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| Author | Simeon Petkov; Adriana Romero; Xavier Carrillo; Petia Radeva; Carlo Gatta | ||||
| Title | Robust and accurate diaphragm border detection in cardiac X-Ray angiographies | Type | Conference Article | ||
| Year | 2012 | Publication | Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges | Abbreviated Journal | |
| Volume | 7746 | Issue | Pages | 225-234 | |
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| Abstract | Workshop STACOM, dins del MICCAI
X-ray angiography is the most common imaging modality employed in the diagnosis of coronary diseases prior to or during a catheter-based intervention. The analysis of the patient X-Ray sequence can provide useful information about the degree of arterial stenosis, the myocardial perfusion and other clinical parameters. If the sequence has been acquired to evaluate the perfusion grade, the opacity due to the diaphragm could potentially hinder any kind of visual inspection and make more difficult a computer aided measurements. In this paper we propose an accurate and robust method to automatically identify the diaphragm border in each frame. Quantitative evaluation on a set of 11 sequences shows that the proposed algorithm outperforms previous methods. |
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| Address | Nice, France | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-36960-5 | Medium | |
| Area | Expedition | Conference | STACOM | ||
| Notes | MILAB | Approved | no | ||
Call Number  |
Admin @ si @ PRC2012 | Serial | 2028 | ||
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| Author | C. Alejandro Parraga; Jordi Roca; Dimosthenis Karatzas; Sophie Wuerger | ||||
| Title | Limitations of visual gamma corrections in LCD displays | Type | Journal Article | ||
| Year | 2014 | Publication | Displays | Abbreviated Journal | Dis |
| Volume | 35 | Issue | 5 | Pages | 227–239 |
| Keywords | Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration | ||||
| Abstract | A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements. | ||||
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| Notes | CIC; DAG; 600.052; 600.077; 600.074 | Approved | no | ||
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Admin @ si @ PRK2014 | Serial | 2511 | ||
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| Author | M. Pros | ||||
| Title | Indexacio icònica amb 2D-String per al reconoixement de persones segons la seva vestimenta | Type | Report | ||
| Year | 2000 | Publication | CVC Technical Report #44 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
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Admin @ si @ Pro2000 | Serial | 345 | ||
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| Author | Giuseppe Pezzano; Vicent Ribas Ripoll; Petia Radeva | ||||
| Title | CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation | Type | Journal Article | ||
| Year | 2021 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
| Volume | 198 | Issue | Pages | 105792 | |
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| Abstract | Background and objective:An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tedious and time-consuming procedure and this represents a high obstacle in clinical practice. In this paper, we propose a novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy. Methods:In contrast to most of the standard end-to-end architectures for nodule segmentation, our network learns the context of the nodules by producing two masks representing all the background and secondary-important elements in the Computed Tomography scan. The nodule is detected by subtracting the context from the original scan image. Additionally, we introduce an asymmetric loss function that automatically compensates for potential errors in the nodule annotations. We trained and tested our Neural Network on the public LIDC-IDRI database, compared it with the state of the art and run a pseudo-Turing test between four radiologists and the network. Results:The results proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists. Our method clearly outperforms the state of the art on CT nodule segmentation in terms of F1 score and IoU of and respectively. Conclusions: The main structure of the network ensures all the properties of the UNet architecture, while the Multi Convolutional Layers give a more accurate pattern recognition. The newly adopted solutions also increase the details on the border of the nodule, even under the noisiest conditions. This method can be applied now for single CT slice nodule segmentation and it represents a starting point for the future development of a fully automatic 3D segmentation software. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number |
Admin @ si @ PRR2021 | Serial | 3530 | ||
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| Author | C. Alejandro Parraga; Jordi Roca; Maria Vanrell | ||||
| Title | Do Basic Colors Influence Chromatic Adaptation? | Type | Journal Article | ||
| Year | 2011 | Publication | Journal of Vision | Abbreviated Journal | VSS |
| Volume | 11 | Issue | 11 | Pages | 85 |
