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	Author	Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta
	Title	Large-scale Graph Indexing using Binary Embeddings of Node Contexts			Type	Conference Article
	Year	2015	Publication	10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition	Abbreviated Journal
	Volume	9069	Issue		Pages	208-217
	Keywords	Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding
	Abstract	Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. Each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents.
	Address	Beijing; China; May 2015
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor	C.-L.Liu; B.Luo; W.G.Kropatsch; J.Cheng
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-319-18223-0	Medium
	Area		Expedition		Conference	GbRPR
	Notes	DAG; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ RLF2015a			Serial	2618
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	Author	Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund
	Title	Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields			Type	Journal Article
	Year	2016	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	80	Issue		Pages	208–215
	Keywords
	Abstract	This paper proposes a technique for RGB-D scene segmentation using Multi-class Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset. The approach shows that simple multi-modal features with the power of the MMSSL paradigm can achieve better performance than state of the art results on the same dataset.
	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	HuPBA; ISE;MILAB; 600.098; 600.119			Approved	no
	Call Number	Admin @ si @ TEG2016			Serial	2843
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	Author	David Lloret; Joan Serrat; Antonio Lopez; A. Soler; Juan J. Villanueva
	Title	Retinal image registration using creases as anatomical landmarks.			Type	Conference Article
	Year	2000	Publication	15 th International Conference on Pattern Recognition	Abbreviated Journal
	Volume	3	Issue		Pages	207-2010
	Keywords
	Abstract	Retinal images are routinely used in ophthalmology to study the optical nerve head and the retina. To assess objectively the evolution of an illness, images taken at different times must be registered. Most methods so far have been designed specifically for a single image modality, like temporal series or stereo pairs of angiographies, fluorescein angiographies or scanning laser ophthalmoscope (SLO) images, which makes them prone to fail when conditions vary. In contrast, the method we propose has shown to be accurate and reliable on all the former modalities. It has been adapted from the 3D registration of CT and MR image to 2D. Relevant features (also known as landmarks) are extracted by means of a robust creaseness operator, and resulting images are iteratively transformed until a maximum in their correlation is achieved. Our method has succeeded in more than 100 pairs tried so far, in all cases including also the scaling as a parameter to be optimized
	Address	Barcelona.
	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	ADAS			Approved	no
	Call Number	ADAS @ adas @ LSL2000 c			Serial	233
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	Author	Bogdan Raducanu; D. Gatica-Perez
	Title	Inferring competitive role patterns in reality TV show through nonverbal analysis			Type	Journal Article
	Year	2012	Publication	Multimedia Tools and Applications	Abbreviated Journal	MTAP
	Volume	56	Issue	1	Pages	207-226
	Keywords
	Abstract	This paper introduces a new facet of social media, namely that depicting social interaction. More concretely, we address this problem from the perspective of nonverbal behavior-based analysis of competitive meetings. For our study, we made use of “The Apprentice” reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status, and predicting the fired candidates. We address this problem by adopting both supervised and unsupervised strategies. The current study was carried out using nonverbal audio cues. Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. The analysis is based on two types of data: individual and relational measures. Results obtained from the analysis of a full season of the show are promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach has been conveniently compared with the Influence Model, demonstrating its superiority.
	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	1380-7501	ISBN		Medium
	Area		Expedition		Conference
	Notes	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ RaG2012			Serial	1360
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	Author	P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes
	Title	A Novel Learning-free Word Spotting Approach Based on Graph Representation			Type	Conference Article
	Year	2014	Publication	11th IAPR International Workshop on Document Analysis and Systems	Abbreviated Journal
	Volume		Issue		Pages	207-211
	Keywords
	Abstract	Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of 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. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods.
	Address	Tours; France; April 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	978-1-4799-3243-6	Medium
	Area		Expedition		Conference	DAS
	Notes	DAG; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ WEG2014b			Serial	2517
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	Author	Alicia Fornes; V.C.Kieu; M. Visani; N.Journet; Anjan Dutta
	Title	The ICDAR/GREC 2013 Music Scores Competition: Staff Removal			Type	Book Chapter
	Year	2014	Publication	Graphics Recognition. Current Trends and Challenges	Abbreviated Journal
	Volume	8746	Issue		Pages	207-220
	Keywords	Competition; Graphics recognition; Music scores; Writer identification; Staff removal
	Abstract	The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor	B.Lamiroy; J.-M. Ogier
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-662-44853-3	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.077; 600.061			Approved	no
	Call Number	Admin @ si @ FKV2014			Serial	2581
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	Author	Lluis Gomez; Dimosthenis Karatzas
	Title	Object Proposals for Text Extraction in the Wild			Type	Conference Article
	Year	2015	Publication	13th International Conference on Document Analysis and Recognition ICDAR2015	Abbreviated Journal
	Volume		Issue		Pages	206 - 210
	Keywords
	Abstract	Object Proposals is a recent computer vision technique receiving increasing interest from the research community. Its main objective is to generate a relatively small set of bounding box proposals that are most likely to contain objects of interest. The use of Object Proposals techniques in the scene text understanding field is innovative. Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, Object Proposals techniques emerge as an alternative to the traditional text detectors. In this paper we study to what extent the existing generic Object Proposals methods may be useful for scene text understanding. Also, we propose a new Object Proposals algorithm that is specifically designed for text and compare it with other generic methods in the state of the art. Experiments show that our proposal is superior in its ability of producing good quality word proposals in an efficient way. The source code of our method is made publicly available
	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	ICDAR
	Notes	DAG; 600.077; 600.084; 601.197			Approved	no
	Call Number	Admin @ si @ GoK2015			Serial	2691
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	Author	Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez
	Title	Real Time Vehicle Pose Using On-Board Stereo Vision System			Type	Conference Article
	Year	2006	Publication	International Conference on Image Analysis and Recognition	Abbreviated Journal	ICIAR
	Volume		Issue	LNCS 4142	Pages	205–216
	Keywords
	Abstract	This paper presents a robust technique for a real time estimation of both camera’s position and orientation—referred as pose. A commercial stereo vision system is used. Unlike previous approaches, it can be used either for urban or highway scenarios. The proposed technique consists of two stages. Initially, a compact 2D representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the road. At the same time, relative camera’s position and orientation are computed. The proposed technique is intended to be used on a driving assistance scheme for applications such as obstacle or pedestrian detection. Experimental results on urban environments with different road geometries are presented.
