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	Author	Josep Llados; Marçal Rusiñol
	Title	Graphics Recognition Techniques			Type	Book Chapter
	Year	2014	Publication	Handbook of Document Image Processing and Recognition	Abbreviated Journal
	Volume	D	Issue		Pages	489-521
	Keywords	Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation
	Abstract	This chapter describes the most relevant approaches for the analysis of graphical documents. The graphics recognition pipeline can be splitted into three tasks. The low level or lexical task extracts the basic units composing the document. The syntactic level is focused on the structure, i.e., how graphical entities are constructed, and involves the location and classification of the symbols present in the document. The third level is a functional or semantic level, i.e., it models what the graphical symbols do and what they mean in the context where they appear. This chapter covers the lexical level, while the next two chapters are devoted to the syntactic and semantic level, respectively. The main problems reviewed in this chapter are raster-to-vector conversion (vectorization algorithms) and the separation of text and graphics components. The research and industrial communities have provided standard methods achieving reasonable performance levels. Hence, graphics recognition techniques can be considered to be in a mature state from a scientific point of view. Additionally this chapter provides insights on some related problems, namely, the extraction and recognition of dimensions in engineering drawings, and the recognition of hatched and tiled patterns. Both problems are usually associated, even integrated, in the vectorization process.
	Address
	Corporate Author				Thesis
	Publisher	Springer London	Place of Publication		Editor	D. Doermann; K. Tombre
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-0-85729-858-4	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @ LlR2014			Serial	2380
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	Author	Salvatore Tabbone; Oriol Ramos Terrades
	Title	An Overview of Symbol Recognition			Type	Book Chapter
	Year	2014	Publication	Handbook of Document Image Processing and Recognition	Abbreviated Journal
	Volume	D	Issue		Pages	523-551
	Keywords	Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting
	Abstract	According to the Cambridge Dictionaries Online, a symbol is a sign, shape, or object that is used to represent something else. Symbol recognition is a subfield of general pattern recognition problems that focuses on identifying, detecting, and recognizing symbols in technical drawings, maps, or miscellaneous documents such as logos and musical scores. This chapter aims at providing the reader an overview of the different existing ways of describing and recognizing symbols and how the field has evolved to attain a certain degree of maturity.
	Address
	Corporate Author				Thesis
	Publisher	Springer London	Place of Publication		Editor	D. Doermann; K. Tombre
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-0-85729-858-4	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @ TaT2014			Serial	2489
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	Author	A.Kesidis; Dimosthenis Karatzas
	Title	Logo and Trademark Recognition			Type	Book Chapter
	Year	2014	Publication	Handbook of Document Image Processing and Recognition	Abbreviated Journal
	Volume	D	Issue		Pages	591-646
	Keywords	Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems
	Abstract	The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images.
	Address
	Corporate Author				Thesis
	Publisher	Springer London	Place of Publication		Editor	D. Doermann; K. Tombre
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-0-85729-858-4	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @ KeK2014			Serial	2425
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	Author	Ariel Amato; Ivan Huerta; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez
	Title	Moving Cast Shadows Detection Methods for Video Surveillance Applications			Type	Book Chapter
	Year	2014	Publication	Augmented Vision and Reality	Abbreviated Journal
	Volume	6	Issue		Pages	23-47
	Keywords
	Abstract	Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (‘shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows).
	Address
	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	2190-5916	ISBN	978-3-642-37840-9	Medium
	Area		Expedition		Conference
	Notes	ISE; 605.203; 600.049; 302.018; 302.012; 600.078			Approved	no
	Call Number	Admin @ si @ AHM2014			Serial	2223
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	Author	Josep Llados; Gemma Sanchez; Enric Marti
	Title	A string based method to recognize symbols and structural textures in architectural plans			Type	Book Chapter
	Year	1998	Publication	Graphics Recognition Algorithms and Systems Second International Workshop, GREC' 97 Nancy, France, August 22–23, 1997 Selected Papers	Abbreviated Journal	LNCS
	Volume	1389	Issue	1998	Pages	91-103
	Keywords
	Abstract	This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clustering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the similarity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion.
