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	Author	Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool
	Title	Active MAP Inference in CRFs for Efficient Semantic Segmentation			Type	Conference Article
	Year	2013	Publication	15th IEEE International Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	2312 - 2319
	Keywords	Semantic Segmentation
	Abstract	Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains.
	Address	Sydney; Australia; December 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	1550-5499	ISBN		Medium
	Area		Expedition		Conference	ICCV
	Notes	ADAS; 600.057			Approved	no
	Call Number	ADAS @ adas @ RBN2013			Serial	2377
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	Author	David Vazquez; Antonio Lopez; Daniel Ponsa; David Geronimo
	Title	Interactive Training of Human Detectors			Type	Book Chapter
	Year	2013	Publication	Multiodal Interaction in Image and Video Applications	Abbreviated Journal
	Volume	48	Issue		Pages	169-182
	Keywords	Pedestrian Detection; Virtual World; AdaBoost; Domain Adaptation
	Abstract	Image based human detection remains as a challenging problem. Most promising detectors rely on classifiers trained with labelled samples. However, labelling is a manual labor intensive step. To overcome this problem we propose to collect images of pedestrians from a virtual city, i.e., with automatic labels, and train a pedestrian detector with them, which works fine when such virtual-world data are similar to testing one, i.e., real-world pedestrians in urban areas. When testing data is acquired in different conditions than training one, e.g., human detection in personal photo albums, dataset shift appears. In previous work, we cast this problem as one of domain adaptation and solve it with an active learning procedure. In this work, we focus on the same problem but evaluating a different set of faster to compute features, i.e., Haar, EOH and their combination. In particular, we train a classifier with virtual-world data, using such features and Real AdaBoost as learning machine. This classifier is applied to real-world training images. Then, a human oracle interactively corrects the wrong detections, i.e., few miss detections are manually annotated and some false ones are pointed out too. A low amount of manual annotation is fixed as restriction. Real- and virtual-world difficult samples are combined within what we call cool world and we retrain the classifier with this data. Our experiments show that this adapted classifier is equivalent to the one trained with only real-world data but requiring 90% less manual annotations.
	Address	Springer Heidelberg New York Dordrecht London
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1868-4394	ISBN	978-3-642-35931-6	Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.057; 600.054; 605.203			Approved	no
	Call Number	VLP2013; ADAS @ adas @ vlp2013			Serial	2193
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	Author	Ferran Poveda; Debora Gil; Enric Marti; Albert Andaluz; Manel Ballester;Francesc Carreras Costa
	Title	Helical structure of the cardiac ventricular anatomy assessed by Diffusion Tensor Magnetic Resonance Imaging multi-resolution tractography			Type	Journal Article
	Year	2013	Publication	Revista Española de Cardiología	Abbreviated Journal	REC
	Volume	66	Issue	10	Pages	782-790
	Keywords	Heart;Diffusion magnetic resonance imaging;Diffusion tractography;Helical heart;Myocardial ventricular band.
	Abstract	Deep understanding of myocardial structure linking morphology and function of the heart would unravel crucial knowledge for medical and surgical clinical procedures and studies. Several conceptual models of myocardial fiber organization have been proposed but the lack of an automatic and objective methodology prevented an agreement. We sought to deepen in this knowledge through advanced computer graphic representations of the myocardial fiber architecture by diffusion tensor magnetic resonance imaging (DT-MRI). We performed automatic tractography reconstruction of unsegmented DT-MRI canine heart datasets coming from the public database of the Johns Hopkins University. Full scale tractographies have been build with 200 seeds and are composed by streamlines computed on the vectorial field of primary eigenvectors given at the diffusion tensor volumes. Also, we introduced a novel multi-scale visualization technique in order to obtain a simplified tractography. This methodology allowed to keep the main geometric features of the fiber tracts, making easier to decipher the main properties of the architectural organization of the heart. On the analysis of the output from our tractographic representations we found exact correlation with low-level details of myocardial architecture, but also with the more abstract conceptualization of a continuous helical ventricular myocardial fiber array. Objective analysis of myocardial architecture by an automated method, including the entire myocardium and using several 3D levels of complexity, reveals a continuous helical myocardial fiber arrangement of both right and left ventricles, supporting the anatomical model of the helical ventricular myocardial band described by Torrent-Guasp.
