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	Author	Marçal Rusiñol; Agnes Borras; Josep Llados
	Title	Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images			Type	Journal Article
	Year	2010	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	31	Issue	3	Pages	188–201
	Keywords	Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings
	Abstract	This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results.
	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	DAG @ dag @ RBL2010			Serial	1177
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	Author	Joan Mas; Josep Llados; Gemma Sanchez; J.A. Jorge
	Title	A syntactic approach based on distortion-tolerant Adjacency Grammars and a spatial-directed parser to interpret sketched diagrams			Type	Journal Article
	Year	2010	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	43	Issue	12	Pages	4148–4164
	Keywords	Syntactic Pattern Recognition; Symbol recognition; Diagram understanding; Sketched diagrams; Adjacency Grammars; Incremental parsing; Spatial directed parsing
	Abstract	This paper presents a syntactic approach based on Adjacency Grammars (AG) for sketch diagram modeling and understanding. Diagrams are a combination of graphical symbols arranged according to a set of spatial rules defined by a visual language. AG describe visual shapes by productions defined in terms of terminal and non-terminal symbols (graphical primitives and subshapes), and a set functions describing the spatial arrangements between symbols. Our approach to sketch diagram understanding provides three main contributions. First, since AG are linear grammars, there is a need to define shapes and relations inherently bidimensional using a sequential formalism. Second, our parsing approach uses an indexing structure based on a spatial tessellation. This serves to reduce the search space when finding candidates to produce a valid reduction. This allows order-free parsing of 2D visual sentences while keeping combinatorial explosion in check. Third, working with sketches requires a distortion model to cope with the natural variations of hand drawn strokes. To this end we extended the basic grammar with a distortion measure modeled on the allowable variation on spatial constraints associated with grammar productions. Finally, the paper reports on an experimental framework an interactive system for sketch analysis. User tests performed on two real scenarios show that our approach is usable in interactive settings.
	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	DAG @ dag @ MLS2010			Serial	1336
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	Author	Fadi Dornaika; Bogdan Raducanu
	Title	Single Snapshot 3D Head Pose Initialization for Tracking in Human Robot Interaction Scenario			Type	Conference Article
	Year	2010	Publication	1st International Workshop on Computer Vision for Human-Robot Interaction	Abbreviated Journal
	Volume		Issue		Pages	32–39
	Keywords	1st International Workshop on Computer Vision for Human-Robot Interaction, in conjunction with IEEE CVPR 2010
	Abstract	This paper presents an automatic 3D head pose initialization scheme for a real-time face tracker with application to human-robot interaction. It has two main contributions. First, we propose an automatic 3D head pose and person specific face shape estimation, based on a 3D deformable model. The proposed approach serves to initialize our realtime 3D face tracker. What makes this contribution very attractive is that the initialization step can cope with faces under arbitrary pose, so it is not limited only to near-frontal views. Second, the previous framework is used to develop an application in which the orientation of an AIBO’s camera can be controlled through the imitation of user’s head pose. In our scenario, this application is used to build panoramic images from overlapping snapshots. Experiments on real videos confirm the robustness and usefulness of the proposed methods.
	Address	San Francisco; CA; USA; June 2010
	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	2160-7508	ISBN	978-1-4244-7029-7	Medium
	Area		Expedition		Conference	CVPRW
	Notes	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ DoR2010a			Serial	1309
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	Author	Fernando Barrera; Felipe Lumbreras; Angel Sappa
	Title	Multimodal Template Matching based on Gradient and Mutual Information using Scale-Space			Type	Conference Article
	Year	2010	Publication	17th IEEE International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages	2749–2752
	Keywords
	Abstract	This paper presents the combined use of gradient and mutual information for infrared and intensity templates matching. We propose to joint: (i) feature matching in a multiresolution context and (ii) information propagation through scale-space representations. Our method consists in combining mutual information with a shape descriptor based on gradient, and propagate them following a coarse-to-fine strategy. The main contributions of this work are: to offer a theoretical formulation towards a multimodal stereo matching; to show that gradient and mutual information can be reinforced while they are propagated between consecutive levels; and to show that they are valid cost functions in multimodal template matchings. Comparisons are presented showing the improvements and viability of the proposed approach.
