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Author Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta
Title Diaphragm border detection in coronary X-ray angiographies: New method and applications Type Journal Article
Year 2014 Publication Computerized Medical Imaging and Graphics Abbreviated Journal CMIG
Volume 38 Issue 4 Pages 296-305
Keywords
Abstract (down) X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation.
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Notes MILAB; LAMP; 600.079 Approved no
Call Number Admin @ si @ PCR2014 Serial 2468
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Author Jon Almazan
Title Learning to Represent Handwritten Shapes and Words for Matching and Recognition Type Book Whole
Year 2014 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract (down) Writing is one of the most important forms of communication and for centuries, handwriting had been the most reliable way to preserve knowledge. However, despite the recent development of printing houses and electronic devices, handwriting is still broadly used for taking notes, doing annotations, or sketching ideas.
Transferring the ability of understanding handwritten text or recognizing handwritten shapes to computers has been the goal of many researches due to its huge importance for many different fields. However, designing good representations to deal with handwritten shapes, e.g. symbols or words, is a very challenging problem due to the large variability of these kinds of shapes. One of the consequences of working with handwritten shapes is that we need representations to be robust, i.e., able to adapt to large intra-class variability. We need representations to be discriminative, i.e., able to learn what are the differences between classes. And, we need representations to be efficient, i.e., able to be rapidly computed and compared. Unfortunately, current techniques of handwritten shape representation for matching and recognition do not fulfill some or all of these requirements.
Through this thesis we focus on the problem of learning to represent handwritten shapes aimed at retrieval and recognition tasks. Concretely, on the first part of the thesis, we focus on the general problem of representing any kind of handwritten shape. We first present a novel shape descriptor based on a deformable grid that deals with large deformations by adapting to the shape and where the cells of the grid can be used to extract different features. Then, we propose to use this descriptor to learn statistical models, based on the Active Appearance Model, that jointly learns the variability in structure and texture of a given class. Then, on the second part, we focus on a concrete application, the problem of representing handwritten words, for the tasks of word spotting, where the goal is to find all instances of a query word in a dataset of images, and recognition. First, we address the segmentation-free problem and propose an unsupervised, sliding-window-based approach that achieves state-of- the-art results in two public datasets. Second, we address the more challenging multi-writer problem, where the variability in words exponentially increases. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace, and where those that represent the same word are close together. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. This leads to a low-dimensional, unified representation of word images and strings, resulting in a method that allows one to perform either image and text searches, as well as image transcription, in a unified framework. We evaluate our methods on different public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Ernest Valveny;Alicia Fornes
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ Alm2014 Serial 2572
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Author L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink
Title A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting Type Journal
Year 2014 Publication Manuscript Cultures Abbreviated Journal
Volume 7 Issue Pages 47-58
Keywords
Abstract (down) With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington
benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline.
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Publisher Place of Publication Editor
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Notes DAG; 600.061; 600.077 Approved no
Call Number Admin @ si @ Serial 3190
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Author Michal Drozdzal
Title Sequential image analysis for computer-aided wireless endoscopy Type Book Whole
Year 2014 Publication PhD Thesis, Universitat de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract (down) Wireless Capsule Endoscopy (WCE) is a technique for inner-visualization of the entire small intestine and, thus, offers an interesting perspective on intestinal motility. The two major drawbacks of this technique are: 1) huge amount of data acquired by WCE makes the motility analysis tedious and 2) since the capsule is the first tool that offers complete inner-visualization of the small intestine,the exact importance of the observed events is still an open issue. Therefore, in this thesis, a novel computer-aided system for intestinal motility analysis is presented. The goal of the system is to provide an easily-comprehensible visual description of motility-related intestinal events to a physician. In order to do so, several tools based either on computer vision concepts or on machine learning techniques are presented. A method for transforming 3D video signal to a holistic image of intestinal motility, called motility bar, is proposed. The method calculates the optimal mapping from video into image from the intestinal motility point of view.
