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	Author	Francesco Brughi
	Title	Artistic Heritage Motive Retrieval: an Explorative Study			Type	Report
	Year	2013	Publication	CVC Technical Report	Abbreviated Journal
	Volume	176	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	IAM			Approved	no
	Call Number	Admin @ si @ Bru2013			Serial	2410
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	Author	Bhaskar Chakraborty; Jordi Gonzalez; Xavier Roca
	Title	Large scale continuous visual event recognition using max-margin Hough transformation framework			Type	Journal Article
	Year	2013	Publication	Computer Vision and Image Understanding	Abbreviated Journal	CVIU
	Volume	117	Issue	10	Pages	1356–1368
	Keywords
	Abstract	In this paper we propose a novel method for continuous visual event recognition (CVER) on a large scale video dataset using max-margin Hough transformation framework. Due to high scalability, diverse real environmental state and wide scene variability direct application of action recognition/detection methods such as spatio-temporal interest point (STIP)-local feature based technique, on the whole dataset is practically infeasible. To address this problem, we apply a motion region extraction technique which is based on motion segmentation and region clustering to identify possible candidate “event of interest” as a preprocessing step. On these candidate regions a STIP detector is applied and local motion features are computed. For activity representation we use generalized Hough transform framework where each feature point casts a weighted vote for possible activity class centre. A max-margin frame work is applied to learn the feature codebook weight. For activity detection, peaks in the Hough voting space are taken into account and initial event hypothesis is generated using the spatio-temporal information of the participating STIPs. For event recognition a verification Support Vector Machine is used. An extensive evaluation on benchmark large scale video surveillance dataset (VIRAT) and as well on a small scale benchmark dataset (MSR) shows that the proposed method is applicable on a wide range of continuous visual event recognition applications having extremely challenging conditions.
	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	1077-3142	ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE			Approved	no
	Call Number	Admin @ si @ CGR2013			Serial	2413
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	Author	Ferran Poveda
	Title	Computer Graphics and Vision Techniques for the Study of the Muscular Fiber Architecture of the Myocardium			Type	Book Whole
	Year	2013	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher		Place of Publication		Editor	Debora Gil;Enric Marti
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM			Approved	no
	Call Number	Admin @ si @ Pov2013			Serial	2417
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	Author	Miquel Ferrer; I. Bardaji; Ernest Valveny; Dimosthenis Karatzas; Horst Bunke
	Title	Median Graph Computation by Means of Graph Embedding into Vector Spaces			Type	Book Chapter
	Year	2013	Publication	Graph Embedding for Pattern Analysis	Abbreviated Journal
	Volume		Issue		Pages	45-72
	Keywords
	Abstract	In pattern recognition [8, 14], a key issue to be addressed when designing a system is how to represent input patterns. Feature vectors is a common option. That is, a set of numerical features describing relevant properties of the pattern are computed and arranged in a vector form. The main advantages of this kind of representation are computational simplicity and a well sound mathematical foundation. Thus, a large number of operations are available to work with vectors and a large repository of algorithms for pattern analysis and classification exist. However, the simple structure of feature vectors might not be the best option for complex patterns where nonnumerical features or relations between different parts of the pattern become relevant.
	Address
	Corporate Author				Thesis
	Publisher	Springer New York	Place of Publication		Editor	Yun Fu; Yungian Ma
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4614-4456-5	Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ FBV2013			Serial	2421
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	Author	J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin
	Title	Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm			Type	Journal Article
	Year	2013	Publication	Expert Systems with Applications	Abbreviated Journal	EXWA
	Volume	40	Issue	17	Pages	6707-6712
	Keywords	Neural gas; Expert vision; Eye-tracking; Fixations
	Abstract	Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves.
