundamentals and Applications.
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Jorge Bernal, F. Javier Sanchez, Cristina Rodriguez de Miguel, & Gloria Fernandez Esparrach. (2015). Bulding up the future of colonoscopy: A synergy between clinicians and computer scientists. In Colonoscopy and Colorectal Cancer.
Abstract: Recent advances in endoscopic technology have generated an increasing interest in strengthening the collaboration between clinicians and computers scientist to develop intelligent systems that can provide additional information to clinicians in the different stages of an intervention. The objective of this chapter is to identify clinical drawbacks of colonoscopy in order to define potential areas of collaboration. Once areas are defined, we present the challenges that colonoscopy images present in order computational methods to provide with meaningful output, including those related to image formation and acquisition, as they are proven to have an impact in the performance of an intelligent system. Finally, we also propose how to define validation frameworks in order to assess the performance of a given method, making an special emphasis on how databases should be created and annotated and which metrics should be used to evaluate systems correctly.
Keywords: Intelligent systems; Image properties; Validation; Clinical drawbacks; Endoluminal scene description
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Klaus Broelemann, Anjan Dutta, Xiaoyi Jiang, & Josep Llados. (2014). Hierarchical Plausibility-Graphs for Symbol Spotting in Graphical Documents. In Bart Lamiroy, & Jean-Marc Ogier (Eds.), Graphics Recognition. Current Trends and Challenges (Vol. 8746, pp. 25–37). LNCS. Springer Berlin Heidelberg.
Abstract: Graph representation of graphical documents often suffers from noise such as spurious nodes and edges, and their discontinuity. In general these errors occur during the low-level image processing viz. binarization, skeletonization, vectorization etc. Hierarchical graph representation is a nice and efficient way to solve this kind of problem by hierarchically merging node-node and node-edge depending on the distance. But the creation of hierarchical graph representing the graphical information often uses hard thresholds on the distance to create the hierarchical nodes (next state) of the lower nodes (or states) of a graph. As a result, the representation often loses useful information. This paper introduces plausibilities to the nodes of hierarchical graph as a function of distance and proposes a modified algorithm for matching subgraphs of the hierarchical graphs. The plausibility-annotated nodes help to improve the performance of the matching algorithm on two hierarchical structures. To show the potential of this approach, we conduct an experiment with the SESYD dataset.
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