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	Author	Farshad Nourbakhsh; Dimosthenis Karatzas; Ernest Valveny
	Title	A polar-based logo representation based on topological and colour features			Type	Conference Article
	Year	2010	Publication	9th IAPR International Workshop on Document Analysis Systems	Abbreviated Journal
	Volume		Issue		Pages	341–348
	Keywords
	Abstract	In this paper, we propose a novel rotation and scale invariant method for colour logo retrieval and classification, which involves performing a simple colour segmentation and subsequently describing each of the resultant colour components based on a set of topological and colour features. A polar representation is used to represent the logo and the subsequent logo matching is based on Cyclic Dynamic Time Warping (CDTW). We also show how combining information about the global distribution of the logo components and their local neighbourhood using the Delaunay triangulation allows to improve the results. All experiments are performed on a dataset of 2500 instances of 100 colour logo images in different rotations and scales.
	Address	Boston; USA;
	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	978-1-60558-773-8	Medium
	Area		Expedition		Conference	DAS
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ NKV2010			Serial	1436
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	Author	Sebastien Mace; Herve Locteau; Ernest Valveny; Salvatore Tabbone
	Title	A system to detect rooms in architectural floor plan images			Type	Conference Article
	Year	2010	Publication	9th IAPR International Workshop on Document Analysis Systems	Abbreviated Journal
	Volume		Issue		Pages	167–174
	Keywords
	Abstract	In this article, a system to detect rooms in architectural floor plan images is described. We first present a primitive extraction algorithm for line detection. It is based on an original coupling of classical Hough transform with image vectorization in order to perform robust and efficient line detection. We show how the lines that satisfy some graphical arrangements are combined into walls. We also present the way we detect some door hypothesis thanks to the extraction of arcs. Walls and door hypothesis are then used by our room segmentation strategy; it consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines between regions can also be rough. We take advantage of knowledge associated to architectural floor plans in order to obtain mostly rectangular rooms. Qualitative and quantitative evaluations performed on a corpus of real documents show promising results.
	Address	Boston; USA
	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	978-1-60558-773-8	Medium
	Area		Expedition		Conference	DAS
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ MLV2010			Serial	1437
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	Author	Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Juan J. Villanueva
	Title	Recursive Coarse-to-Fine Localization for fast Object Recognition			Type	Conference Article
	Year	2010	Publication	11th European Conference on Computer Vision	Abbreviated Journal
	Volume	6313	Issue	II	Pages	280–293
	Keywords
	Abstract	Cascading techniques are commonly used to speed-up the scan of an image for object detection. However, cascades of detectors are slow to train due to the high number of detectors and corresponding thresholds to learn. Furthermore, they do not use any prior knowledge about the scene structure to decide where to focus the search. To handle these problems, we propose a new way to scan an image, where we couple a recursive coarse-to-fine refinement together with spatial constraints of the object location. For doing that we split an image into a set of uniformly distributed neighborhood regions, and for each of these we apply a local greedy search over feature resolutions. The neighborhood is defined as a scanning region that only one object can occupy. Therefore the best hypothesis is obtained as the location with maximum score and no thresholds are needed. We present an implementation of our method using a pyramid of HOG features and we evaluate it on two standard databases, VOC2007 and INRIA dataset. Results show that the Recursive Coarse-to-Fine Localization (RCFL) achieves a 12x speed-up compared to standard sliding windows. Compared with a cascade of multiple resolutions approach our method has slightly better performance in speed and Average-Precision. Furthermore, in contrast to cascading approach, the speed-up is independent of image conditions, the number of detected objects and clutter.
	Address	Crete (Greece)
	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	978-3-642-15566-6	Medium
	Area		Expedition		Conference	ECCV
	Notes	ISE			Approved	no
	Call Number	DAG @ dag @ PGB2010			Serial	1438
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	Author	Carles Fernandez; Jordi Gonzalez; Xavier Roca
	Title	Automatic Learning of Background Semantics in Generic Surveilled Scenes			Type	Conference Article
	Year	2010	Publication	11th European Conference on Computer Vision	Abbreviated Journal
	Volume	6313	Issue	II	Pages	678–692
	Keywords
	Abstract	Advanced surveillance systems for behavior recognition in outdoor traffic scenes depend strongly on the particular configuration of the scenario. Scene-independent trajectory analysis techniques statistically infer semantics in locations where motion occurs, and such inferences are typically limited to abnormality. Thus, it is interesting to design contributions that automatically categorize more specific semantic regions. State-of-the-art approaches for unsupervised scene labeling exploit trajectory data to segment areas like sources, sinks, or waiting zones. Our method, in addition, incorporates scene-independent knowledge to assign more meaningful labels like crosswalks, sidewalks, or parking spaces. First, a spatiotemporal scene model is obtained from trajectory analysis. Subsequently, a so-called GI-MRF inference process reinforces spatial coherence, and incorporates taxonomy-guided smoothness constraints. Our method achieves automatic and effective labeling of conceptual regions in urban scenarios, and is robust to tracking errors. Experimental validation on 5 surveillance databases has been conducted to assess the generality and accuracy of the segmentations. The resulting scene models are used for model-based behavior analysis.