	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	0957-4174	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ CRM2013			Serial	2438
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	Author	Katerine Diaz; Francesc J. Ferri
	Title	Extensiones del método de vectores comunes discriminantes Aplicadas a la clasificación de imágenes			Type	Book Whole
	Year	2013	Publication	Extensiones del método de vectores comunes discriminantes Aplicadas a la clasificación de imágenes	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Los métodos basados en subespacios son una herramienta muy utilizada en aplicaciones de visión por computador. Aquí se presentan y validan algunos algoritmos que hemos propuesto en este campo de investigación. El primer algoritmo está relacionado con una extensión del método de vectores comunes discriminantes con kernel, que reinterpreta el espacio nulo de la matriz de dispersión intra-clase del conjunto de entrenamiento para obtener las características discriminantes. Dentro de los métodos basados en subespacios existen diferentes tipos de entrenamiento. Uno de los más populares, pero no por ello uno de los más eficientes, es el aprendizaje por lotes. En este tipo de aprendizaje, todas las muestras del conjunto de entrenamiento tienen que estar disponibles desde el inicio. De este modo, cuando nuevas muestras se ponen a disposición del algoritmo, el sistema tiene que ser reentrenado de nuevo desde cero. Una alternativa a este tipo de entrenamiento es el aprendizaje incremental. Aquí se proponen diferentes algoritmos incrementales del método de vectores comunes discriminantes.
	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	978-3-639-55339-0	Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ DiF2013			Serial	2440
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	Author	Naveen Onkarappa
	Title	Optical Flow in Driver Assistance Systems			Type	Book Whole
	Year	2013	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Motion perception is one of the most important attributes of the human brain. Visual motion perception consists in inferring speed and direction of elements in a scene based on visual inputs. Analogously, computer vision is assisted by motion cues in the scene. Motion detection in computer vision is useful in solving problems such as segmentation, depth from motion, structure from motion, compression, navigation and many others. These problems are common in several applications, for instance, video surveillance, robot navigation and advanced driver assistance systems (ADAS). One of the most widely used techniques for motion detection is the optical flow estimation. The work in this thesis attempts to make optical flow suitable for the requirements and conditions of driving scenarios. In this context, a novel space-variant representation called reverse log-polar representation is proposed that is shown to be better than the traditional log-polar space-variant representation for ADAS. The space-variant representations reduce the amount of data to be processed. Another major contribution in this research is related to the analysis of the influence of specific characteristics from driving scenarios on the optical flow accuracy. Characteristics such as vehicle speed and road texture are considered in the aforementioned analysis. From this study, it is inferred that the regularization weight has to be adapted according to the required error measure and for different speeds and road textures. It is also shown that polar represented optical flow suits driving scenarios where predominant motion is translation. Due to the requirements of such a study and by the lack of needed datasets a new synthetic dataset is presented; it contains: i) sequences of different speeds and road textures in an urban scenario; ii) sequences with complex motion of an on-board camera; and iii) sequences with additional moving vehicles in the scene. The ground-truth optical flow is generated by the ray-tracing technique. Further, few applications of optical flow in ADAS are shown. Firstly, a robust RANSAC based technique to estimate horizon line is proposed. Then, an egomotion estimation is presented to compare the proposed space-variant representation with the classical one. As a final contribution, a modification in the regularization term is proposed that notably improves the results in the ADAS applications. This adaptation is evaluated using a state of the art optical flow technique. The experiments on a public dataset (KITTI) validate the advantages of using the proposed modification.
	Address	Bellaterra
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	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	978-84-940902-1-9	Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ Nav2013			Serial	2447
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	Author	Mohammad Rouhani; Angel Sappa
	Title	The Richer Representation the Better Registration			Type	Journal Article
	Year	2013	Publication	IEEE Transactions on Image Processing	Abbreviated Journal	TIP
	Volume	22	Issue	12	Pages	5036-5049
	Keywords
	Abstract	In this paper, the registration problem is formulated as a point to model distance minimization. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, this formulation avoids the correspondence search that is time-consuming. In the first stage, the target set is described through an implicit function by employing a linear least squares fitting. This function can be either an implicit polynomial or an implicit B-spline from a coarse to fine representation. In the second stage, we show how the obtained implicit representation is used as an interface to convert point-to-point registration into point-to-implicit problem. Furthermore, we show that this registration distance is smooth and can be minimized through the Levengberg-Marquardt algorithm. All the formulations presented for both stages are compact and easy to implement. In addition, we show that our registration method can be handled using any implicit representation though some are coarse and others provide finer representations; hence, a tradeoff between speed and accuracy can be set by employing the right implicit function. Experimental results and comparisons in 2D and 3D show the robustness and the speed of convergence of the proposed approach.