	Address
	Corporate Author				Thesis
	Publisher	Springer Link	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title	LNCS	Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; IAM			Approved	no
	Call Number	IAM @ iam @ SLE1998			Serial	1573
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	Author	Ole Vilhelm-Larsen; Petia Radeva; Enric Marti
	Title	Guidelines for choosing optimal parameters of elasticity for snakes			Type	Book Chapter
	Year	1995	Publication	Computer Analysis Of Images And Patterns	Abbreviated Journal	LNCS
	Volume	970	Issue		Pages	106-113
	Keywords
	Abstract	This paper proposes a guidance in the process of choosing and using the parameters of elasticity of a snake in order to obtain a precise segmentation. A new two step procedure is defined based on upper and lower bounds on the parameters. Formulas, by which these bounds can be calculated for real images where parts of the contour may be missing, are presented. Experiments on segmentation of bone structures in X-ray images have verified the usefulness of the new procedure.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title	Lecture Notes in Computer Science	Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB;IAM			Approved	no
	Call Number	IAM @ iam @ LRM1995b			Serial	1558
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	Author	Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva
	Title	A Machine Learning framework using SOMs: Applications in the Intestinal Motility Assessment			Type	Book Chapter
	Year	2006	Publication	11th Iberoamerican Congress on Pattern Recognition	Abbreviated Journal
	Volume	4225	Issue		Pages	188–197
	Keywords
	Abstract	Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visualization time required-, our efforts have been focused on the development of a machine learning system, built up in sequential stages, which provides the specialists with the useful part of the video, rejecting those parts not valid for analysis. We successfully used Self Organized Maps in a general semi-supervised framework with the aim of tackling the different learning stages of our system. The analysis of the diverse types of images and the automatic detection of intestinal contractions is performed under the perspective of intestinal motility assessment in a clinical environment.
	Address	Cancun (Mexico)
	Corporate Author				Thesis
	Publisher	Springer Verlag	Place of Publication	Berlin-Heidelberg	Editor	J.P. Martinez–Trinidad et al
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area	800	Expedition		Conference	CIARP06
	Notes	MV;OR;MILAB;SIAI			Approved	no
	Call Number	BCNPCL @ bcnpcl @ VSV2006d; IAM @ iam @ VSV2006e			Serial	729
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	Author	Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva
	Title	Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions			Type	Book Chapter
	Year	2006	Publication	11th Iberoamerican Congress on Pattern Recognition	Abbreviated Journal
	Volume	4225	Issue		Pages	178–187
	Keywords
	Abstract	This work tackles the categorization of general linear radial patterns by means of the valleys and ridges detection and the use of descriptors of directional information, which are provided by steerable filters in different regions of the image. We successfully apply our proposal in the specific case of automatic detection of tonic contractions in video capsule endoscopy, which represent a paradigmatic example of linear radial patterns.
	Address	Cancun (Mexico)
	Corporate Author				Thesis
	Publisher	Springer Verlag	Place of Publication	Berlin Heidelberg	Editor	.F. Mart ́ınez-Trinidad et al
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area	800	Expedition		Conference
	Notes	MV;OR;MILAB;SIAI			Approved	no
	Call Number	BCNPCL @ bcnpcl @ VSV2006c; IAM @ iam @ VSB2006f			Serial	728
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	Author	Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Fernando Azpiroz; Petia Radeva
	Title	Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions			Type	Book Chapter
	Year	2006	Publication	9th International Conference on Medical Image Computing and Computer–Assisted Intervention	Abbreviated Journal
	Volume	4191	Issue		Pages	161–168
	Keywords
	Abstract	Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of con- traction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions.
