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	Author	Jon Almazan; Alicia Fornes; Ernest Valveny
	Title	A Non-Rigid Feature Extraction Method for Shape Recognition			Type	Conference Article
	Year	2011	Publication	11th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	987-991
	Keywords
	Abstract	This paper presents a methodology for shape recognition that focuses on dealing with the difficult problem of large deformations. The proposed methodology consists in a novel feature extraction technique, which uses a non-rigid representation adaptable to the shape. This technique employs a deformable grid based on the computation of geometrical centroids that follows a region partitioning algorithm. Then, a feature vector is extracted by computing pixel density measures around these geometrical centroids. The result is a shape descriptor that adapts its representation to the given shape and encodes the pixel density distribution. The validity of the method when dealing with large deformations has been experimentally shown over datasets composed of handwritten shapes. It has been applied to signature verification and shape recognition tasks demonstrating high accuracy and low computational cost.
	Address	Beijing; China; September 2011
	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	978-0-7695-4520-2	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ AFV2011			Serial	1763
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	Author	Jon Almazan; Alicia Fornes; Ernest Valveny
	Title	A Deformable HOG-based Shape Descriptor			Type	Conference Article
	Year	2013	Publication	12th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	1022-1026
	Keywords
	Abstract	In this paper we deal with the problem of recognizing handwritten shapes. We present a new deformable feature extraction method that adapts to the shape to be described, dealing in this way with the variability introduced in the handwriting domain. It consists in a selection of the regions that best define the shape to be described, followed by the computation of histograms of oriented gradients-based features over these points. Our results significantly outperform other descriptors in the literature for the task of hand-drawn shape recognition and handwritten word retrieval
	Address	Washington; USA; August 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	1520-5363	ISBN		Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ AFV2013			Serial	2326
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	Author	Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny
	Title	Handwritten Word Spotting with Corrected Attributes			Type	Conference Article
	Year	2013	Publication	15th IEEE International Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	1017-1024
	Keywords
	Abstract	We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results.
	Address	Sydney; Australia; December 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	1550-5499	ISBN		Medium
	Area		Expedition		Conference	ICCV
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ AGF2013			Serial	2327
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	Author	Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny
	Title	Efficient Exemplar Word Spotting			Type	Conference Article
	Year	2012	Publication	23rd British Machine Vision Conference	Abbreviated Journal
	Volume		Issue		Pages	67.1- 67.11
	Keywords
	Abstract	In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding window approach is used to locate the document regions that are most similar to the query. We use the exemplar SVM framework to produce a better representation of the query in an unsupervised way. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage.
	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	1-901725-46-4	Medium
	Area		Expedition		Conference	BMVC
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ AGF2012			Serial	1984
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	Author	Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny
	Title	Word Spotting and Recognition with Embedded Attributes			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	36	Issue	12	Pages	2552 - 2566
	Keywords
	Abstract	This article addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.
	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	0162-8828	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; 600.056; 600.045; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ AGF2014a			Serial	2483
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	Author	Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny
	Title	Segmentation-free Word Spotting with Exemplar SVMs			Type	Journal Article
	Year	2014	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	47	Issue	12	Pages	3967–3978
	Keywords	Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression
	Abstract	In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage.
	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; 600.045; 600.056; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ AGF2014b			Serial	2485
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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	Jon Almazan
	Title	Deforming the Blurred Shape Model for Shape Description and Recognition			Type	Report
	Year	2010	Publication	CVC Technical Report	Abbreviated Journal
	Volume	163	Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis	Master's 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				Approved	no
	Call Number	Admin @ si @ Alm2010			Serial	1354
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	Author	Jon Almazan
	Title	Learning to Represent Handwritten Shapes and Words for Matching and Recognition			Type	Book Whole
	Year	2014	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Writing is one of the most important forms of communication and for centuries, handwriting had been the most reliable way to preserve knowledge. However, despite the recent development of printing houses and electronic devices, handwriting is still broadly used for taking notes, doing annotations, or sketching ideas. Transferring the ability of understanding handwritten text or recognizing handwritten shapes to computers has been the goal of many researches due to its huge importance for many different fields. However, designing good representations to deal with handwritten shapes, e.g. symbols or words, is a very challenging problem due to the large variability of these kinds of shapes. One of the consequences of working with handwritten shapes is that we need representations to be robust, i.e., able to adapt to large intra-class variability. We need representations to be discriminative, i.e., able to learn what are the differences between classes. And, we need representations to be efficient, i.e., able to be rapidly computed and compared. Unfortunately, current techniques of handwritten shape representation for matching and recognition do not fulfill some