	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	1057-7149	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ RoS2013			Serial	2665
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	Author	A.S. Coquel; Jean-Pascal Jacob; M. Primet; A. Demarez; Mariella Dimiccoli; T. Julou; L. Moisan; A. Lindner; H. Berry
	Title	Localization of protein aggregation in Escherichia coli is governed by diffusion and nucleoid macromolecular crowding effect			Type	Journal Article
	Year	2013	Publication	Plos Computational Biology	Abbreviated Journal	PCB
	Volume	9	Issue	4	Pages
	Keywords
	Abstract	Aggregates of misfolded proteins are a hallmark of many age-related diseases. Recently, they have been linked to aging of Escherichia coli (E. coli) where protein aggregates accumulate at the old pole region of the aging bacterium. Because of the potential of E. coli as a model organism, elucidating aging and protein aggregation in this bacterium may pave the way to significant advances in our global understanding of aging. A first obstacle along this path is to decipher the mechanisms by which protein aggregates are targeted to specific intercellular locations. Here, using an integrated approach based on individual-based modeling, time-lapse fluorescence microscopy and automated image analysis, we show that the movement of aging-related protein aggregates in E. coli is purely diffusive (Brownian). Using single-particle tracking of protein aggregates in live E. coli cells, we estimated the average size and diffusion constant of the aggregates. Our results provide evidence that the aggregates passively diffuse within the cell, with diffusion constants that depend on their size in agreement with the Stokes-Einstein law. However, the aggregate displacements along the cell long axis are confined to a region that roughly corresponds to the nucleoid-free space in the cell pole, thus confirming the importance of increased macromolecular crowding in the nucleoids. We thus used 3D individual-based modeling to show that these three ingredients (diffusion, aggregation and diffusion hindrance in the nucleoids) are sufficient and necessary to reproduce the available experimental data on aggregate localization in the cells. Taken together, our results strongly support the hypothesis that the localization of aging-related protein aggregates in the poles of E. coli results from the coupling of passive diffusion-aggregation with spatially non-homogeneous macromolecular crowding. They further support the importance of “soft” intracellular structuring (based on macromolecular crowding) in diffusion-based protein localization in E. coli.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	: Stanislav Shvartsman, Princeton University, United States of America
	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 @CJP2013			Serial	2786
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	Author	Mariella Dimiccoli; Benoît Girard; Alain Berthoz; Daniel Bennequin
	Title	Striola Magica: a functional explanation of otolith organs			Type	Journal Article
	Year	2013	Publication	Journal of Computational Neuroscience	Abbreviated Journal	JCN
	Volume	35	Issue	2	Pages	125-154
	Keywords	Otolith organs ;Striola; Vestibular pathway
	Abstract	Otolith end organs of vertebrates sense linear accelerations of the head and gravitation. The hair cells on their epithelia are responsible for transduction. In mammals, the striola, parallel to the line where hair cells reverse their polarization, is a narrow region centered on a curve with curvature and torsion. It has been shown that the striolar region is functionally different from the rest, being involved in a phasic vestibular pathway. We propose a mathematical and computational model that explains the necessity of this amazing geometry for the striola to be able to carry out its function. Our hypothesis, related to the biophysics of the hair cells and to the physiology of their afferent neurons, is that striolar afferents collect information from several type I hair cells to detect the jerk in a large domain of acceleration directions. This predicts a mean number of two calyces for afferent neurons, as measured in rodents. The domain of acceleration directions sensed by our striolar model is compatible with the experimental results obtained on monkeys considering all afferents. Therefore, the main result of our study is that phasic and tonic vestibular afferents cover the same geometrical fields, but at different dynamical and frequency domains.
