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	Author	German Ros; J. Guerrero; Angel Sappa; Antonio Lopez
	Title	VSLAM pose initialization via Lie groups and Lie algebras optimization			Type	Conference Article
	Year	2013	Publication	Proceedings of IEEE International Conference on Robotics and Automation	Abbreviated Journal
	Volume		Issue		Pages	5740 - 5747
	Keywords	SLAM
	Abstract	We present a novel technique for estimating initial 3D poses in the context of localization and Visual SLAM problems. The presented approach can deal with noise, outliers and a large amount of input data and still performs in real time in a standard CPU. Our method produces solutions with an accuracy comparable to those produced by RANSAC but can be much faster when the percentage of outliers is high or for large amounts of input data. On the current work we propose to formulate the pose estimation as an optimization problem on Lie groups, considering their manifold structure as well as their associated Lie algebras. This allows us to perform a fast and simple optimization at the same time that conserve all the constraints imposed by the Lie group SE(3). Additionally, we present several key design concepts related with the cost function and its Jacobian; aspects that are critical for the good performance of the algorithm.
	Address	Karlsruhe; Germany; May 2013
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1050-4729	ISBN	978-1-4673-5641-1	Medium
	Area		Expedition		Conference	ICRA
	Notes	ADAS; 600.054; 600.055; 600.057			Approved	no
	Call Number	Admin @ si @ RGS2013a; ADAS @ adas @			Serial	2225
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	Author	Carlos Boned Riera; Oriol Ramos Terrades
	Title	Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph			Type	Conference Article
	Year	2022	Publication	26th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2186-2191
	Keywords	Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition
	Abstract	Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks.
	Address	Montreal; Quebec; Canada; August 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.121; 600.162			Approved	no
	Call Number	Admin @ si @ BoR2022			Serial	3741
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	Author	Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal
	Title	DocEnTr: An End-to-End Document Image Enhancement Transformer			Type	Conference Article
	Year	2022	Publication	26th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1699-1705
	Keywords	Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads
	Abstract	Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR
	Address	August 21-25, 2022 , Montréal Québec
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.121; 600.162; 602.230; 600.140			Approved	no
	Call Number	Admin @ si @ SBJ2022			Serial	3730
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	Author	Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor
	Title	A Few-shot Learning Approach for Historical Encoded Manuscript Recognition			Type	Conference Article
	Year	2021	Publication	25th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	5413-5420
	Keywords
	Abstract	Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition.
	Address	Virtual; January 2021
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.121; 600.140			Approved	no
	Call Number	Admin @ si @ SFK2021			Serial	3449
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	Author	Xialei Liu; Marc Masana; Luis Herranz; Joost Van de Weijer; Antonio Lopez; Andrew Bagdanov
	Title	Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting			Type	Conference Article
	Year	2018	Publication	24th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2262-2268
	Keywords
	Abstract	In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	LAMP; ADAS; 601.305; 601.109; 600.124; 600.106; 602.200; 600.120; 600.118			Approved	no
	Call Number	Admin @ si @ LMH2018			Serial	3160
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	Author	Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados; Umapada Pal
	Title	Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch			Type	Conference Article
	Year	2018	Publication	24th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	916 - 921
	Keywords
	Abstract	In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets.
	Address	Beijing; China; August 2018
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 602.167; 602.168; 600.097; 600.084; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ DDG2018b			Serial	3152
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	Author	Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes
	Title	Learning Graph Distances with Message Passing Neural Networks			Type	Conference Article
	Year	2018	Publication	24th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2239-2244
	Keywords	★Best Paper Award★
	Abstract	Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of error-tolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high computational complexity, which makes it difficult to apply these matching algorithms in a real scenario. In this paper, we propose an efficient graph distance based on the emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach. The performance of the proposed graph distance is validated in two application cases, graph classification and graph retrieval of handwritten words, and shows a promising performance when compared with (approximate) graph edit distance benchmarks.
