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Author | Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados | ||||
Title | Hierarchical graph representation for symbol spotting in graphical document images | Type | Conference Article | ||
Year | 2012 | Publication | Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop | Abbreviated Journal | |
Volume | 7626 | Issue | Pages | 529-538 | |
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Abstract | Symbol spotting can be defined as locating given query symbol in a large collection of graphical documents. In this paper we present a hierarchical graph representation for symbols. This representation allows graph matching methods to deal with low-level vectorization errors and, thus, to perform a robust symbol spotting. To show the potential of this approach, we conduct an experiment with the SESYD dataset. | ||||
Address | Miyajima-Itsukushima, Hiroshima | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-34165-6 | Medium | |
Area | Expedition | Conference | SSPR&SPR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ BDJ2012 | Serial | 2126 | ||
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Author | Jaume Gibert; Ernest Valveny; Horst Bunke; Alicia Fornes | ||||
Title | On the Correlation of Graph Edit Distance and L1 Distance in the Attribute Statistics Embedding Space | Type | Conference Article | ||
Year | 2012 | Publication | Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop | Abbreviated Journal | |
Volume | 7626 | Issue | Pages | 135-143 | |
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Abstract | Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected. | ||||
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Publisher | Springer-Berlag, Berlin | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-642-34165-6 | Medium | ||
Area | Expedition | Conference | SSPR&SPR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ GVB2012c | Serial | 2167 | ||
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Author | Fadi Dornaika; A.Assoum; Bogdan Raducanu | ||||
Title | Automatic Dimensionality Estimation for Manifold Learning through Optimal Feature Selection | Type | Conference Article | ||
Year | 2012 | Publication | Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop | Abbreviated Journal | |
Volume | 7626 | Issue | Pages | 575-583 | |
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Abstract | A very important aspect in manifold learning is represented by automatic estimation of the intrinsic dimensionality. Unfortunately, this problem has received few attention in the literature of manifold learning. In this paper, we argue that feature selection paradigm can be used to the problem of automatic dimensionality estimation. Besides this, it also leads to improved recognition rates. Our approach for optimal feature selection is based on a Genetic Algorithm. As a case study for manifold learning, we have considered Laplacian Eigenmaps (LE) and Locally Linear Embedding (LLE). The effectiveness of the proposed framework was tested on the face recognition problem. Extensive experiments carried out on ORL, UMIST, Yale, and Extended Yale face data sets confirmed our hypothesis. | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-34165-6 | Medium | |
Area | Expedition | Conference | SSPR&SPR | ||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ DAR2012 | Serial | 2174 | ||
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Author | Bogdan Raducanu; Fadi Dornaika | ||||
Title | Out-of-Sample Embedding by Sparse Representation | Type | Conference Article | ||
Year | 2012 | Publication | Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop | Abbreviated Journal | |
Volume | 7626 | Issue | Pages | 336-344 | |
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Abstract | A critical aspect of non-linear dimensionality reduction techniques is represented by the construction of the adjacency graph. The difficulty resides in finding the optimal parameters, a process which, in general, is heuristically driven. Recently, sparse representation has been proposed as a non-parametric solution to overcome this problem. In this paper, we demonstrate that this approach not only serves for the graph construction, but also represents an efficient and accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. Experimental results conducted on some challenging datasets confirmed the robustness of our approach and its superiority when compared to existing techniques. | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-34165-6 | Medium | |
Area | Expedition | Conference | SSPR&SPR | ||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ RaD2012c | Serial | 2175 | ||
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Author | Miguel Angel Bautista; Xavier Baro; Oriol Pujol; Petia Radeva; Jordi Vitria; Sergio Escalera | ||||
Title | Compact Evolutive Design of Error-Correcting Output Codes | Type | Conference Article | ||
Year | 2010 | Publication | Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | Abbreviated Journal | |
Volume | Issue | Pages | 119-128 | ||
Keywords | Ensemble of Dichotomizers; Error-Correcting Output Codes; Evolutionary optimization | ||||
Abstract | The classication of large number of object categories is a challenging trend in the Machine Learning eld. In literature, this is often addressed using an ensemble of classiers. In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for the combination of classiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classiers. Evolutionary computation is used for tuning the parameters of the classiers and looking for the best Minimal ECOC code conguration. The results over several public UCI data sets and a challenging multi-class Computer Vision problem show that the proposed methodology obtains comparable and even better results than state-of-the-art ECOC methodologies with far less number of dichotomizers. | ||||
Address | Barcelona (Spain) | ||||