	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	IAM; 600.044; 600.060			Approved	no
	Call Number	IAM @ iam @ PGM2013			Serial	2194
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	Author	David Roche; Debora Gil; Jesus Giraldo
	Title	Mechanistic analysis of the function of agonists and allosteric modulators: Reconciling two-state and operational models			Type	Journal Article
	Year	2013	Publication	British Journal of Pharmacology	Abbreviated Journal	BJP
	Volume	169	Issue	6	Pages	1189-202
	Keywords
	Abstract	Two-state and operational models of both agonism and allosterism are compared to identify and characterize common pharmacological parameters. To account for the receptor-dependent basal response, constitutive receptor activity is considered in the operational models. By arranging two-state models as the fraction of active receptors and operational models as the fractional response relative to the maximum effect of the system, a one-by-one correspondence between parameters is found. The comparative analysis allows a better understanding of complex allosteric interactions. In particular, the inclusion of constitutive receptor activity in the operational model of allosterism allows the characterization of modulators able to lower the basal response of the system; that is, allosteric modulators with negative intrinsic efficacy. Theoretical simulations and overall goodness of fit of the models to simulated data suggest that it is feasible to apply the models to experimental data and constitute one step forward in receptor theory formalism.
	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	IAM; 600.044; 605.203			Approved	no
	Call Number	IAM @ iam @ RGG2013b			Serial	2195
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	Author	Angel Sappa; Jordi Vitria
	Title	Multimodal Interaction in Image and Video Applications			Type	Book Whole
	Year	2013	Publication	Multimodal Interaction in Image and Video Applications	Abbreviated Journal
	Volume	48	Issue		Pages
	Keywords
	Abstract	Book Series Intelligent Systems Reference Library
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1868-4394	ISBN	978-3-642-35931-6	Medium
	Area		Expedition		Conference
	Notes	ADAS; OR;MV			Approved	no
	Call Number	Admin @ si @ SaV2013			Serial	2199
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	Author	Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa
	Title	Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection			Type	Conference Article
	Year	2013	Publication	IEEE Intelligent Vehicles Symposium	Abbreviated Journal
	Volume		Issue		Pages	467 - 472
	Keywords	Pedestrian Detection; Virtual World; Part based
	Abstract	State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii) the multiview pedestrian detector enhances the performance of the pedestrian root model, (iii) a top-down approach is used for part detection which reduces the searching space. We evaluate our model on Daimler and Karlsruhe Pedestrian Benchmarks with publicly available Caltech pedestrian detection evaluation framework and the result outperforms the state-of-the-art latent SVM V4.0, on both average miss rate and speed (our detector is ten times faster).
	Address	Gold Coast; Australia; June 2013
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1931-0587	ISBN	978-1-4673-2754-1	Medium
	Area		Expedition		Conference	IV
	Notes	ADAS; 600.054; 600.057			Approved	no
	Call Number	XVL2013; ADAS @ adas @ xvl2013a			Serial	2214
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	Author	Marina Alberti
	Title	Detection and Alignment of Vascular Structures in Intravascular Ultrasound using Pattern Recognition Techniques			Type	Book Whole
	Year	2013	Publication	PhD Thesis, Universitat de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this thesis, several methods for the automatic analysis of Intravascular Ultrasound (IVUS) sequences are presented, aimed at assisting physicians in the diagnosis, the assessment of the intervention and the monitoring of the patients with coronary disease. The basis for the developed frameworks are machine learning, pattern recognition and image processing techniques. First, a novel approach for the automatic detection of vascular bifurcations in IVUS is presented. The task is addressed as a binary classication problem (identifying bifurcation and non-bifurcation angular sectors in the sequence images). The multiscale stacked sequential learning algorithm is applied, to take into account the spatial and temporal context in IVUS sequences, and the results are rened using a-priori information about branching dimensions and geometry. The achieved performance is comparable to intra- and inter-observer variability. Then, we propose a novel method for the automatic non-rigid alignment of IVUS sequences of the same patient, acquired at dierent moments (before and after percutaneous coronary intervention, or at baseline and follow-up examinations). The method is based on the description of the morphological content of the vessel, obtained by extracting temporal morphological proles from the IVUS acquisitions, by means of methods for segmentation, characterization and detection in IVUS. A technique for non-rigid sequence alignment – the Dynamic Time Warping algorithm - is applied to the proles and adapted to the specic clinical problem. Two dierent robust strategies are proposed to address the partial overlapping between frames of corresponding sequences, and a regularization term is introduced to compensate for possible errors in the prole extraction. The benets of the proposed strategy are demonstrated by extensive validation on synthetic and in-vivo data. The results show the interest of the proposed non-linear alignment and the clinical value of the method. Finally, a novel automatic approach for the extraction of the luminal border in IVUS images is presented. The method applies the multiscale stacked sequential learning algorithm and extends it to 2-D+T, in a rst classication phase (the identi- cation of lumen and non-lumen regions of the images), while an active contour model is used in a second phase, to identify the lumen contour. The method is extended to the longitudinal dimension of the sequences and it is validated on a challenging data-set.