	Address	Copenhagen (Denmark)
	Corporate Author				Thesis
	Publisher	Springer Verlag	Place of Publication	Berlin Heidelberg	Editor	R. Larsen, M. Nielsen, and J. Sporring
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area	800	Expedition		Conference	MICCAI06
	Notes	MV;OR;MILAB;SIAI			Approved	no
	Call Number	BCNPCL @ bcnpcl @ SVV2006; IAM @ iam @ SVV2006			Serial	725
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	Author	Jose Manuel Alvarez; Antonio Lopez
	Title	Photometric Invariance by Machine Learning			Type	Book Chapter
	Year	2012	Publication	Color in Computer Vision: Fundamentals and Applications	Abbreviated Journal
	Volume	7	Issue		Pages	113-134
	Keywords	road detection
	Abstract
	Address
	Corporate Author				Thesis
	Publisher	iConcept Press Ltd	Place of Publication		Editor	Theo Gevers, Arjan Gijsenij, Joost van de Weijer, Jan-Mark Geusebroek
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-0-470-89084-4	Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ AlL2012			Serial	2186
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	Author	Antonio Lopez
	Title	Pedestrian Detection Systems			Type	Book Chapter
	Year	2018	Publication	Wiley Encyclopedia of Electrical and Electronics Engineering	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Pedestrian detection is a highly relevant topic for both advanced driver assistance systems (ADAS) and autonomous driving. In this entry, we review the ideas behind pedestrian detection systems from the point of view of perception based on computer vision and machine learning.
	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; 600.118			Approved	no
	Call Number	Admin @ si @ Lop2018			Serial	3230
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	Author	Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva
	Title	Multiresolution texture classification of ceramic tiles.			Type	Book Chapter
	Year	1999	Publication	Recent Research developments in optical engineering, Research Signpost, 2: 213–228	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	India
	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;CIC			Approved	no
	Call Number	ADAS @ adas @ LBV1999b			Serial	45
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	Author	A. Martinez; Jordi Vitria
	Title	Designing and Implementing Real Walking Agents using Virtual Environments.			Type	Book Chapter
	Year	1996	Publication	Applications of Artificial Intelligence	Abbreviated Journal
	Volume		Issue		Pages	105-114
	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	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ MaV1995a			Serial	121
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	Author	Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados
	Title	Textual Descriptions for Browsing People by Visual Apperance.			Type	Book Chapter
	Year	2002	Publication	Lecture Notes in Artificial Intelligence	Abbreviated Journal
	Volume	2504	Issue		Pages	419-429
	Keywords
	Abstract	This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building
	Address
	Corporate Author				Thesis
	Publisher	Springer Verlag	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;CIC			Approved	no
	Call Number	CAT @ cat @ TBB2002b			Serial	319
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	Author	Fernando Vilariño; Petia Radeva
	Title	Cardiac Segmentation with Discriminant Active Contours			Type	Book Chapter
	Year	2003	Publication		Abbreviated Journal
	Volume		Issue		Pages	211–217
	Keywords
	Abstract	Dynamic tracking of heart moving is one relevant target in medical imag- ing and can be helpful for analyzing heart dynamics in the study of several cardiac diseases. For this aim, a previous segmentation problem of such structures is stated, based on certain relevant features (like edges or intensity levels, textures, etc.) Clas- sical active models have been used, but they fail when overlapping structures or not well-defined contours are present. Automatic feature learning systems may be a pow- erful tool. Discriminant active contours present optimal results in this kind of problem. They are a kind of deformable models that converge to an optimal object segmenta- tion that dynamically adapts to the object contour. The feature space is designed from a filter bank in order to guarantee the search and learning of the set of relevant fea- tures for optimal classification on each part of the object. Tracking of target evolution is obtained through the whole set of images, using information from the actual and previous stages. Feedback systems are implemented to guarantee the minimum well- separable classification set in each segmentation step. Our implementation has been proved with several series of Magnetic Resonance with improved results in segmenta- tion in comparison to previous methods.
	Address	Palma de Mallorca
	Corporate Author				Thesis
	Publisher	IOS Press	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	CCIA
	Notes	MV;MILAB;SIAI			Approved	no
	Call Number	BCNPCL @ bcnpcl @ ViR2003; IAM @ iam @ VRa2003			Serial	426
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