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	Author	Jorge Bernal; Fernando Vilariño; F. Javier Sanchez
	Title	Feature Detectors and Feature Descriptors: Where We Are Now			Type	Report
	Year	2010	Publication	CVC Technical Report	Abbreviated Journal
	Volume	154	Issue		Pages
	Keywords
	Abstract	Feature Detection and Feature Description are clearly nowadays topics. Many Computer Vision applications rely on the use of several of these techniques in order to extract the most significant aspects of an image so they can help in some tasks such as image retrieval, image registration, object recognition, object categorization and texture classification, among others. In this paper we define what Feature Detection and Description are and then we present an extensive collection of several methods in order to show the different techniques that are being used right now. The aim of this report is to provide a glimpse of what is being used currently in these fields and to serve as a starting point for future endeavours.
	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	800	Expedition		Conference
	Notes	MV;SIAI			Approved	no
	Call Number	Admin @ si @ BVS2010; IAM @ iam @ BVS2010			Serial	1348
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	Author	Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
	Title	Reduction of Pattern Search Area in Colonoscopy Images by Merging Non-Informative Regions			Type	Conference Article
	Year	2010	Publication	28th Congreso Anual de la Sociedad Española de Ingeniería Biomédica	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	One of the first usual steps in pattern recognition schemas is image segmentation, in order to reduce the dimensionality of the problem and manage smaller quantity of data. In our case as we are pursuing real-time colon cancer polyp detection, this step is crucial. In this paper we present a non-informative region estimation algorithm that will let us discard some parts of the image where we will not expect to find colon cancer polyps. The performance of our approach will be measured in terms of both non-informative areas elimination and polyps’ areas preserving. The results obtained show the importance of having correct non- informative region estimation in order to fasten the whole recognition process.
	Address	Madrid (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	800	Expedition		Conference	CASEIB
	Notes	MV;SIAI			Approved	no
	Call Number	Admin @ si @ BSV2010			Serial	1469
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	Author	Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva
	Title	Compact Evolutive Design of Error-Correcting Output Codes. Supervised and Unsupervised Ensemble Methods and Applications			Type	Conference Article
	Year	2010	Publication	European Conference on Machine Learning	Abbreviated Journal
	Volume	I	Issue		Pages	119-128
	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	ECML
	Notes	MILAB; OR;HUPBA;MV			Approved	no
	Call Number	Admin @ si @ BEB2010			Serial	1775
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	Author	Thierry Brouard; A. Delaplace; Muhammad Muzzamil Luqman; H. Cardot; Jean-Yves Ramel
	Title	Design of Evolutionary Methods Applied to the Learning of Bayesian Nerwork Structures			Type	Book Chapter
	Year	2010	Publication	Bayesian Network	Abbreviated Journal
	Volume		Issue		Pages	13-37
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher	Sciyo	Place of Publication		Editor	Ahmed Rebai
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-953-307-124-4	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	Admin @ si @ BDL2010			Serial	1461
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	Author	Ekain Artola
	Title	Human Attention Map Prediction Combining Visual Features			Type	Report
	Year	2010	Publication	CVC Technical Report	Abbreviated Journal
	Volume	160	Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis	Bachelor's thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	Admin @ si @ Art2010			Serial	1352
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	Author	Joan Arnedo-Moreno; Agata Lapedriza
	Title	Visualizing key authenticity: turning your face into your public key			Type	Conference Article
	Year	2010	Publication	6th China International Conference on Information Security and Cryptology	Abbreviated Journal
	Volume		Issue		Pages	605-618
	Keywords
	Abstract	Biometric information has become a technology complementary to cryptography, allowing to conveniently manage cryptographic data. Two important needs are ful lled: rst of all, making such data always readily available, and additionally, making its legitimate owner easily identi able. In this work we propose a signature system which integrates face recognition biometrics with and identity-based signature scheme, so the user's face e ectively becomes his public key and system ID. Thus, other users may verify messages using photos of the claimed sender, providing a reasonable trade-o between system security and usability, as well as a much more straightforward public key authenticity and distribution process.
	Address
	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		ISBN		Medium
	Area		Expedition		Conference	Inscrypt
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ ArL2010c			Serial	2149
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	Author	David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo
	Title	Real-time Object Segmentation using a Bag of Features Approach			Type	Conference Article
	Year	2010	Publication	13th International Conference of the Catalan Association for Artificial Intelligence	Abbreviated Journal
	Volume	220	Issue		Pages	321–329
	Keywords	Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors
	Abstract	In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset.
	Address
	Corporate Author				Thesis
	Publisher	IOS Press Amsterdam,	Place of Publication		Editor	In R.Alquezar, A.Moreno, J.Aguilar.
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	9781607506423	Medium
	Area		Expedition		Conference	CCIA
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ ARL2010b			Serial	1417
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	Author	David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo
	Title	Fast and Robust Object Segmentation with the Integral Linear Classifier			Type	Conference Article
	Year	2010	Publication	23rd IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1046–1053
	Keywords
	Abstract	We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets.