	Address	Hong-Kong
	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	1522-4880	ISBN	978-1-4244-7992-4	Medium
	Area		Expedition		Conference	ICIP
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ BLS2010			Serial	1358
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	Author	Ivan Huerta
	Title	Foreground Object Segmentation and Shadow Detection for Video Sequences in Uncontrolled Environments			Type	Book Whole
	Year	2010	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This Thesis is mainly divided in two parts. The first one presents a study of motion segmentation problems. Based on this study, a novel algorithm for mobile-object segmentation from a static background scene is also presented. This approach is demonstrated robust and accurate under most of the common problems in motion segmentation. The second one tackles the problem of shadows in depth. Firstly, a bottom-up approach based on a chromatic shadow detector is presented to deal with umbra shadows. Secondly, a top-down approach based on a tracking system has been developed in order to enhance the chromatic shadow detection. In our first contribution, a case analysis of motion segmentation problems is presented by taking into account the problems associated with different cues, namely colour, edge and intensity. Our second contribution is a hybrid architecture which handles the main problems observed in such a case analysis, by fusing (i) the knowledge from these three cues and (ii) a temporal difference algorithm. On the one hand, we enhance the colour and edge models to solve both global/local illumination changes (shadows and highlights) and camouflage in intensity. In addition, local information is exploited to cope with a very challenging problem such as the camouflage in chroma. On the other hand, the intensity cue is also applied when colour and edge cues are not available, such as when beyond the dynamic range. Additionally, temporal difference is included to segment motion when these three cues are not available, such as that background not visible during the training period. Lastly, the approach is enhanced for allowing ghost detection. As a result, our approach obtains very accurate and robust motion segmentation in both indoor and outdoor scenarios, as quantitatively and qualitatively demonstrated in the experimental results, by comparing our approach with most best-known state-of-the-art approaches. Motion Segmentation has to deal with shadows to avoid distortions when detecting moving objects. Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows. Consequently, umbra shadows are usually detected as part of moving objects. Firstly, a bottom-up approach for detection and removal of chromatic moving shadows in surveillance scenarios is proposed. Secondly, a top-down approach based on kalman filters to detect and track shadows has been developed in order to enhance the chromatic shadow detection. In the Bottom-up part, the shadow detection approach applies a novel technique based on gradient and colour models for separating chromatic moving shadows from moving objects. Well-known colour and gradient models are extended and improved into an invariant colour cone model and an invariant gradient model, respectively, to perform automatic segmentation while detecting potential shadows. Hereafter, the regions corresponding to potential shadows are grouped by considering ”a bluish effect” and an edge partitioning. Lastly, (i) temporal similarities between local gradient structures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. In the top-down process, after detection of objects and shadows both are tracked using Kalman filters, in order to enhance the chromatic shadow detection, when it fails to detect a shadow. Firstly, this implies a data association between the blobs (foreground and shadow) and Kalman filters. Secondly, an event analysis of the different data association cases is performed, and occlusion handling is managed by a Probabilistic Appearance Model (PAM). Based on this association, temporal consistency is looked for the association between foregrounds and shadows and their respective Kalman Filters. From this association several cases are studied, as a result lost chromatic shadows are correctly detected. Finally, the tracking results are used as feedback to improve the shadow and object detection. Unlike other approaches, our method does not make any a-priori assumptions about camera location, surface geometries, surface textures, shapes and types of shadows, objects, and background. Experimental results show the performance and accuracy of our approach in different shadowed materials and illumination conditions.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Jordi Gonzalez;Xavier Roca
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-937261-3-3	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	ISE @ ise @ Hue2010			Serial	1332
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	Author	Dani Rowe; Jordi Gonzalez; Marco Pedersoli; Juan J. Villanueva
	Title	On Tracking Inside Groups			Type	Journal Article
	Year	2010	Publication	Machine Vision and Applications	Abbreviated Journal	MVA
	Volume	21	Issue	2	Pages	113–127
	Keywords
	Abstract	This work develops a new architecture for multiple-target tracking in unconstrained dynamic scenes, which consists of a detection level which feeds a two-stage tracking system. A remarkable characteristic of the system is its ability to track several targets while they group and split, without using 3D information. Thus, special attention is given to the feature-selection and appearance-computation modules, and to those modules involved in tracking through groups. The system aims to work as a stand-alone application in complex and dynamic scenarios. No a-priori knowledge about either the scene or the targets, based on a previous training period, is used. Hence, the scenario is completely unknown beforehand. Successful tracking has been demonstrated in well-known databases of both indoor and outdoor scenarios. Accurate and robust localisations have been yielded during long-term target merging and occlusions.