	Address	Beijing; China; August 2018
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.097; 603.057; 601.302; 600.121			Approved	no
	Call Number	Admin @ si @ RFL2018			Serial	3168
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	Author	Gemma Rotger; Felipe Lumbreras; Francesc Moreno-Noguer; Antonio Agudo
	Title	2D-to-3D Facial Expression Transfer			Type	Conference Article
	Year	2018	Publication	24th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2008 - 2013
	Keywords
	Abstract	Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape –obtained from standard factorization approaches over the input video– using a triangular mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	MSIAU; 600.086; 600.130; 600.118			Approved	no
	Call Number	Admin @ si @ RLM2018			Serial	3232
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	Author	Lu Yu; Yongmei Cheng; Joost Van de Weijer
	Title	Weakly Supervised Domain-Specific Color Naming Based on Attention			Type	Conference Article
	Year	2018	Publication	24th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3019 - 3024
	Keywords
	Abstract	The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains.
	Address	Beijing; August 2018
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	LAMP; 600.109; 602.200; 600.120			Approved	no
	Call Number	Admin @ si @ YCW2018			Serial	3243
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	Author	Jiaolong Xu; Sebastian Ramos;David Vazquez; Antonio Lopez
	Title	Cost-sensitive Structured SVM for Multi-category Domain Adaptation			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3886 - 3891
	Keywords	Domain Adaptation; Pedestrian Detection
	Abstract	Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition.
	Address	Stockholm; Sweden; August 2014
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	ADAS; 600.057; 600.054; 601.217; 600.076			Approved	no
	Call Number	ADAS @ adas @ XRV2014a			Serial	2434
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	Author	Francisco Cruz; Oriol Ramos Terrades
	Title	EM-Based Layout Analysis Method for Structured Documents			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	315-320
	Keywords
	Abstract	In this paper we present a method to perform layout analysis in structured documents. We proposed an EM-based algorithm to fit a set of Gaussian mixtures to the different regions according to the logical distribution along the page. After the convergence, we estimate the final shape of the regions according to the parameters computed for each component of the mixture. We evaluated our method in the task of record detection in a collection of historical structured documents and performed a comparison with other previous works in this task.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 602.006; 600.061; 600.077			Approved	no
	Call Number	Admin @ si @ CrR2014			Serial	2530
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	Author	Lluis Gomez; Dimosthenis Karatzas
	Title	MSER-based Real-Time Text Detection and Tracking			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3110 - 3115
	Keywords
	Abstract	We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization. A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices.
	Address	Stockholm; August 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.056; 601.158; 601.197; 600.077			Approved	no
	Call Number	Admin @ si @ GoK2014a			Serial	2492
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	Author	P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes
	Title	A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3074 - 3079
	Keywords	word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance
	Abstract	Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy.
	Address	Stockholm; Sweden; August 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ WEG2014a			Serial	2515
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	Author	David Fernandez; Jon Almazan; Nuria Cirera; Alicia Fornes; Josep Llados
	Title	BH2M: the Barcelona Historical Handwritten Marriages database			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	256 - 261
	Keywords
	Abstract	This paper presents an image database of historical handwritten marriages records stored in the archives of Barcelona cathedral, and the corresponding meta-data addressed to evaluate the performance of document analysis algorithms. The contribution of this paper is twofold. First, it presents a complete ground truth which covers the whole pipeline of handwriting recognition research, from layout analysis to recognition and understanding. Second, it is the first dataset in the emerging area of genealogical document analysis, where documents are manuscripts pseudo-structured with specific lexicons and the interest is beyond pure transcriptions but context dependent.
	Address	Creete Island; Grecia; September 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.056; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ FAC2014			Serial	2461
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	Author	Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados
	Title	Embedding Document Structure to Bag-of-Words through Pair-wise Stable Key-regions			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2903 - 2908
	Keywords
	Abstract	Since the document structure carries valuable discriminative information, plenty of efforts have been made for extracting and understanding document structure among which layout analysis approaches are the most commonly used. In this paper, Distance Transform based MSER (DTMSER) is employed to efficiently extract the document structure as a dendrogram of key-regions which roughly correspond to structural elements such as characters, words and paragraphs. Inspired by the Bag of Words (BoW) framework, we propose an efficient method for structural document matching by representing the document image as a histogram of key-region pairs encoding structural relationships. Applied to the scenario of document image retrieval, experimental results demonstrate a remarkable improvement when comparing the proposed method with typical BoW and pyramidal BoW methods.
	Address	Stockholm; Sweden; August 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.056; 600.061; 600.077			Approved	no
	Call Number	Admin @ si @ GRK2014b			Serial	2497
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