	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			Approved	no
	Call Number	Admin @ si @ ARL2010a			Serial	1311
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	Author	Jose Manuel Alvarez
	Title	Combining Context and Appearance for Road Detection			Type	Book Whole
	Year	2010	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Road traffic crashes have become a major cause of death and injury throughout the world. Hence, in order to improve road safety, the automobile manufacture is moving towards the development of vehicles with autonomous functionalities such as keeping in the right lane, safe distance keeping between vehicles or regulating the speed of the vehicle according to the traffic conditions. A key component of these systems is vision–based road detection that aims to detect the free road surface ahead the moving vehicle. Detecting the road using a monocular vision system is very challenging since the road is an outdoor scenario imaged from a mobile platform. Hence, the detection algorithm must be able to deal with continuously changing imaging conditions such as the presence ofdifferent objects (vehicles, pedestrians), different environments (urban, highways, off–road), different road types (shape, color), and different imaging conditions (varying illumination, different viewpoints and changing weather conditions). Therefore, in this thesis, we focus on vision–based road detection using a single color camera. More precisely, we first focus on analyzing and grouping pixels according to their low–level properties. In this way, two different approaches are presented to exploit color and photometric invariance. Then, we focus the research of the thesis on exploiting context information. This information provides relevant knowledge about the road not using pixel features from road regions but semantic information from the analysis of the scene. In this way, we present two different approaches to infer the geometry of the road ahead the moving vehicle. Finally, we focus on combining these context and appearance (color) approaches to improve the overall performance of road detection algorithms. The qualitative and quantitative results presented in this thesis on real–world driving sequences show that the proposed method is robust to varying imaging conditions, road types and scenarios going beyond the state–of–the–art.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Antonio Lopez;Theo Gevers
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-937261-8-8	Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ Alv2010			Serial	1454
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	Author	Jon Almazan
	Title	Deforming the Blurred Shape Model for Shape Description and Recognition			Type	Report
	Year	2010	Publication	CVC Technical Report	Abbreviated Journal
	Volume	163	Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis	Master's thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	Admin @ si @ Alm2010			Serial	1354
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	Author	Joan Serrat; Antonio Lopez
	Title	Deteccion automatica de lineas de carril para la asistencia a la conduccion			Type	Miscellaneous
	Year	2010	Publication	UAB Divulga – Revista de divulgacion cientifica	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	La detección por cámara de las líneas de carril en las carreteras puede ser una solución asequible a los riesgos de conducción generados por los adelantamientos o las salidas de carril. Este trabajo propone un sistema que funciona en tiempo real y que obtiene muy buenos resultados. El sistema está preparado para identificar las líneas en condiciones de visibilidad poco favorables, como puede ser la conducción nocturna o con otros vehículos que dificulten la visión.
	Address	Bellaterra (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
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ SeL2010			Serial	1430
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	Author	Angel Sappa (ed)
	Title	Computer Graphics and Imaging			Type	Book Whole
	Year	2010	Publication	Computer Graphics and Imaging	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	Angel Sappa
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978–0–88986–836–6	Medium
	Area		Expedition		Conference	CGIM
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ Sap2010			Serial	1468
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	Author	Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa
	Title	Multiple-target tracking for the intelligent headlights control			Type	Conference Article
	Year	2010	Publication	13th Annual International Conference on Intelligent Transportation Systems	Abbreviated Journal
	Volume		Issue		Pages	903–910
	Keywords	Intelligent Headlights
	Abstract	TA7.4 Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm.
	Address	Madeira Island (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		Medium
	Area		Expedition		Conference	ITSC
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ RSL2010			Serial	1422
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	Author	Mohammad Rouhani; Angel Sappa
	Title	A Fast accurate Implicit Polynomial Fitting Approach			Type	Conference Article
	Year	2010	Publication	17th IEEE International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages	1429–1432
	Keywords
	Abstract	This paper presents a novel hybrid approach that combines state of the art fitting algorithms: algebraic-based and geometric-based. It consists of two steps; first, the 3L algorithm is used as an initialization and then, the obtained result, is improved through a geometric approach. The adopted geometric approach is based on a distance estimation that avoids costly search for the real orthogonal distance. Experimental results are presented as well as quantitative comparisons.
	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 @ RoS2010b			Serial	1359
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	Author	Mohammad Rouhani; Angel Sappa
	Title	Relaxing the 3L Algorithm for an Accurate Implicit Polynomial Fitting			Type	Conference Article
	Year	2010	Publication	23rd IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3066-3072
	Keywords
	Abstract	This paper presents a novel method to increase the accuracy of linear fitting of implicit polynomials. The proposed method is based on the 3L algorithm philosophy. The novelty lies on the relaxation of the additional constraints, already imposed by the 3L algorithm. Hence, the accuracy of the final solution is increased due to the proper adjustment of the expected values in the aforementioned additional constraints. Although iterative, the proposed approach solves the fitting problem within a linear framework, which is independent of the threshold tuning. Experimental results, both in 2D and 3D, showing improvements in the accuracy of the fitting are presented. Comparisons with both state of the art algorithms and a geometric based one (non-linear fitting), which is used as a ground truth, are provided.
	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			Approved	no
	Call Number	ADAS @ adas @ RoS2010a			Serial	1303
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