	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	0932-8092	ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE			Approved	no
	Call Number	ISE @ ise @ RGP2010			Serial	1158
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	Author	Sergio Escalera; Oriol Pujol; Petia Radeva
	Title	Traffic sign recognition system with β -correction			Type	Journal Article
	Year	2010	Publication	Machine Vision and Applications	Abbreviated Journal	MVA
	Volume	21	Issue	2	Pages	99–111
	Keywords
	Abstract	Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.
	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	0932-8092	ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB;HUPBA			Approved	no
	Call Number	BCNPCL @ bcnpcl @ EPR2010a			Serial	1276
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	Author	N. Serrano; L. Tarazon; D. Perez; Oriol Ramos Terrades; S. Juan
	Title	The GIDOC Prototype			Type	Conference Article
	Year	2010	Publication	10th International Workshop on Pattern Recognition in Information Systems	Abbreviated Journal
	Volume		Issue		Pages	82-89
	Keywords
	Abstract	Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. It might be carried out by first processing all document images off-line, and then manually supervising system transcriptions to edit incorrect parts. However, current techniques for automatic page layout analysis, text line detection and handwriting recognition are still far from perfect, and thus post-editing system output is not clearly better than simply ignoring it. A more effective approach to transcribe old text documents is to follow an interactive- predictive paradigm in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Following this approach, a system prototype called GIDOC (Gimp-based Interactive transcription of old text DOCuments) has been developed to provide user-friendly, integrated support for interactive-predictive layout analysis, line detection and handwriting transcription. GIDOC is designed to work with (large) collections of homogeneous documents, that is, of similar structure and writing styles. They are annotated sequentially, by (par- tially) supervising hypotheses drawn from statistical models that are constantly updated with an increasing number of available annotated documents. And this is done at different annotation levels. For instance, at the level of page layout analysis, GIDOC uses a novel text block detection method in which conventional, memoryless techniques are improved with a “history” model of text block positions. Similarly, at the level of text line image transcription, GIDOC includes a handwriting recognizer which is steadily improved with a growing number of (partially) supervised transcriptions.
	Address	Funchal, 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	978-989-8425-14-0	Medium
	Area		Expedition		Conference	PRIS
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ STP2010			Serial	1868
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	Author	Marta Teres; Eduard Vazquez
	Title	Museums, spaces and museographical resources. Current state and proposals for a multidisciplinary framework to open new perspectives			Type	Conference Article
	Year	2010	Publication	Proceedings of The CREATE 2010 Conference	Abbreviated Journal
	Volume		Issue		Pages	319–323
	Keywords
	Abstract	Two of the main aims of a museum are to communicate its heritage and to make enjoy its visitors. This communication can be done through the pieces itself and the museographical resources but also through the building, the interior design, the light and the colour. Art museums, in opposition with other museums, lack on the application of these additional resources. Such a work necessarily requires a multidisciplinary point of view for a holistic vision of all what a museum implies and to use all its potential as a tool of knowledge and culture for all the visitors.
	Address	Gjovik, Norway
	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	CREATE
	Notes				Approved	no
	Call Number	Admin @ si @ TeV2010			Serial	1298
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	Author	Javier Vazquez; G. D. Finlayson; Maria Vanrell
	Title	A compact singularity function to predict WCS data and unique hues			Type	Conference Article
	Year	2010	Publication	5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science	Abbreviated Journal
	Volume		Issue		Pages	33–38
	Keywords
	Abstract	Understanding how colour is used by the human vision system is a widely studied research field. The field, though quite advanced, still faces important unanswered questions. One of them is the explanation of the unique hues and the assignment of color names. This problem addresses the fact of different perceptual status for different colors. Recently, Philipona and O'Regan have proposed a biological model that allows to extract the reflection properties of any surface independently of the lighting conditions. These invariant properties are the basis to compute a singularity index that predicts the asymmetries presented in unique hues and basic color categories psychophysical data, therefore is giving a further step in their explanation. In this paper we build on their formulation and propose a new singularity index. This new formulation equally accounts for the location of the 4 peaks of the World colour survey and has two main advantages. First, it is a simple elegant numerical measure (the Philipona measurement is a rather cumbersome formula). Second, we develop a colour-based explanation for the measure.