To characterize intestinal motility, methods for automatic extraction of motility information from WCE are presented. Two of them are based on the motility bar and two of them are based on frame-per-frame analysis. In particular, four algorithms dealing with the problems of intestinal contraction detection, lumen size estimation, intestinal content characterization and wrinkle frame detection are proposed and validated. The results of the algorithms are converted into sequential features using an online statistical test. This test is designed to work with multivariate data streams. To this end, we propose a novel formulation of concentration inequality that is introduced into a robust adaptive windowing algorithm for multivariate data streams. The algorithm is used to obtain robust representation of segments with constant intestinal motility activity. The obtained sequential features are shown to be discriminative in the problem of abnormal motility characterization.
Finally, we tackle the problem of efficient labeling. To this end, we incorporate active learning concepts to the problems present in WCE data and propose two approaches. The first one is based the concepts of sequential learning and the second one adapts the partition-based active learning to an error-free labeling scheme. All these steps are sufficient to provide an extensive visual description of intestinal motility that can be used by an expert as decision support system.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Petia Radeva
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-940902-3-3 Medium
Area Expedition Conference
Notes MILAB Approved no
Call Number Admin @ si @ Dro2014 Serial 2486
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Author Jorge Bernal; Fernando Vilariño; F. Javier Sanchez; M. Arnold; Anarta Ghosh; Gerard Lacey
Title Experts vs Novices: Applying Eye-tracking Methodologies in Colonoscopy Video Screening for Polyp Search Type Conference Article
Year 2014 Publication 2014 Symposium on Eye Tracking Research and Applications Abbreviated Journal
Volume Issue Pages 223-226
Keywords
Abstract (down) We present in this paper a novel study aiming at identifying the differences in visual search patterns between physicians of diverse levels of expertise during the screening of colonoscopy videos. Physicians were clustered into two groups -experts and novices- according to the number of procedures performed, and fixations were captured by an eye-tracker device during the task of polyp search in different video sequences. These fixations were integrated into heat maps, one for each cluster. The obtained maps were validated over a ground truth consisting of a mask of the polyp, and the comparison between experts and novices was performed by using metrics such as reaction time, dwelling time and energy concentration ratio. Experimental results show a statistically significant difference between experts and novices, and the obtained maps show to be a useful tool for the characterisation of the behaviour of each group.
Address USA; 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 978-1-4503-2751-0 Medium
Area Expedition Conference ETRA
Notes MV; 600.047; 600.060;SIAI Approved no
Call Number Admin @ si @ BVS2014 Serial 2448
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Author Francesco Ciompi; Oriol Pujol; Petia Radeva
Title ECOC-DRF: Discriminative random fields based on error correcting output codes Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 6 Pages 2193-2204
Keywords Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models
Abstract (down) We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.
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Notes LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 Approved no
Call Number Admin @ si @ CPR2014b Serial 2470
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Author Lluis Gomez; Dimosthenis Karatzas
Title MSER-based Real-Time Text Detection and Tracking Type Conference Article
Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 3110 - 3115
Keywords
Abstract (down) We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization.
A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices.
Address Stockholm; August 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 1051-4651 ISBN Medium
Area Expedition Conference ICPR
Notes DAG; 600.056; 601.158; 601.197; 600.077 Approved no
Call Number Admin @ si @ GoK2014a Serial 2492
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Author David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson
Title Automated Prediction of Preferences Using Facial Expressions Type Journal Article
Year 2014 Publication PloS one Abbreviated Journal Plos
Volume 9 Issue 2 Pages e87434
Keywords
Abstract (down) We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available.
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Notes OR;MV Approved no
Call Number Admin @ si @ MNT2014 Serial 2453
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Author Joan M. Nuñez; Jorge Bernal; Miquel Ferrer; Fernando Vilariño
Title Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos Type Conference Article
Year 2014 Publication CARE workshop Abbreviated Journal
Volume Issue Pages
Keywords Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection
Abstract (down) We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels.
Address Boston; USA; September 2014
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CARE
Notes MV; DAG; 600.060; 600.047; 600.077;SIAI Approved no
Call Number Admin @ si @ NBF2014 Serial 2504
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Author Onur Ferhat; Fernando Vilariño; F. Javier Sanchez
Title A cheap portable eye-tracker solution for common setups. Type Journal Article
Year 2014 Publication Journal of Eye Movement Research Abbreviated Journal JEMR
Volume 7 Issue 3 Pages 1-10
Keywords
Abstract (down) We analyze the feasibility of a cheap eye-tracker where the hardware consists of a single webcam and a Raspberry Pi device. Our aim is to discover the limits of such a system and to see whether it provides an acceptable performance. We base our work on the open source Opengazer (Zielinski, 2013) and we propose several improvements to create a robust, real-time system which can work on a computer with 30Hz sampling rate. After assessing the accuracy of our eye-tracker in elaborated experiments involving 12 subjects under 4 different system setups, we install it on a Raspberry Pi to create a portable stand-alone eye-tracker which achieves 1.42° horizontal accuracy with 3Hz refresh rate for a building cost of 70 Euros.
