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Pau Riba, Josep Llados, Alicia Fornes, & Anjan Dutta. (2015). Large-scale Graph Indexing using Binary Embeddings of Node Contexts. In C.-L.Liu, B.Luo, W.G.Kropatsch, & J.Cheng (Eds.), 10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition (Vol. 9069, pp. 208–217). LNCS. Springer International Publishing.
Abstract: Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. Each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents.
Keywords: Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding
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Juan Ignacio Toledo, Jordi Cucurull, Jordi Puiggali, Alicia Fornes, & Josep Llados. (2015). Document Analysis Techniques for Automatic Electoral Document Processing: A Survey. In E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 (pp. 139–141). LNCS.
Abstract: In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents.
Keywords: Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally
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Gloria Fernandez Esparrach, Jorge Bernal, Cristina Rodriguez de Miguel, Debora Gil, Fernando Vilariño, Henry Cordova, et al. (2015). Colonic polyps are correctly identified by a computer vision method using wm-dova energy maps. In Proceedings of 23 United European- UEG Week 2015.
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Olivier Lefebvre, Pau Riba, Charles Fournier, Alicia Fornes, Josep Llados, Rejean Plamondon, et al. (2015). Monitoring neuromotricity on-line: a cloud computing approach. In 17th Conference of the International Graphonomics Society IGS2015.
Abstract: The goal of our experiment is to develop a useful and accessible tool that can be used to evaluate a patient's health by analyzing handwritten strokes. We use a cloud computing approach to analyze stroke data sampled on a commercial tablet working on the Android platform and a distant server to perform complex calculations using the Delta and Sigma lognormal algorithms. A Google Drive account is used to store the data and to ease the development of the project. The communication between the tablet, the cloud and the server is encrypted to ensure biomedical information confidentiality. Highly parameterized biomedical tests are implemented on the tablet as well as a free drawing test to evaluate the validity of the data acquired by the first test compared to the second one. A blurred shape model descriptor pattern recognition algorithm is used to classify the data obtained by the free drawing test. The functions presented in this paper are still currently under development and other improvements are needed before launching the application in the public domain.
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Youssef El Rhabi, Simon Loic, & Brun Luc. (2015). Estimation de la pose d’une caméra à partir d’un flux vidéo en s’approchant du temps réel. In 15ème édition d'ORASIS, journées francophones des jeunes chercheurs en vision par ordinateur ORASIS2015.
Abstract: Finding a way to estimate quickly and robustly the pose of an image is essential in augmented reality. Here we will discuss the approach we chose in order to get closer to real time by using SIFT points [4]. We propose a method based on filtering both SIFT points and images on which to focus on. Hence we will focus on relevant data.
Keywords: Augmented Reality; SFM; SLAM; real time pose computation; 2D/3D registration
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J.Poujol, Cristhian A. Aguilera-Carrasco, E.Danos, Boris X. Vintimilla, Ricardo Toledo, & Angel Sappa. (2015). Visible-Thermal Fusion based Monocular Visual Odometry. In 2nd Iberian Robotics Conference ROBOT2015 (Vol. 417, pp. 517–528). Springer International Publishing.
Abstract: The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
Keywords: Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion.
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Mariella Dimiccoli, & Petia Radeva. (2015). Lifelogging in the era of outstanding digitization. In International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage.
Abstract: In this paper, we give an overview on the emerging trend of the digitized self, focusing on visual lifelogging through wearable cameras. This is about continuously recording our life from a first-person view by wearing a camera that passively captures images. On one hand, visual lifelogging has opened the door to a large number of applications, including health. On the other, it has also boosted new challenges in the field of data analysis as well as new ethical concerns. While currently increasing efforts are being devoted to exploit lifelogging data for the improvement of personal well-being, we believe there are still many interesting applications to explore, ranging from tourism to the digitization of human behavior.
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Firat Ismailoglu, Ida G. Sprinkhuizen-Kuyper, Evgueni Smirnov, Sergio Escalera, & Ralf Peeters. (2015). Fractional Programming Weighted Decoding for Error-Correcting Output Codes. In Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 (pp. 38–50). Springer International Publishing.
Abstract: In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments.
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