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	Author	Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta
	Title	Structure-preserving smoothing of biomedical images			Type	Journal Article
	Year	2011	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	44	Issue	9	Pages	1842-1851
	Keywords	Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography
	Abstract	Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images.
	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	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM; ADAS			Approved	no
	Call Number	IAM @ iam @ GHB2011			Serial	1526
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	Author	Palaiahnakote Shivakumara; Anjan Dutta; Trung Quy Phan; Chew Lim Tan; Umapada Pal
	Title	A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification in Video			Type	Journal Article
	Year	2011	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	44	Issue	8	Pages	1671-1683
	Keywords
	Abstract	In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels.
	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
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ SDP2011			Serial	1727
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	Author	Carlo Gatta; Eloi Puertas; Oriol Pujol
	Title	Multi-Scale Stacked Sequential Learning			Type	Journal Article
	Year	2011	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	44	Issue	10-11	Pages	2414-2416
	Keywords	Stacked sequential learning; Multiscale; Multiresolution; Contextual classification
	Abstract	One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB;HuPBA			Approved	no
	Call Number	Admin @ si @ GPP2011			Serial	1802
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	Author	Partha Pratim Roy; Umapada Pal; Josep Llados
	Title	Document Seal Detection Using Ght and Character Proximity Graphs			Type	Journal Article
	Year	2011	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	44	Issue	6	Pages	1282-1295
	Keywords	Seal recognition; Graphical symbol spotting; Generalized Hough transform; Multi-oriented character recognition
	Abstract	This paper deals with automatic detection of seal (stamp) from documents with cluttered background. Seal detection involves a difficult challenge due to its multi-oriented nature, arbitrary shape, overlapping of its part with signature, noise, etc. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors computed from recognition result of individual connected components (characters). Scale and rotation invariant features are used in a Support Vector Machine (SVM) classifier to recognize multi-scale and multi-oriented text characters. The concept of generalized Hough transform (GHT) is used to detect the seal and a voting scheme is designed for finding possible location of the seal in a document based on the spatial feature descriptor of neighboring component pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal in a document. Experiment is performed in an archive of historical documents of handwritten/printed English text. Experimental results show that the method is robust in locating seal instances of arbitrary shape and orientation in documents, and also efficient in indexing a collection of documents for retrieval purposes.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	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
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ RPL2011			Serial	1820
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	Author	Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva
	Title	Occlusion handling via random subspace classifiers for human detection			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Systems, Man, and Cybernetics (Part B)	Abbreviated Journal	TSMCB
	Volume	44	Issue	3	Pages	342-354
	Keywords	Pedestriand Detection; occlusion handling
	Abstract	This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes
	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	2168-2267	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076			Approved	no
	Call Number	ADAS @ adas @ MVL2014			Serial	2213
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	Author	Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez
	Title	Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Systems, Man and Cybernetics (Part B)	Abbreviated Journal	TSMCB
	Volume	44	Issue	12	Pages	2379-2390
	Keywords
	Abstract	The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios.
	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	2168-2267	ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA; ISE; 600.078;MILAB			Approved	no
	Call Number	Admin @ si @ LRE2014			Serial	2442
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	Author	Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl
	Title	Decryption of historical manuscripts: the DECRYPT project			Type	Journal Article
	Year	2020	Publication	Cryptologia	Abbreviated Journal	CRYPT
	Volume	44	Issue	6	Pages	545-559
	Keywords	automatic decryption; cipher collection; historical cryptology; image transcription
	Abstract	Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology.
	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
	Notes	DAG; 600.140; 600.121			Approved	no
	Call Number	Admin @ si @ MEF2020			Serial	3347
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	Author	Mohamed Ali Souibgui; Y.Kessentini
	Title	DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement			Type	Journal Article
	Year	2022	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	44	Issue	3	Pages	1180-1191
	Keywords
	Abstract	Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems.
	Address	1 March 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
	Notes	DAG; 602.230; 600.121; 600.140			Approved	no
	Call Number	Admin @ si @ SoK2022			Serial	3454
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	Author	Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez
	Title	Discriminative Compact Pyramids for Object and Scene Recognition			Type	Journal Article
	Year	2012	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	45	Issue	4	Pages	1627-1636
	Keywords
	Abstract	Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets.
	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	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE; CAT;CIC			Approved	no
	Call Number	Admin @ si @ EKW2012			Serial	1807
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	Author	Bogdan Raducanu; Fadi Dornaika
	Title	A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning			Type	Journal Article
	Year	2012	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	45	Issue	6	Pages	2432-2444
	Keywords
	Abstract	IF= 2.61 IF=2.61 (2010) In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other. Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes	OR; MV			Approved	no
	Call Number	Admin @ si @ RaD2012a			Serial	1884
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	Author	Jon Almazan; Alicia Fornes; Ernest Valveny
	Title	A non-rigid appearance model for shape description and recognition			Type	Journal Article
	Year	2012	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	45	Issue	9	Pages	3105--3113
	Keywords	Shape recognition; Deformable models; Shape modeling; Hand-drawn recognition
	Abstract	In this paper we describe a framework to learn a model of shape variability in a set of patterns. The framework is based on the Active Appearance Model (AAM) and permits to combine shape deformations with appearance variability. We have used two modifications of the Blurred Shape Model (BSM) descriptor as basic shape and appearance features to learn the model. These modifications permit to overcome the rigidity of the original BSM, adapting it to the deformations of the shape to be represented. We have applied this framework to representation and classification of handwritten digits and symbols. We show that results of the proposed methodology outperform the original BSM approach.
	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	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ AFV2012			Serial	1982
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	Author	Jaume Gibert; Ernest Valveny; Horst Bunke
	Title	Graph Embedding in Vector Spaces by Node Attribute Statistics			Type	Journal Article
	Year	2012	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	45	Issue	9	Pages	3072-3083
	Keywords	Structural pattern recognition; Graph embedding; Data clustering; Graph classification
	Abstract	Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs.
	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	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ GVB2012a			Serial	1992
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	Author	Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
	Title	Towards Automatic Polyp Detection with a Polyp Appearance Model			Type	Journal Article
	Year	2012	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	45	Issue	9	Pages	3166-3182
	Keywords	Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot
	Abstract	This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0031-3203	ISBN		Medium
	Area	800	Expedition		Conference	IbPRIA
	Notes	MV;SIAI			Approved	no
	Call Number	Admin @ si @ BSV2012; IAM @ iam			Serial	1997
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	Author	Mario Hernandez; Joao Sanchez; Jordi Vitria
	Title	Selected papers from Iberian Conference on Pattern Recognition and Image Analysis			Type	Book Whole
	Year	2012	Publication	Pattern Recognition	Abbreviated Journal
	Volume	45	Issue	9	Pages	3047-3582
	Keywords
	Abstract
	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	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ HSV2012			Serial	2069
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	Author	Susana Alvarez; Maria Vanrell
	Title	Texton theory revisited: a bag-of-words approach to combine textons			Type	Journal Article
	Year	2012	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	45	Issue	12	Pages	4312-4325
	Keywords
	Abstract	The aim of this paper is to revisit an old theory of texture perception and update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets.
	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	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ AlV2012a			Serial	2130
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