	Address	Crete (Greece)
	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	978-3-642-15551-2	Medium
	Area		Expedition		Conference	ECCV
	Notes	ISE			Approved	no
	Call Number	ISE @ ise @ FGR2010			Serial	1439
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	Author	Herve Locteau; Sebastien Mace; Ernest Valveny; Salvatore Tabbone
	Title	Extraction des pieces de un plan de habitation			Type	Conference Article
	Year	2010	Publication	Colloque Internacional Francophone de l´Ecrit et le Document	Abbreviated Journal
	Volume		Issue		Pages	1–12
	Keywords
	Abstract	In this article, a method to extract the rooms of an architectural floor plan image is described. We first present a line detection algorithm to extract long lines in the image. Those lines are analyzed to identify the existing walls. From this point, room extraction can be seen as a classical segmentation task for which each region corresponds to a room. The chosen resolution strategy consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines can also be rough. Thus, we take advantage of knowledge associated to architectural floor plans in order to obtain mainly rectangular rooms. Preliminary tests on a set of real documents show promising results.
	Address	Sousse, Tunisia
	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	CIFED
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ LMV2010			Serial	1440
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	Author	Carlo Gatta; Simone Balocco; Francesco Ciompi; R. Hemetsberger; Oriol Rodriguez-Leor; Petia Radeva
	Title	Real-time gating of IVUS sequences based on motion blur analysis: Method and quantitative validation			Type	Conference Article
	Year	2010	Publication	13th international conference on Medical image computing and computer-assisted intervention	Abbreviated Journal
	Volume	II	Issue		Pages	59-67
	Keywords
	Abstract	Intravascular Ultrasound (IVUS) is an image-guiding technique for cardiovascular diagnostic, providing cross-sectional images of vessels. During the acquisition, the catheter is pulled back (pullback) at a constant speed in order to acquire spatially subsequent images of the artery. However, during this procedure, the heart twist produces a swinging fluctuation of the probe position along the vessel axis. In this paper we propose a real-time gating algorithm based on the analysis of motion blur variations during the IVUS sequence. Quantitative tests performed on an in-vitro ground truth data base shown that our method is superior to state of the art algorithms both in computational speed and accuracy.
	Address
	Corporate Author				Thesis
	Publisher	Springer-Verlag Berlin	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	MICCAI
	Notes	MILAB			Approved	no
	Call Number	BCNPCL @ bcnpcl @ GBC2010			Serial	1447
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	Author	Xavier Otazu; C. Alejandro Parraga; Maria Vanrell
	Title	Towards a unified chromatic inducction model			Type	Journal Article
	Year	2010	Publication	Journal of Vision	Abbreviated Journal	VSS
	Volume	10	Issue	12:5	Pages	1-24
	Keywords	Visual system; Color induction; Wavelet transform
	Abstract	In a previous work (X. Otazu, M. Vanrell, & C. A. Párraga, 2008b), we showed how several brightness induction effects can be predicted using a simple multiresolution wavelet model (BIWaM). Here we present a new model for chromatic induction processes (termed Chromatic Induction Wavelet Model or CIWaM), which is also implemented on a multiresolution framework and based on similar assumptions related to the spatial frequency and the contrast surround energy of the stimulus. The CIWaM can be interpreted as a very simple extension of the BIWaM to the chromatic channels, which in our case are defined in the MacLeod-Boynton (lsY) color space. This new model allows us to unify both chromatic assimilation and chromatic contrast effects in a single mathematical formulation. The predictions of the CIWaM were tested by means of several color and brightness induction experiments, which showed an acceptable agreement between model predictions and psychophysical data.