	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	1573-6873. 2013	ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @DBG2013			Serial	2787
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	Author	Marc Bolaños; Maite Garolera; Petia Radeva
	Title	Active labeling application applied to food-related object recognition			Type	Conference Article
	Year	2013	Publication	5th International Workshop on Multimedia for Cooking & Eating Activities	Abbreviated Journal
	Volume		Issue		Pages	45-50
	Keywords
	Abstract	Every day, lifelogging devices, available for recording different aspects of our daily life, increase in number, quality and functions, just like the multiple applications that we give to them. Applying wearable devices to analyse the nutritional habits of people is a challenging application based on acquiring and analyzing life records in long periods of time. However, to extract the information of interest related to the eating patterns of people, we need automatic methods to process large amount of life-logging data (e.g. recognition of food-related objects). Creating a rich set of manually labeled samples to train the algorithms is slow, tedious and subjective. To address this problem, we propose a novel method in the framework of Active Labeling for construct- ing a training set of thousands of images. Inspired by the hierarchical sampling method for active learning [6], we propose an Active forest that organizes hierarchically the data for easy and fast labeling. Moreover, introducing a classifier into the hierarchical structures, as well as transforming the feature space for better data clustering, additionally im- prove the algorithm. Our method is successfully tested to label 89.700 food-related objects and achieves significant reduction in expert time labelling. Active labeling application applied to food-related object recognition ResearchGate. Available from: http://www.researchgate.net/publication/262252017Activelabelingapplicationappliedtofood-relatedobjectrecognition [accessed Jul 14, 2015].
	Address	Barcelona; October 2013
	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	ACM-CEA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ BGR2013b			Serial	2637
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	Author	Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades
	Title	Document noise removal using sparse representations over learned dictionary			Type	Conference Article
	Year	2013	Publication	Symposium on Document engineering	Abbreviated Journal
	Volume		Issue		Pages	161-168
	Keywords
	Abstract	best paper award In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental results on several datasets demonstrate the robustness of our method compared with the state-of-the-art.
	Address	Barcelona; October 2013
	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	978-1-4503-1789-4	Medium
	Area		Expedition		Conference	ACM-DocEng
	Notes	DAG; 600.061			Approved	no
	Call Number	Admin @ si @ DTR2013a			Serial	2330
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	Author	H. Emrah Tasli; Jan van Gemert; Theo Gevers
	Title	Spot the differences: from a photograph burst to the single best picture			Type	Conference Article
	Year	2013	Publication	21ST ACM International Conference on Multimedia	Abbreviated Journal
	Volume		Issue		Pages	729-732
	Keywords
	Abstract	With the rise of the digital camera, people nowadays typically take several near-identical photos of the same scene to maximize the chances of a good shot. This paper proposes a user-friendly tool for exploring a personal photo gallery for selecting or even creating the best shot of a scene between its multiple alternatives. This functionality is realized through a graphical user interface where the best viewpoint can be selected from a generated panorama of the scene. Once the viewpoint is selected, the user is able to go explore possible alternatives coming from the other images. Using this tool, one can explore a photo gallery efficiently. Moreover, additional compositions from other images are also possible. With such additional compositions, one can go from a burst of photographs to the single best one. Even funny compositions of images, where you can duplicate a person in the same image, are possible with our proposed tool.
	Address	Barcelona
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ACM-MM
	Notes	ALTRES;ISE			Approved	no
	Call Number	TGG2013			Serial	2368
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	Author	Sezer Karaoglu; Jan van Gemert; Theo Gevers
	Title	Con-text: text detection using background connectivity for fine-grained object classification			Type	Conference Article
	Year	2013	Publication	21ST ACM International Conference on Multimedia	Abbreviated Journal
	Volume		Issue		Pages	757-760
	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	ACM-MM
	Notes	ALTRES;ISE			Approved	no
	Call Number	Admin @ si @ KGG2013			Serial	2369
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	Author	Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
	Title	Logo recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers			Type	Conference Article
	Year	2013	Publication	26th Canadian Conference on Artificial Intelligence	Abbreviated Journal
	Volume	7884	Issue		Pages	1-12
	Keywords	Logo recognition; ensemble classification; Dempster-Shafer fusion; Zernike moments; generic Fourier descriptor; shape signature
	Abstract	Best paper award The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology.
	Address	Canada; May 2013
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-38456-1	Medium
	Area		Expedition		Conference	AI
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ BGE2013b			Serial	2249
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