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	Author	Jorge Bernal; Joan M. Nuñez; F. Javier Sanchez; Fernando Vilariño
	Title	Polyp Segmentation Method in Colonoscopy Videos by means of MSA-DOVA Energy Maps Calculation			Type	Conference Article
	Year	2014	Publication	3rd MICCAI Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging	Abbreviated Journal
	Volume	8680	Issue		Pages	41-49
	Keywords	Image segmentation; Polyps; Colonoscopy; Valley information; Energy maps
	Abstract	In this paper we present a novel polyp region segmentation method for colonoscopy videos. Our method uses valley information associated to polyp boundaries in order to provide an initial segmentation. This first segmentation is refined to eliminate boundary discontinuities caused by image artifacts or other elements of the scene. Experimental results over a publicly annotated database show that our method outperforms both general and specific segmentation methods by providing more accurate regions rich in polyp content. We also prove how image preprocessing is needed to improve final polyp region segmentation.
	Address	Boston; USA; September 2014
	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	CLIP
	Notes	MV; 600.060; 600.044; 600.047;SIAI			Approved	no
	Call Number	Admin @ si @ BNS2014			Serial	2502
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	Author	Jorge Bernal
	Title	Polyp Localization and Segmentation in Colonoscopy Images by Means of a Model of Appearance for Polyps			Type	Journal Article
	Year	2014	Publication	Electronic Letters on Computer Vision and Image Analysis	Abbreviated Journal	ELCVIA
	Volume	13	Issue	2	Pages	9-10
	Keywords	Colonoscopy; polyp localization; polyp segmentation; Eye-tracking
	Abstract	Colorectal cancer is the fourth most common cause of cancer death worldwide and its survival rate depends on the stage in which it is detected on hence the necessity for an early colon screening. There are several screening techniques but colonoscopy is still nowadays the gold standard, although it has some drawbacks such as the miss rate. Our contribution, in the field of intelligent systems for colonoscopy, aims at providing a polyp localization and a polyp segmentation system based on a model of appearance for polyps. To develop both methods we define a model of appearance for polyps, which describes a polyp as enclosed by intensity valleys. The novelty of our contribution resides on the fact that we include in our model aspects of the image formation and we also consider the presence of other elements from the endoluminal scene such as specular highlights and blood vessels, which have an impact on the performance of our methods. In order to develop our polyp localization method we accumulate valley information in order to generate energy maps, which are also used to guide the polyp segmentation. Our methods achieve promising results in polyp localization and segmentation. As we want to explore the usability of our methods we present a comparative analysis between physicians fixations obtained via an eye tracking device and our polyp localization method. The results show that our method is indistinguishable to novice physicians although it is far from expert physicians.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	Alicia Fornes; Volkmar Frinken
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MV			Approved	no
	Call Number	Admin @ si @ Ber2014			Serial	2487
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	Author	P. Ricaurte; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa
	Title	Performance Evaluation of Feature Point Descriptors in the Infrared Domain			Type	Conference Article
	Year	2014	Publication	9th International Conference on Computer Vision Theory and Applications	Abbreviated Journal
	Volume	1	Issue		Pages	545-550
	Keywords	Infrared Imaging; Feature Point Descriptors
	Abstract	This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered.
	Address	Lisboa; Portugal; January 2014
	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	VISAPP
	Notes	ADAS; 600.055; 600.076			Approved	no
	Call Number	Admin @ si @ RCA2014b			Serial	2476
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	Author	Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg
	Title	Painting-91: A Large Scale Database for Computational Painting Categorization			Type	Journal Article
	Year	2014	Publication	Machine Vision and Applications	Abbreviated Journal	MVAP
	Volume	25	Issue	6	Pages	1385-1397
	Keywords
	Abstract	Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.