Corporate Author | Thesis | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | SUEMA | ||
Notes | OR;MILAB;HUPBA;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ BBP2010 | Serial | 1363 | ||
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Author | Pierluigi Casale; Oriol Pujol; Petia Radeva | ||||
Title | Embedding Random Projections in Regularized Gradient Boosting Machines | Type | Conference Article | ||
Year | 2010 | Publication | Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | Abbreviated Journal | |
Volume | Issue | Pages | 44–53 | ||
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Address | Barcelona (Spain) | ||||
Corporate Author | Thesis | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | SUEMA | ||
Notes | MILAB;HUPBA | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ CPR2010c | Serial | 1466 | ||
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Author | Ana Garcia Rodriguez; Jorge Bernal; F. Javier Sanchez; Henry Cordova; Rodrigo Garces Duran; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach | ||||
Title | Polyp fingerprint: automatic recognition of colorectal polyps’ unique features | Type | Journal Article | ||
Year | 2020 | Publication | Surgical Endoscopy and other Interventional Techniques | Abbreviated Journal | SEND |
Volume | 34 | Issue | 4 | Pages | 1887-1889 |
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Abstract | BACKGROUND:
Content-based image retrieval (CBIR) is an application of machine learning used to retrieve images by similarity on the basis of features. Our objective was to develop a CBIR system that could identify images containing the same polyp ('polyp fingerprint'). METHODS: A machine learning technique called Bag of Words was used to describe each endoscopic image containing a polyp in a unique way. The system was tested with 243 white light images belonging to 99 different polyps (for each polyp there were at least two images representing it in two different temporal moments). Images were acquired in routine colonoscopies at Hospital Clínic using high-definition Olympus endoscopes. The method provided for each image the closest match within the dataset. RESULTS: The system matched another image of the same polyp in 221/243 cases (91%). No differences were observed in the number of correct matches according to Paris classification (protruded: 90.7% vs. non-protruded: 91.3%) and size (< 10 mm: 91.6% vs. > 10 mm: 90%). CONCLUSIONS: A CBIR system can match accurately two images containing the same polyp, which could be a helpful aid for polyp image recognition. KEYWORDS: Artificial intelligence; Colorectal polyps; Content-based image retrieval |
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Notes | MV; no menciona | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3403 | ||
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Author | Marçal Rusiñol; Josep Llados | ||||
Title | Symbol Spotting in Digital Libraries:Focused Retrieval over Graphic-rich Document Collections | Type | Book Whole | ||
Year | 2010 | Publication | Symbol Spotting in Digital Libraries:Focused Retrieval over Graphic-rich Document Collections | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Focused Retrieval , Graphical Pattern Indexation,Graphics Recognition ,Pattern Recognition , Performance Evaluation , Symbol Description ,Symbol Spotting | ||||
Abstract | The specific problem of symbol recognition in graphical documents requires additional techniques to those developed for character recognition. The most well-known obstacle is the so-called Sayre paradox: Correct recognition requires good segmentation, yet improvement in segmentation is achieved using information provided by the recognition process. This dilemma can be avoided by techniques that identify sets of regions containing useful information. Such symbol-spotting methods allow the detection of symbols in maps or technical drawings without having to fully segment or fully recognize the entire content.
This unique text/reference provides a complete, integrated and large-scale solution to the challenge of designing a robust symbol-spotting method for collections of graphic-rich documents. The book examines a number of features and descriptors, from basic photometric descriptors commonly used in computer vision techniques to those specific to graphical shapes, presenting a methodology which can be used in a wide variety of applications. Additionally, readers are supplied with an insight into the problem of performance evaluation of spotting methods. Some very basic knowledge of pattern recognition, document image analysis and graphics recognition is assumed. |
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Publisher | Springer | Place of Publication | Editor | ||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-1-84996-208-7 | Medium | ||
Area | Expedition | Conference | |||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ RuL2010a | Serial | 1292 | ||
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Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
Title | Document noise removal using sparse representations over learned dictionary | Type | Conference Article | ||
Year | 2013 | Publication | Symposium on Document engineering | Abbreviated Journal | |
Volume | Issue | Pages | 161-168 | ||
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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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Address | Barcelona; October 2013 | ||||
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ISSN | ISBN | 978-1-4503-1789-4 | Medium | ||
Area | Expedition | Conference | ACM-DocEng | ||
Notes | DAG; 600.061 | Approved | no | ||
Call Number | Admin @ si @ DTR2013a | Serial | 2330 | ||
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Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li | ||||
Title | Multi-modal Face Presentation Attach Detection | Type | Book Whole | ||