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| Abstract | Color constancy (the ability to perceive colors relatively stable under different illuminants) is the result of several mechanisms spread across different neural levels and responding to several visual scene cues. It is usually measured by estimating the perceived color of a grey patch under an illuminant change. In this work, we hypothesize whether chromatic adaptation (without a reference white or grey) could be driven by certain colors, specifically those corresponding to the universal color terms proposed by Berlin and Kay (1969). To this end we have developed a new psychophysical paradigm in which subjects adjust the color of a test patch (in CIELab space) to match their memory of the best example of a given color chosen from the universal terms list (grey, red, green, blue, yellow, purple, pink, orange and brown). The test patch is embedded inside a Mondrian image and presented on a calibrated CRT screen inside a dark cabin. All subjects were trained to “recall” their most exemplary colors reliably from memory and asked to always produce the same basic colors when required under several adaptation conditions. These include achromatic and colored Mondrian backgrounds, under a simulated D65 illuminant and several colored illuminants. A set of basic colors were measured for each subject under neutral conditions (achromatic background and D65 illuminant) and used as “reference” for the rest of the experiment. The colors adjusted by the subjects in each adaptation condition were compared to the reference colors under the corresponding illuminant and a “constancy index” was obtained for each of them. Our results show that for some colors the constancy index was better than for grey. The set of best adapted colors in each condition were common to a majority of subjects and were dependent on the chromaticity of the illuminant and the chromatic background considered. | ||||
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| ISSN | 1534-7362 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number |
Admin @ si @ PRV2011 | Serial | 1759 | ||
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| Author | Cristina Palmero; Javier Selva; Mohammad Ali Bagueri; Sergio Escalera | ||||
| Title | Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues | Type | Conference Article | ||
| Year | 2018 | Publication | 29th British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | Gaze behavior is an important non-verbal cue in social signal processing and humancomputer interaction. In this paper, we tackle the problem of person- and head poseindependent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame. Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on
EYEDIAP dataset, further improved by 4% when the temporal modality is included. |
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| Address | Newcastle; UK; September 2018 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number |
Admin @ si @ PSB2018 | Serial | 3208 | ||
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| Author | Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska | ||||
| Title | Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation | Type | Conference Article | ||
| Year | 2018 | Publication | International MICCAI Brainlesion Workshop | Abbreviated Journal | |
| Volume | 11384 | Issue | Pages | 393-405 | |
| Keywords | Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution | ||||
| Abstract | In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge. | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | MICCAIW | ||
| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number |
Admin @ si @ PSH2018 | Serial | 3251 | ||
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| Author | Monica Piñol; Angel Sappa; Angeles Lopez; Ricardo Toledo | ||||
| Title | Feature Selection Based on Reinforcement Learning for Object Recognition | Type | Conference Article | ||
| Year | 2012 | Publication | Adaptive Learning Agents Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 33-39 | ||
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| Address | Valencia | ||||
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| Area | Expedition | Conference | ALA | ||
| Notes | ADAS; RV | Approved | no | ||
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Admin @ si @ PSL2012 | Serial | 2018 | ||
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| Author | Cristina Palmero; Javier Selva; Sorina Smeureanu; Julio C. S. Jacques Junior; Albert Clapes; Alexa Mosegui; Zejian Zhang; David Gallardo; Georgina Guilera; David Leiva; Sergio Escalera | ||||
| Title | Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1-12 | ||
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| Abstract | This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self- and peer-reported personality, internal state, and relationship profiling from participants. As an initial analysis on UDIVA, we propose a
transformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors to regress a target person’s personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information. |
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| Address | Virtual; January 2021 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | HUPBA | Approved | no | ||
| Call Number |
Admin @ si @ PSS2021 | Serial | 3532 | ||
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| Author | Monica Piñol; Angel Sappa; Ricardo Toledo | ||||
| Title | MultiTable Reinforcement for Visual Object Recognition | Type | Conference Article | ||
| Year | 2012 | Publication | 4th International Conference on Signal and Image Processing | Abbreviated Journal | |