	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	ADAS			Approved	no
	Call Number	ADAS @ adas @ SGD2006b			Serial	671
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	Author	Debora Gil; Petia Radeva
	Title	Shape Restoration via a Regularized Curvature Flow			Type	Journal Article
	Year	2004	Publication	Journal of Mathematical Imaging and Vision	Abbreviated Journal
	Volume	21	Issue	3	Pages	205-223
	Keywords
	Abstract	Any image filtering operator designed for automatic shape restoration should satisfy robustness (whatever the nature and degree of noise is) as well as non-trivial smooth asymptotic behavior. Moreover, a stopping criterion should be determined by characteristics of the evolved image rather than dependent on the number of iterations. Among the several PDE based techniques, curvature flows appear to be highly reliable for strongly noisy images compared to image diffusion processes. In the present paper, we introduce a regularized curvature flow (RCF) that admits non-trivial steady states. It is based on a measure of the local curve smoothness that takes into account regularity of the curve curvature and serves as stopping term in the mean curvature flow. We prove that this measure decreases over the orbits of RCF, which endows the method with a natural stop criterion in terms of the magnitude of this measure. Further, in its discrete version it produces steady states consisting of piece-wise regular curves. Numerical experiments made on synthetic shapes corrupted with different kinds of noise show the abilities and limitations of each of the current geometric flows and the benefits of RCF. Finally, we present results on real images that illustrate the usefulness of the present approach in practical applications.
	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	IAM;MILAB			Approved	no
	Call Number	IAM @ iam @ GiR2004c			Serial	1532
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	Author	Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg
	Title	Coloring Action Recognition in Still Images			Type	Journal Article
	Year	2013	Publication	International Journal of Computer Vision	Abbreviated Journal	IJCV
	Volume	105	Issue	3	Pages	205-221
	Keywords
	Abstract	In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.
	Address
	Corporate Author				Thesis
	Publisher	Springer US	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0920-5691	ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC; ADAS; 600.057; 600.048			Approved	no
	Call Number	Admin @ si @ KRW2013			Serial	2285
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	Author	Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva
	Title	Egocentric video description based on temporally-linked sequences			Type	Journal Article
	Year	2018	Publication	Journal of Visual Communication and Image Representation	Abbreviated Journal	JVCIR
	Volume	50	Issue		Pages	205-216
	Keywords	egocentric vision; video description; deep learning; multi-modal learning
	Abstract	Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures. In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description.
	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	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ BPC2018			Serial	3109
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	Author	Patricia Suarez; Angel Sappa; Boris X. Vintimilla
	Title	Deep learning-based vegetation index estimation			Type	Book Chapter
	Year	2021	Publication	Generative Adversarial Networks for Image-to-Image Translation	Abbreviated Journal
	Volume		Issue		Pages	205-234
	Keywords
	Abstract	Chapter 9
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor	A.Solanki; A.Nayyar; M.Naved
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MSIAU; 600.122			Approved	no
	Call Number	Admin @ si @ SSV2021a			Serial	3578
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	Author	Agnes Borras; Josep Llados
	Title	Corest: A measure of color and space stability to detect salient regions according to human criteria			Type	Conference Article
	Year	2009	Publication	5th International Conference on Computer Vision Theory and Applications	Abbreviated Journal
	Volume		Issue		Pages	204-209
	Keywords
	Abstract
	Address	Lisboa, Portugal
	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	978-989-8111-69-2	Medium
	Area		Expedition		Conference	VISAPP
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ BoL2009			Serial	1225
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	Author	David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras
	Title	Visual Registration Method For A Low Cost Robot: Computer Vision Systems			Type	Conference Article
	Year	2009	Publication	7th International Conference on Computer Vision Systems	Abbreviated Journal
	Volume	5815	Issue		Pages	204–214
	Keywords
	Abstract	An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most methods estimate the transformation relating the maps from matches established between low level feature extracted from sensor data. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by the Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated in a typical office environment showing good performance.
	Address	Belgica
	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	978-3-642-04666-7	Medium
	Area		Expedition		Conference	ICVS
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ ATR2009b			Serial	1247
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	Author	Wenjuan Gong; Jordi Gonzalez; Joao Manuel R. S. Taveres; Xavier Roca
	Title	A New Image Dataset on Human Interactions			Type	Conference Article
	Year	2012	Publication	7th Conference on Articulated Motion and Deformable Objects	Abbreviated Journal
	Volume	7378	Issue		Pages	204-209
	Keywords
	Abstract	This article describes a new collection of still image dataset which are dedicated to interactions between people. Human action recognition from still images have been a hot topic recently, but most of them are actions performed by a single person, like running, walking, riding bikes, phoning and so on and there is no interactions between people in one image. The dataset collected in this paper are concentrating on human interaction between two people aiming to explore this new topic in the research area of action recognition from still images.
	Address	Mallorca
	Corporate Author				Thesis
	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	978-3-642-31566-4	Medium
	Area		Expedition		Conference	AMDO
	Notes	ISE			Approved	no
	Call Number	Admin @ si @ GGT2012			Serial	2030
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