Year | 2020 | Publication | Synthesis Lectures on Computer Vision | Abbreviated Journal | |
Volume | 13 | Issue | Pages | ||
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Notes | HuPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2020 | Serial | 3440 | ||
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Author | Michael Teutsch; Angel Sappa; Riad I. Hammoud | ||||
Title | Computer Vision in the Infrared Spectrum: Challenges and Approaches | Type | Book Whole | ||
Year | 2021 | Publication | Synthesis Lectures on Computer Vision | Abbreviated Journal | |
Volume | 10 | Issue | 2 | Pages | 1-138 |
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Abstract | Human visual perception is limited to the visual-optical spectrum. Machine vision is not. Cameras sensitive to the different infrared spectra can enhance the abilities of autonomous systems and visually perceive the environment in a holistic way. Relevant scene content can be made visible especially in situations, where sensors of other modalities face issues like a visual-optical camera that needs a source of illumination. As a consequence, not only human mistakes can be avoided by increasing the level of automation, but also machine-induced errors can be reduced that, for example, could make a self-driving car crash into a pedestrian under difficult illumination conditions. Furthermore, multi-spectral sensor systems with infrared imagery as one modality are a rich source of information and can provably increase the robustness of many autonomous systems. Applications that can benefit from utilizing infrared imagery range from robotics to automotive and from biometrics to surveillance. In this book, we provide a brief yet concise introduction to the current state-of-the-art of computer vision and machine learning in the infrared spectrum. Based on various popular computer vision tasks such as image enhancement, object detection, or object tracking, we first motivate each task starting from established literature in the visual-optical spectrum. Then, we discuss the differences between processing images and videos in the visual-optical spectrum and the various infrared spectra. An overview of the current literature is provided together with an outlook for each task. Furthermore, available and annotated public datasets and common evaluation methods and metrics are presented. In a separate chapter, popular applications that can greatly benefit from the use of infrared imagery as a data source are presented and discussed. Among them are automatic target recognition, video surveillance, or biometrics including face recognition. Finally, we conclude with recommendations for well-fitting sensor setups and data processing algorithms for certain computer vision tasks. We address this book to prospective researchers and engineers new to the field but also to anyone who wants to get introduced to the challenges and the approaches of computer vision using infrared images or videos. Readers will be able to start their work directly after reading the book supported by a highly comprehensive backlog of recent and relevant literature as well as related infrared datasets including existing evaluation frameworks. Together with consistently decreasing costs for infrared cameras, new fields of application appear and make computer vision in the infrared spectrum a great opportunity to face nowadays scientific and engineering challenges. | ||||
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ISSN | ISBN | 978-1636392431 | Medium | ||
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Notes | MSIAU | Approved | no | ||
Call Number | Admin @ si @ TSH2021 | Serial | 3666 | ||
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Author | Jordi Vitria; J. Llacer | ||||
Title | Recovering Depth from Focus Using Iterative image Estimation Techniques. | Type | Miscellaneous | ||
Year | 1993 | Publication | Tech.Report BL–35158, Lawrence Berkeley Laboratory. | Abbreviated Journal | |
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Address | University of California; Berkeley; USA; | ||||
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Notes | OR;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ ViL1993 | Serial | 142 | ||
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Author | Jaume Garcia | ||||
Title | Propagacio de fronts per a la segmentacio en imatges IVUS | Type | Report | ||
Year | 2002 | Publication | Technical Report | Abbreviated Journal | |
Volume | Issue | 65 | Pages | ||
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Address | CVC (UAB) | ||||
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Notes | IAM | Approved | no | ||
Call Number | IAM @ iam @ Gar2002 | Serial | 328 | ||
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Author | Ole Larsen; Petia Radeva; Enric Marti | ||||
Title | Calculating the Bounds on the Optimal Parameters of Elasticity for a Snake | Type | Report | ||
Year | 1994 | Publication | Technical Report | Abbreviated Journal | |
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Address | Aalborg University | ||||
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Publisher | Aalborg University, Laboratory of image Analysis. | Place of Publication | Denmark | Editor | |
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Area | Aalborg University, Laboratory of image Analysis. | Expedition | Conference | ||
Notes | MILAB;IAM | Approved | no | ||
Call Number | IAM @ iam @ LRM1994 | Serial | 1560 | ||
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Author | Javier Varona; Juan J. Villanueva | ||||
Title | Neural networks as spatial filters for image processing: Neurofilters | Type | Report | ||
Year | 1996 | Publication | Technical Report #07 | Abbreviated Journal | |
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Address | CVC (UAB) | ||||
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Notes | Approved | no | |||
Call Number | ISE @ ise @ VaV1996 | Serial | 95 | ||
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