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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Jean-Marc Ogier, Wenyin Liu, & Josep Llados (Eds.). (2010). Graphics Recognition: Achievements, Challenges, and Evolution (Vol. 6020). LNCS. Springer Link.
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Joan Mas, Gemma Sanchez, & Josep Llados. (2010). SSP: Sketching slide Presentations, a Syntactic Approach. In Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers (Vol. 6020, pp. 118–129). LNCS. Springer Berlin Heidelberg.
Abstract: The design of a slide presentation is a creative process. In this process first, humans visualize in their minds what they want to explain. Then, they have to be able to represent this knowledge in an understandable way. There exists a lot of commercial software that allows to create our own slide presentations but the creativity of the user is rather limited. In this article we present an application that allows the user to create and visualize a slide presentation from a sketch. A slide may be seen as a graphical document or a diagram where its elements are placed in a particular spatial arrangement. To describe and recognize slides a syntactic approach is proposed. This approach is based on an Adjacency Grammar and a parsing methodology to cope with this kind of grammars. The experimental evaluation shows the performance of our methodology from a qualitative and a quantitative point of view. Six different slides containing different number of symbols, from 4 to 7, have been given to the users and they have drawn them without restrictions in the order of the elements. The quantitative results give an idea on how suitable is our methodology to describe and recognize the different elements in a slide.
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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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Christophe Rigaud, Dimosthenis Karatzas, Jean-Christophe Burie, & Jean-Marc Ogier. (2013). Speech balloon contour classification in comics. In 10th IAPR International Workshop on Graphics Recognition.
Abstract: Comic books digitization combined with subsequent comic book understanding create a variety of new applications, including mobile reading and data mining. Document understanding in this domain is challenging as comics are semi-structured documents, combining semantically important graphical and textual parts. In this work we detail a novel approach for classifying speech balloon in scanned comics book pages based on their contour time series.
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Lluis Pere de las Heras, David Fernandez, Alicia Fornes, Ernest Valveny, Gemma Sanchez, & Josep Llados. (2013). Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans. In 10th IAPR International Workshop on Graphics Recognition.
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Lluis Pere de las Heras, Ernest Valveny, & Gemma Sanchez. (2013). Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies. In 10th IAPR International Workshop on Graphics Recognition.
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Pau Riba, Alicia Fornes, & Josep Llados. (2015). Towards the Alignment of Handwritten Music Scores. In Bart Lamiroy, & Rafael Dueire Lins (Eds.), 11th IAPR International Workshop on Graphics Recognition. LNCS. Springer International Publishing.
Abstract: It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study. This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such differences. Given the difficulties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the staff lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
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Hana Jarraya, Muhammad Muzzamil Luqman, & Jean-Yves Ramel. (2017). Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition. In B. Lamiroy, & R Dueire Lins (Eds.), Graphics Recognition. Current Trends and Challenges (Vol. 9657). LNCS. Springer.
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Hana Jarraya, Oriol Ramos Terrades, & Josep Llados. (2017). Learning structural loss parameters on graph embedding applied on symbolic graphs. In 12th IAPR International Workshop on Graphics Recognition.
Abstract: We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset.
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Sounak Dey, Anjan Dutta, Josep Llados, Alicia Fornes, & Umapada Pal. (2017). Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario. In 12th IAPR International Workshop on Graphics Recognition (pp. 31–32).
Abstract: One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with
very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration
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Pau Riba, Anjan Dutta, Josep Llados, & Alicia Fornes. (2017). Graph-based deep learning for graphics classification. In 12th IAPR International Workshop on Graphics Recognition (pp. 29–30).
Abstract: Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and
we show how they can be used in graphics recognition problems
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Adria Rico, & Alicia Fornes. (2017). Camera-based Optical Music Recognition using a Convolutional Neural Network. In 12th IAPR International Workshop on Graphics Recognition (pp. 27–28).
Abstract: Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results
Keywords: optical music recognition; document analysis; convolutional neural network; deep learning
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Arnau Baro, Pau Riba, Jorge Calvo-Zaragoza, & Alicia Fornes. (2018). Optical Music Recognition by Long Short-Term Memory Networks. In B. L. A. Fornes (Ed.), Graphics Recognition. Current Trends and Evolutions (Vol. 11009, pp. 81–95). LNCS. Springer.
Abstract: Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach.
Keywords: Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory
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Asma Bensalah, Pau Riba, Alicia Fornes, & Josep Llados. (2019). Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning. In 13th IAPR International Workshop on Graphics Recognition (pp. 80–85).
Abstract: With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of
sketches, showing promising results.
Keywords: Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning
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