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
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Area Expedition Conference
Notes ;SIAI Approved no
Call Number Admin @ si @ FVS2014 Serial 2435
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Author Carlo Gatta; Francesco Ciompi
Title Stacked Sequential Scale-Space Taylor Context Type Journal Article
Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 36 Issue 8 Pages 1694-1700
Keywords
Abstract (down) We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets.
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 0162-8828 ISBN Medium
Area Expedition Conference
Notes LAMP; MILAB; 601.160; 600.079 Approved no
Call Number Admin @ si @ GaC2014 Serial 2466
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Author Adria Ruiz; Joost Van de Weijer; Xavier Binefa
Title Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization Type Conference Article
Year 2014 Publication 25th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages
Keywords
Abstract (down) We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection.
Address Nottingham; UK; 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 BMVC
Notes LAMP; CIC; 600.074; 600.079 Approved no
Call Number Admin @ si @ RWB2014 Serial 2508
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Author M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer
Title Adaptive color attributes for real-time visual tracking Type Conference Article
Year 2014 Publication 27th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 1090 - 1097
Keywords
Abstract (down) Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally
efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.
This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional
variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms
state-of-the-art tracking methods while running at more than 100 frames per second.
Address Nottingham; UK; 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 CVPR
Notes CIC; LAMP; 600.074; 600.079 Approved no
Call Number Admin @ si @ DKF2014 Serial 2509
Permanent link to this record
 
 
Author Antonio Esteban Lansaque
Title 3D reconstruction and recognition using structured ligth Type Report
Year 2014 Publication CVC Technical Report Abbreviated Journal
Volume 179 Issue Pages
Keywords
Abstract (down) This work covers the problem of 3D reconstruction, recognition and 6DOF pose estimation. The goal of this project is to reconstruct a 3D scene and to align an object model of the industrial pieces onto the reconstructed scene. The reconstruction algorithm is based on stereo techniques and the recognition algorithm is based on SHOT descriptors computed on a set of uniform keypoints. Correspondences are used to estimate a first 6DOF transformation that maps the model onto the scene and then ICP algorithm is used to refine the transformation. In order to check the effectiveness of the proposed algorithm, several experiments were performed. These experiments were conducted on a lab environment in order to get results under the same conditions in all of them. Although obtained results are not real time results, the proposed algorithm ends up with high rates of object recognition.
Address UAB; September 2014
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; 600.075 Approved no
Call Number Admin @ si @ Est2014 Serial 2578
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Author Cristhian A. Aguilera-Carrasco
Title Evaluation of feature detectors and descriptors in VISIBLE-LWIR cross-spectral imaging Type Report
Year 2014 Publication CVC Technical Report Abbreviated Journal
Volume 177 Issue Pages
Keywords Multi-spectral; Cross-spectral; Visible-LWIR imaging; Multimodal.
Abstract (down) This thesis evaluates the performance of different state-of-art feature detectors and descriptors algorithms in the Visible-LWIR cross-spectral scenario. The focus is to determine if current detector and descriptor algorithms can be used to match features between the LWIR spectrum and the visible spectrum in applications such as, visual odometry, object recognition, image registration and stereo vision. An outdoor cross-spectral dataset was created to evaluate the suitability of the different algorithms. The results
show that the tested algorithms are not suitable to the task of matching features across different spectra. The repeatability ratio was smaller than the 30 percent in the best case and in general matched features were not accurate located. Additionally, these results also suggest that is necessary to create new algorithms that take into account the nature of the different spectra, describing characteristics that exist in both spectra such as discontinuities.
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 ADAS; 600.076 Approved no
Call Number Admin @ si @Agu2014 Serial 2526
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