or all of these requirements. Through this thesis we focus on the problem of learning to represent handwritten shapes aimed at retrieval and recognition tasks. Concretely, on the first part of the thesis, we focus on the general problem of representing any kind of handwritten shape. We first present a novel shape descriptor based on a deformable grid that deals with large deformations by adapting to the shape and where the cells of the grid can be used to extract different features. Then, we propose to use this descriptor to learn statistical models, based on the Active Appearance Model, that jointly learns the variability in structure and texture of a given class. Then, on the second part, we focus on a concrete application, the problem of representing handwritten words, for the tasks of word spotting, where the goal is to find all instances of a query word in a dataset of images, and recognition. First, we address the segmentation-free problem and propose an unsupervised, sliding-window-based approach that achieves state-of- the-art results in two public datasets. Second, we address the more challenging multi-writer problem, where the variability in words exponentially increases. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace, and where those that represent the same word are close together. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. This leads to a low-dimensional, unified representation of word images and strings, resulting in a method that allows one to perform either image and text searches, as well as image transcription, in a unified framework. We evaluate our methods on different public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Ernest Valveny;Alicia Fornes
	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; 600.077			Approved	no
	Call Number	Admin @ si @ Alm2014			Serial	2572
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	Author	Joel Barajas; Karla Lizbeth Caballero; Petia Radeva
	Title	Cardiac Phase Extraction in IVUS Sequences Using 1-D Gabor Filters			Type	Conference Article
	Year	2007	Publication	Engineering in Medicine and Biology Society, 29th Annual International Conference of the IEEE	Abbreviated Journal
	Volume		Issue		Pages	343–36
	Keywords
	Abstract
	Address	Lyon (France)
	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	MILAB			Approved	no
	Call Number	BCNPCL @ bcnpcl @ BCR2007			Serial	924
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	Author	Joel Barajas; Jaume Garcia; Karla Lizbeth Caballero; Francesc Carreras; Sandra Pujades; Petia Radeva
	Title	Correction of Misalignment Artifacts Among 2-D Cardiac MR Images in 3-D Space			Type	Conference Article
	Year	2006	Publication	1st International Wokshop on Computer Vision for Intravascular and Intracardiac Imaging (CVII’06)	Abbreviated Journal
	Volume	3217	Issue		Pages	114-121
	Keywords
	Abstract	Cardiac Magnetic Resonance images offer the opportunity to study the heart in detail. One of the main issues in its modelling is to create an accurate 3-D reconstruction of the left ventricle from 2-D views. A first step to achieve this goal is the correct registration among the different image planes due to patient movements. In this article, we present an accurate method to correct displacement artifacts using the Normalized Mutual Information. Here, the image views are treated as planes in order to diminish the approximation error caused by the association of a certain thickness, and moved simultaneously to avoid any kind of bias in the alignment process. This method has been validated using real and syntectic plane displacements, yielding promising results.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication	Copenhagen (Denmark)	Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-540-22977-3	Medium
	Area		Expedition		Conference
	Notes	IAM;MILAB			Approved	no
	Call Number	IAM @ iam @ BGC2006			Serial	1485
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	Author	Joel Barajas; Jaume Garcia; Francesc Carreras; Sandra Pujades; Petia Radeva
	Title	Angle Images Using Gabor Filters in Cardiac Tagged MRI			Type	Conference Article
	Year	2005	Publication	Proceeding of the 2005 conference on Artificial Intelligence Research and Development	Abbreviated Journal
	Volume		Issue		Pages	107-114
	Keywords	Angle Images, Gabor Filters, Harp, Tagged Mri
	Abstract	Tagged Magnetic Resonance Imaging (MRI) is a non-invasive technique used to examine cardiac deformation in vivo. An Angle Image is a representation of a Tagged MRI which recovers the relative position of the tissue respect to the distorted tags. Thus cardiac deformation can be estimated. This paper describes a new approach to generate Angle Images using a bank of Gabor filters in short axis cardiac Tagged MRI. Our method improves the Angle Images obtained by global techniques, like HARP, with a local frequency analysis. We propose to use the phase response of a combination of a Gabor filters bank, and use it to find a more precise deformation of the left ventricle. We demonstrate the accuracy of our method over HARP by several experimental results.
	Address	Amsterdam; The Netherlands
	Corporate Author				Thesis
	Publisher	IOS Press	Place of Publication	Amsterdam, The Netherlands	Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	1-58603-560-6	Medium
	Area		Expedition		Conference	CAIRD
	Notes	IAM;MILAB			Approved	no
	Call Number	BCNPCL @ bcnpcl @ BGC2005; IAM @ iam			Serial	595
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	Author	Joel Barajas
	Title	Spectral Rigid Registration of Medical Images: Application to Tagged MRI and IVUS			Type	Report
	Year	2007	Publication	CVC Technical Report #106	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	CVC (UAB)
	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				Approved	no
	Call Number	Admin @ si @ Bar2007			Serial	821
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	Author	Joaquin Salas; Wendy Avalos; Rafael Castañeda; Mario Maya
	Title	A machine-vision system to measure the parameters describing the performance of a Foucault pendulum			Type	Journal
	Year	2006	Publication	Machine Vision and Applications	Abbreviated Journal
	Volume	17	Issue	2	Pages	133–138
	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		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	Admin @ si @ SAC2006			Serial	644
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	Author	Joaquin Salas; P. Martinez; Jordi Gonzalez
	Title	Background Updating with the Use of Intrinsic Curves			Type	Book Chapter
	Year	2006	Publication	International Conference on Image Analysis and Recognition (ICIAR´06), LNCS 4141 (A. Campilho et al., eds.), 1: 731–742, ISBN 978–3–540–44891–4	Abbreviated Journal
	Volume		Issue		Pages
	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		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	ISE @ ise @ SMG2006			Serial	768
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