	Address	Barcelona
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Simone Balocco;Petia Radeva
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ Alb2013			Serial	2215
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	Author	Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu
	Title	Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes			Type	Journal
	Year	2013	Publication	Komputer Sapiens	Abbreviated Journal	KS
	Volume	1	Issue		Pages	20-25
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ TSR2013			Serial	2231
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	Author	Patricia Marquez; Debora Gil; Aura Hernandez-Sabate; Daniel Kondermann
	Title	When Is A Confidence Measure Good Enough?			Type	Conference Article
	Year	2013	Publication	9th International Conference on Computer Vision Systems	Abbreviated Journal
	Volume	7963	Issue		Pages	344-353
	Keywords	Optical flow, confidence measure, performance evaluation
	Abstract	Confidence estimation has recently become a hot topic in image processing and computer vision.Yet, several definitions exist of the term “confidence” which are sometimes used interchangeably. This is a position paper, in which we aim to give an overview on existing definitions, thereby clarifying the meaning of the used terms to facilitate further research in this field. Based on these clarifications, we develop a theory to compare confidence measures with respect to their quality.
	Address	St Petersburg; Russia; July 2013
	Corporate Author				Thesis
	Publisher	Springer Link	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	978-3-642-39401-0	Medium
	Area		Expedition		Conference	ICVS
	Notes	IAM;ADAS; 600.044; 600.057; 600.060; 601.145			Approved	no
	Call Number	IAM @ iam @ MGH2013a			Serial	2218
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	Author	David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa
	Title	Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes			Type	Conference Article
	Year	2013	Publication	CVPR Workshop on Ground Truth – What is a good dataset?	Abbreviated Journal
	Volume		Issue		Pages	706 - 711
	Keywords	Pedestrian Detection; Domain Adaptation
	Abstract	Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs.
	Address	Portland; Oregon; June 2013
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	ADAS; 600.054; 600.057; 601.217			Approved	no
	Call Number	ADAS @ adas @ VXR2013a			Serial	2219
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	Author	Jiaolong Xu; David Vazquez; Sebastian Ramos; Antonio Lopez; Daniel Ponsa
	Title	Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers			Type	Conference Article
	Year	2013	Publication	CVPR Workshop on Ground Truth – What is a good dataset?	Abbreviated Journal
	Volume		Issue		Pages	688 - 693
	Keywords	Pedestrian Detection; Domain Adaptation
	Abstract	Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%.
	Address	Portland; oregon; June 2013
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	ADAS; 600.054; 600.057; 601.217			Approved	yes
	Call Number	XVR2013; ADAS @ adas @ xvr2013a			Serial	2220
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	Author	Ivet Rafegas
	Title	Exploring Low-Level Vision Models. Case Study: Saliency Prediction			Type	Report
	Year	2013	Publication	CVC Technical Report	Abbreviated Journal
	Volume	175	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	CIC			Approved	no
	Call Number	Admin @ si @ Raf2013			Serial	2409
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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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