	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	0932-8092	ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC; LAMP; 600.074; 600.079			Approved	no
	Call Number	Admin @ si @ KBW2014			Serial	2510
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	Author	David Fernandez; Pau Riba; Alicia Fornes; Josep Llados
	Title	On the Influence of Key Point Encoding for Handwritten Word Spotting			Type	Conference Article
	Year	2014	Publication	14th International Conference on Frontiers in Handwriting Recognition	Abbreviated Journal
	Volume		Issue		Pages	476 - 481
	Keywords	Local descriptors; Interest points; Handwritten documents; Word spotting; Historical document analysis
	Abstract	In this paper we evaluate the influence of the selection of key points and the associated features in the performance of word spotting processes. In general, features can be extracted from a number of characteristic points like corners, contours, skeletons, maxima, minima, crossings, etc. A number of descriptors exist in the literature using different interest point detectors. But the intrinsic variability of handwriting vary strongly on the performance if the interest points are not stable enough. In this paper, we analyze the performance of different descriptors for local interest points. As benchmarking dataset we have used the Barcelona Marriage Database that contains handwritten records of marriages over five centuries.
	Address	Creete Island; Grecia; September 2014
	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	2167-6445	ISBN	978-1-4799-4335-7	Medium
	Area		Expedition		Conference	ICFHR
	Notes	DAG; 600.056; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ FRF2014			Serial	2460
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	Author	Miguel Angel Bautista; Sergio Escalera; Oriol Pujol
	Title	On the Design of an ECOC-Compliant Genetic Algorithm			Type	Journal Article
	Year	2014	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	47	Issue	2	Pages	865-884
	Keywords
	Abstract	Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches.
	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	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ BEP2013			Serial	2254
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	Author	Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva
	Title	Occlusion handling via random subspace classifiers for human detection			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Systems, Man, and Cybernetics (Part B)	Abbreviated Journal	TSMCB
	Volume	44	Issue	3	Pages	342-354
	Keywords	Pedestriand Detection; occlusion handling
	Abstract	This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes
	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	2168-2267	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076			Approved	no
	Call Number	ADAS @ adas @ MVL2014			Serial	2213
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	Author	Frederic Sampedro; Anna Domenech; Sergio Escalera
	Title	Obtaining quantitative global tumoral state indicators based on whole-body PET/CT scans: A breast cancer case study			Type	Journal Article
	Year	2014	Publication	Nuclear Medicine Communications	Abbreviated Journal	NMC
	Volume	35	Issue	4	Pages	362-371
	Keywords
	Abstract	Objectives: In this work we address the need for the computation of quantitative global tumoral state indicators from oncological whole-body PET/computed tomography scans. The combination of such indicators with other oncological information such as tumor markers or biopsy results would prove useful in oncological decision-making scenarios. Materials and methods: From an ordering of 100 breast cancer patients on the basis of oncological state through visual analysis by a consensus of nuclear medicine specialists, a set of numerical indicators computed from image analysis of the PET/computed tomography scan is presented, which attempts to summarize a patient’s oncological state in a quantitative manner taking into consideration the total tumor volume, aggressiveness, and spread. Results: Results obtained by comparative analysis of the proposed indicators with respect to the experts’ evaluation show up to 87% Pearson’s correlation coefficient when providing expert-guided PET metabolic tumor volume segmentation and 64% correlation when using completely automatic image analysis techniques. Conclusion: Global quantitative tumor information obtained by whole-body PET/CT image analysis can prove useful in clinical nuclear medicine settings and oncological decision-making scenarios. The completely automatic computation of such indicators would improve its impact as time efficiency and specialist independence would be achieved.
	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	HuPBA;MILAB			Approved	no
	Call Number	SDE2014a			Serial	2444
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	Author	Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier
	Title	Normalisation et validation d'images de documents capturées en mobilité			Type	Conference Article
	Year	2014	Publication	Colloque International Francophone sur l'Écrit et le Document	Abbreviated Journal
	Volume		Issue		Pages	109-124
	Keywords	mobile document image acquisition; perspective correction; illumination correction; quality assessment; focus measure; OCR accuracy prediction
	Abstract	Mobile document image acquisition integrates many distortions which must be corrected or detected on the device, before the document becomes unavailable or paying data transmission fees. In this paper, we propose a system to correct perspective and illumination issues, and estimate the sharpness of the image for OCR recognition. The correction step relies on fast and accurate border detection followed by illumination normalization. Its evaluation on a private dataset shows a clear improvement on OCR accuracy. The quality assessment step relies on a combination of focus measures. Its evaluation on a public dataset shows that this simple method compares well to state of the art, learning-based methods which cannot be embedded on a mobile, and outperforms metric-based methods.