| Volume | 221 | Issue | Pages | 469-480 | |
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| Abstract | This paper presents a bag of feature based method for visual object recognition. Our contribution is focussed on the selection of the best feature descriptor. It is implemented by using a novel multi-table reinforcement learning method that selects among five of classical descriptors (i.e., Spin, SIFT, SURF, C-SIFT and PHOW) the one that best describes each image. Experimental results and comparisons are provided showing the improvements achieved with the proposed approach. | ||||
| Address | Coimbatore, India | ||||
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| Publisher | Springer India | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1876-1100 | ISBN | 978-81-322-0996-6 | Medium | |
| Area | Expedition | Conference | ICSIP | ||
| Notes | ADAS | Approved | no | ||
| Call Number |
Admin @ si @ PST2012 | Serial | 2157 | ||
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| Author | Marcelo D. Pistarelli; Angel Sappa; Ricardo Toledo | ||||
| Title | Multispectral Stereo Image Correspondence | Type | Conference Article | ||
| Year | 2013 | Publication | 15th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 8048 | Issue | Pages | 217-224 | |
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| Abstract | This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach. | ||||
| Address | York; uk; August 2013 | ||||
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| Publisher | Springer Berlin Heidelberg | 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 | 978-3-642-40245-6 | Medium | |
| Area | Expedition | Conference | CAIP | ||
| Notes | ADAS; 600.055 | Approved | no | ||
| Call Number |
Admin @ si @ PST2013 | Serial | 2561 | ||
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| Author | Monica Piñol; Angel Sappa; Ricardo Toledo | ||||
| Title | Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy | Type | Journal Article | ||
| Year | 2015 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | 150 | Issue | A | Pages | 106–115 |
| Keywords | Reinforcement learning; Q-learning; Bag of features; Descriptors | ||||
| Abstract | This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach. | ||||
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| Notes | ADAS; 600.055; 600.076 | Approved | no | ||
| Call Number |
Admin @ si @ PST2015 | Serial | 2473 | ||
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| Author | Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo | ||||
| Title | Subspace Procrustes Analysis | Type | Conference Article | ||
| Year | 2014 | Publication | ECCV Workshop on ChaLearn Looking at People | Abbreviated Journal | |
| Volume | 8925 | Issue | Pages | 654-668 | |
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| Abstract | Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling di erent views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more ecient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the bene ts of our approach. | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | OR; HuPBA;MILAB | Approved | no | ||
| Call Number |
Admin @ si @ PTI2014 | Serial | 2539 | ||
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| Author | Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo | ||||
| Title | Subspace Procrustes Analysis | Type | Journal Article | ||
| Year | 2017 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 121 | Issue | 3 | Pages | 327–343 |
| Keywords | |||||
| Abstract | Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several
instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benefits of our approach. |
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| Notes | MILAB; HuPBA; no proj | Approved | no | ||
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Admin @ si @ PTI2017 | Serial | 2841 | ||
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| Author | D. Perez; L. Tarazon; N. Serrano; F.M. Castro; Oriol Ramos Terrades; A. Juan | ||||
| Title | The GERMANA Database | Type | Conference Article | ||
| Year | 2009 | Publication | 10th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 301-305 | ||
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| Abstract | A new handwritten text database, GERMANA, is presented to facilitate empirical comparison of different approaches to text line extraction and off-line handwriting recognition. GERMANA is the result of digitising and annotating a 764-page Spanish manuscript from 1891, in which most pages only contain nearly calligraphed text written on ruled sheets of well-separated lines. To our knowledge, it is the first publicly available database for handwriting research, mostly written in Spanish and comparable in size to standard databases. Due to its sequential book structure, it is also well-suited for realistic assessment of interactive handwriting recognition systems. To provide baseline results for reference in future studies, empirical results are also reported, using standard techniques and tools for preprocessing, feature extraction, HMM-based image modelling, and language modelling. | ||||
| Address | Barcelona; Spain | ||||
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| ISSN | 1520-5363 | ISBN | 978-1-4244-4500-4 | Medium | |
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number |
Admin @ si @ PTS2009 | Serial | 1870 | ||
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