	Address	Joensuu, Finland
	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	9781617388897	Medium
	Area		Expedition		Conference	CGIV/MCS
	Notes	CIC			Approved	no
	Call Number	CAT @ cat @ VFV2010			Serial	1324
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	Author	Jose Manuel Alvarez; Theo Gevers; Antonio Lopez
	Title	3D Scene Priors for Road Detection			Type	Conference Article
	Year	2010	Publication	23rd IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	57–64
	Keywords	road detection
	Abstract	Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods.
	Address	San Francisco; CA; USA; June 2010
	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	1063-6919	ISBN	978-1-4244-6984-0	Medium
	Area		Expedition		Conference	CVPR
	Notes	ADAS;ISE			Approved	no
	Call Number	ADAS @ adas @ AGL2010a			Serial	1302
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	Author	Albert Gordo; Florent Perronnin
	Title	A Bag-of-Pages Approach to Unordered Multi-Page Document Classification			Type	Conference Article
	Year	2010	Publication	20th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1920–1923
	Keywords
	Abstract	We consider the problem of classifying documents containing multiple unordered pages. For this purpose, we propose a novel bag-of-pages document representation. To represent a document, one assigns every page to a prototype in a codebook of pages. This leads to a histogram representation which can then be fed to any discriminative classifier. We also consider several refinements over this initial approach. We show on two challenging datasets that the proposed approach significantly outperforms a baseline system.
	Address	Istanbul (Turkey)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN	978-1-4244-7542-1	Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ GoP2010			Serial	1480
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	Author	Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva
	Title	Conditional Random Fields for image segmentation in Intravascular Ultrasound			Type	Conference Article
	Year	2010	Publication	Medical Image Computing in Catalunya: Graduate Student Workshop	Abbreviated Journal
	Volume		Issue		Pages	13–14
	Keywords
	Abstract	We present a Conditional Random Fields based approach for segmenting Intravascular Ultrasond (IVUS) images. The presented method uses a contextual discriminative graphical model to deal with the presence of distorsions and artifacts in IVUS images, that turns the segmentation of interesting regions into a difficult task. An accurate lumen segmentation on IVUS longitudinal images is achieved.
	Address	Girona
	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	MICCAT
	Notes	MILAB;HUPBA			Approved	no
	Call Number	BCNPCL @ bcnpcl @ CPF2010			Serial	1453
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	Author	Muhammad Muzzamil Luqman; Thierry Brouard; Jean-Yves Ramel; Josep Llados
	Title	A Content Spotting System For Line Drawing Graphic Document Images			Type	Conference Article
	Year	2010	Publication	20th International Conference on Pattern Recognition	Abbreviated Journal
	Volume	20	Issue		Pages	3420–3423
	Keywords
	Abstract	We present a content spotting system for line drawing graphic document images. The proposed system is sufficiently domain independent and takes the keyword based information retrieval for graphic documents, one step forward, to Query By Example (QBE) and focused retrieval. During offline learning mode: we vectorize the documents in the repository, represent them by attributed relational graphs, extract regions of interest (ROIs) from them, convert each ROI to a fuzzy structural signature, cluster similar signatures to form ROI classes and build an index for the repository. During online querying mode: a Bayesian network classifier recognizes the ROIs in the query image and the corresponding documents are fetched by looking up in the repository index. Experimental results are presented for synthetic images of architectural and electronic documents.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN	978-1-4244-7542-1	Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ LBR2010b			Serial	1460
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	Author	Francesco Ciompi; Oriol Pujol; Petia Radeva
	Title	A meta-learning approach to Conditional Random Fields using Error-Correcting Output Codes			Type	Conference Article
	Year	2010	Publication	20th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	710–713
	Keywords
	Abstract	We present a meta-learning framework for the design of potential functions for Conditional Random Fields. The design of both node potential and edge potential is formulated as a classification problem where margin classifiers are used. The set of state transitions for the edge potential is treated as a set of different classes, thus defining a multi-class learning problem. The Error-Correcting Output Codes (ECOC) technique is used to deal with the multi-class problem. Furthermore, the point defined by the combination of margin classifiers in the ECOC space is interpreted in a probabilistic manner, and the obtained distance values are then converted into potential values. The proposed model exhibits very promising results when applied to two real detection problems.
	Address	Istanbul;Turkey
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN	978-1-4244-7542-1	Medium
	Area		Expedition		Conference	ICPR
	Notes	MILAB;HUPBA			Approved	no
	Call Number	BCNPCL @ bcnpcl @ CPR2010a			Serial	1365
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