	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	CIC			Approved	no
	Call Number	CAT @ cat @ OPV2010			Serial	1450
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	Author	Jose Manuel Alvarez; Theo Gevers; Antonio Lopez
	Title	Learning photometric invariance for object detection			Type	Journal Article
	Year	2010	Publication	International Journal of Computer Vision	Abbreviated Journal	IJCV
	Volume	90	Issue	1	Pages	45-61
	Keywords	road detection
	Abstract	Impact factor: 3.508 (the last available from JCR2009SCI). Position 4/103 in the category Computer Science, Artificial Intelligence. Quartile Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time. Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods
	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	ADAS;ISE			Approved	no
	Call Number	ADAS @ adas @ AGL2010c			Serial	1451
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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; Josep Llados; Jean-Yves Ramel; Thierry Brouard
	Title	A Fuzzy-Interval Based Approach For Explicit Graph Embedding, Recognizing Patterns in Signals, Speech, Images and Video			Type	Conference Article
	Year	2010	Publication	20th International Conference on Pattern Recognition	Abbreviated Journal
	Volume	6388	Issue		Pages	93–98
	Keywords
	Abstract	We present a new method for explicit graph embedding. Our algorithm extracts a feature vector for an undirected attributed graph. The proposed feature vector encodes details about the number of nodes, number of edges, node degrees, the attributes of nodes and the attributes of edges in the graph. The first two features are for the number of nodes and the number of edges. These are followed by w features for node degrees, m features for k node attributes and n features for l edge attributes — which represent the distribution of node degrees, node attribute values and edge attribute values, and are obtained by defining (in an unsupervised fashion), fuzzy-intervals over the list of node degrees, node attributes and edge attributes. Experimental results are provided for sample data of ICPR2010 contest GEPR.
	Address
	Corporate Author				Thesis
	Publisher	Springer, 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	978-3-642-17710-1	Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ LLR2010			Serial	1459
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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	Jaume Gibert; Ernest Valveny; Horst Bunke
	Title	Graph of Words Embedding for Molecular Structure-Activity Relationship Analysis			Type	Conference Article
	Year	2010	Publication	15th Iberoamerican Congress on Pattern Recognition	Abbreviated Journal
	Volume	6419	Issue		Pages	30–37
	Keywords
	Abstract	Structure-Activity relationship analysis aims at discovering chemical activity of molecular compounds based on their structure. In this article we make use of a particular graph representation of molecules and propose a new graph embedding procedure to solve the problem of structure-activity relationship analysis. The embedding is essentially an arrangement of a molecule in the form of a vector by considering frequencies of appearing atoms and frequencies of covalent bonds between them. Results on two benchmark databases show the effectiveness of the proposed technique in terms of recognition accuracy while avoiding high operational costs in the transformation.
	Address	Sao Paulo, Brazil
	Corporate Author				Thesis
	Publisher		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	978-3-642-16686-0	Medium
	Area		Expedition		Conference	CIARP
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ GVB2010			Serial	1462
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	Author	Ariel Amato; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez
	Title	Robust Real-Time Background Subtraction Based on Local Neighborhood Patterns			Type	Journal Article
	Year	2010	Publication	EURASIP Journal on Advances in Signal Processing	Abbreviated Journal	EURASIPJ
	Volume		Issue		Pages	7
	Keywords
	Abstract	Article ID 901205 This paper describes an efficient background subtraction technique for detecting moving objects. The proposed approach is able to overcome difficulties like illumination changes and moving shadows. Our method introduces two discriminative features based on angular and modular patterns, which are formed by similarity measurement between two sets of RGB color vectors: one belonging to the background image and the other to the current image. We show how these patterns are used to improve foreground detection in the presence of moving shadows and in the case when there are strong similarities in color between background and foreground pixels. Experimental results over a collection of public and own datasets of real image sequences demonstrate that the proposed technique achieves a superior performance compared with state-of-the-art methods. Furthermore, both the low computational and space complexities make the presented algorithm feasible for real-time 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	1110-8657	ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE			Approved	no
	Call Number	ISE @ ise @ AMR2010			Serial	1463
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	Author	Pierluigi Casale; Oriol Pujol; Petia Radeva
	Title	Embedding Random Projections in Regularized Gradient Boosting Machines			Type	Conference Article
	Year	2010	Publication	Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases	Abbreviated Journal
	Volume		Issue		Pages	44–53
	Keywords
	Abstract
	Address	Barcelona (Spain)
	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	SUEMA
	Notes	MILAB;HUPBA			Approved	no
	Call Number	BCNPCL @ bcnpcl @ CPR2010c			Serial	1466
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	Author	Pierluigi Casale; Oriol Pujol; Petia Radeva
	Title	Classyfing Agitation in Sedated ICU Patients			Type	Conference Article
	Year	2010	Publication	Medical Image Computing in Catalunya: Graduate Student Workshop	Abbreviated Journal
	Volume		Issue		Pages	19–20
	Keywords
	Abstract	Agitation is a serious problem in sedated intensive care unit (ICU) patients. In this work, standard machine learning techniques working on wearable accelerometer data have been used to classifying agitation levels achieving very good classification performances.
	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 @ COR2010			Serial	1467
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