	Address	Nancy; France; March 2014
	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; 601.223; 600.077			Approved	no
	Call Number	Admin @ si @ RCO2014b			Serial	2546
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	Author	Mohammad Rouhani; E. Boyer; Angel Sappa
	Title	Non-Rigid Registration meets Surface Reconstruction			Type	Conference Article
	Year	2014	Publication	International Conference on 3D Vision	Abbreviated Journal
	Volume		Issue		Pages	617-624
	Keywords
	Abstract	Non rigid registration is an important task in computer vision with many applications in shape and motion modeling. A fundamental step of the registration is the data association between the source and the target sets. Such association proves difficult in practice, due to the discrete nature of the information and its corruption by various types of noise, e.g. outliers and missing data. In this paper we investigate the benefit of the implicit representations for the non-rigid registration of 3D point clouds. First, the target points are described with small quadratic patches that are blended through partition of unity weighting. Then, the discrete association between the source and the target can be replaced by a continuous distance field induced by the interface. By combining this distance field with a proper deformation term, the registration energy can be expressed in a linear least square form that is easy and fast to solve. This significantly eases the registration by avoiding direct association between points. Moreover, a hierarchical approach can be easily implemented by employing coarse-to-fine representations. Experimental results are provided for point clouds from multi-view data sets. The qualitative and quantitative comparisons show the outperformance and robustness of our framework. %in presence of noise and outliers.
	Address	Tokyo; Japan; December 2014
	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	3DV
	Notes	ADAS; 600.055; 600.076			Approved	no
	Call Number	Admin @ si @ RBS2014			Serial	2534
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	Author	Adriana Romero; Petia Radeva; Carlo Gatta
	Title	No more meta-parameter tuning in unsupervised sparse feature learning			Type	Miscellaneous
	Year	2014	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	CoRR abs/1402.5766 We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well.
	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	MILAB; LAMP; 600.079			Approved	no
	Call Number	Admin @ si @ RRG2014			Serial	2471
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	Author	Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu
	Title	New Opportunities for Computer Vision-Based Assistive Technology Systems for the Visually Impaired			Type	Journal Article
	Year	2014	Publication	Computer	Abbreviated Journal	COMP
	Volume	47	Issue	4	Pages	52-58
	Keywords
	Abstract	Computing advances and increased smartphone use gives technology system designers greater flexibility in exploiting computer vision to support visually impaired users. Understanding these users' needs will certainly provide insight for the development of improved usability of computing devices.
	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	0018-9162	ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP;			Approved	no
	Call Number	Admin @ si @ TSR2014a			Serial	2317
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	Author	Marçal Rusiñol; Volkmar Frinken; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados
	Title	Multimodal page classification in administrative document image streams			Type	Journal Article
	Year	2014	Publication	International Journal on Document Analysis and Recognition	Abbreviated Journal	IJDAR
	Volume	17	Issue	4	Pages	331-341
	Keywords	Digital mail room; Multimodal page classification; Visual and textual document description
	Abstract	In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an n-gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages.
	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	1433-2833	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; LAMP; 600.056; 600.061; 601.240; 601.223; 600.077; 600.079			Approved	no
	Call Number	Admin @ si @ RFK2014			Serial	2523
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	Author	Palaiahnakote Shivakumara; Anjan Dutta; Chew Lim Tan; Umapada Pal
	Title	Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing			Type	Journal Article
	Year	2014	Publication	Multimedia Tools and Applications	Abbreviated Journal	MTAP
	Volume	72	Issue	1	Pages	515-539
	Keywords
	Abstract	In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well.
	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	1380-7501	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @ SDT2014			Serial	2357
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	Author	Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou
	Title	Multi-Illuminant Estimation with Conditional Random Fields			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Image Processing	Abbreviated Journal	TIP
	Volume	23	Issue	1	Pages	83-95
	Keywords	color constancy; CRF; multi-illuminant
	Abstract	Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation 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	CIC; LAMP; 600.074; 600.079			Approved	no
	Call Number	Admin @ si @ BRW2014			Serial	2451
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