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| Author | Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers | ||||
| Title | Adapting Pedestrian Detection from Synthetic to Far Infrared Images | Type | Conference Article | ||
| Year | 2013 | Publication | ICCV Workshop on Visual Domain Adaptation and Dataset Bias | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Domain Adaptation; Far Infrared; Pedestrian Detection | ||||
| Abstract | We present different techniques to adapt a pedestrian classifier trained with synthetic images and the corresponding automatically generated annotations to operate with far infrared (FIR) images. The information contained in this kind of images allow us to develop a robust pedestrian detector invariant to extreme illumination changes. | ||||
| Address | Sydney; Australia; December 2013 | ||||
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| Publisher | Place of Publication | Sydney, Australy | Editor | ||
| Language | English | Summary Language | Original Title  |
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| Area | Expedition | Conference | ICCVW-VisDA | ||
| Notes | ADAS; 600.054; 600.055; 600.057; 601.217;ISE | Approved | no | ||
| Call Number | ADAS @ adas @ SRV2013 | Serial | 2334 | ||
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| Author | V.C.Kieu; Alicia Fornes; M. Visani; N.Journet ; Anjan Dutta | ||||
| Title | The ICDAR/GREC 2013 Music Scores Competition on Staff Removal | Type | Conference Article | ||
| Year | 2013 | Publication | 10th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Competition; Music scores; Staff Removal | ||||
| Abstract | The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we propose a staff removal competition where we simulate old music scores. Thus, we have created a new set of images, which contain noise and 3D distortions. This paper describes the distortion methods, metrics, the participant’s methods and the obtained results. | ||||
| Address | Bethlehem; PA; USA; August 2013 | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.045; 600.061 | Approved | no | ||
| Call Number | Admin @ si @ KFV2013 | Serial | 2337 | ||
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| Author | M. Visani; V.C.Kieu; Alicia Fornes; N.Journet | ||||
| Title | The ICDAR 2013 Music Scores Competition: Staff Removal | Type | Conference Article | ||
| Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1439-1443 | ||
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| Abstract | The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results. | ||||
| Address | Washington; USA; August 2013 | ||||
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| ISSN | 1520-5363 | ISBN | Medium | ||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.045; 600.061 | Approved | no | ||
| Call Number | Admin @ si @ VKF2013 | Serial | 2338 | ||
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| Author | Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo | ||||
| Title | Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes | Type | Journal Article | ||
| Year | 2011 | Publication | Computer Graphics Forum | Abbreviated Journal | CGF |
| Volume | 30 | Issue | 7 | Pages | 2107-2115 |
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| Abstract | IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy. |
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ EPA2011 | Serial | 1881 | ||
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| Author | Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Xavier Jimenez ; Oscar Vilarroya; Petia Radeva | ||||
| Title | A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder | Type | Journal Article | ||
| Year | 2011 | Publication | BioMedical Engineering Online | Abbreviated Journal | BEO |
| Volume | 10 | Issue | 105 | Pages | 1-23 |
| Keywords | Brain caudate nucleus; segmentation; MRI; atlas-based strategy; Graph Cut framework | ||||
| Abstract | Background
Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD. |
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| ISSN | 1475-925X | ISBN | Medium | ||
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| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ ISH2011 | Serial | 1882 | ||
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| Author | Mario Rojas; David Masip; A. Todorov; Jordi Vitria | ||||
| Title | Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models | Type | Journal Article | ||
| Year | 2011 | Publication | PloS one | Abbreviated Journal | Plos |
| Volume | 6 | Issue | 8 | Pages | e23323 |
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| Abstract | JCR Impact Factor 2010: 4.411
Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions |
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| Publisher | Public Library of Science | Place of Publication | Editor | ||
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| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ RMT2011 | Serial | 1883 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| Title | A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 45 | Issue | 6 | Pages | 2432-2444 |
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| Abstract | IF= 2.61
IF=2.61 (2010) In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other. Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | OR; MV | Approved | no | ||
| Call Number | Admin @ si @ RaD2012a | Serial | 1884 | ||
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| Author | Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva; Bogdan Raducanu | ||||
| Title | Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction | Type | Journal Article | ||
| Year | 2012 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 12 | Issue | 2 | Pages | 1702-1719 |
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| Abstract | IF=1.77 (2010)
Social interactions are a very important component in peopleís lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Timesí Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The linksí weights are a measure of the ìinfluenceî a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network. |
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| Publisher | Molecular Diversity Preservation International | Place of Publication | Editor | ||
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| Notes | MILAB; OR;HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ EBV2012 | Serial | 1885 | ||
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| Author | Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva | ||||
| Title | On the Design of Low Redundancy Error-Correcting Output Codes | Type | Book Chapter | ||
| Year | 2011 | Publication | Ensembles in Machine Learning Applications | Abbreviated Journal | |
| Volume | 373 | Issue | 2 | Pages | 21-38 |
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| Abstract | The classification of large number of object categories is a challenging trend in the Pattern Recognition field. In the literature, this is often addressed using an ensemble of classifiers . In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for combining classifiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a compact design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best compact ECOC code configuration. The results over several public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| ISSN | 1860-949X | ISBN | 978-3-642-22909-1 | Medium | |
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| Notes | MILAB; OR;HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ BEB2011b | Serial | 1886 | ||
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| Author | Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera | ||||
| Title | Introducing the Separability Matrix for Error Correcting Output Codes Coding | Type | Conference Article | ||
| Year | 2011 | Publication | 10th International Conference on Multiple Classifier Systems | Abbreviated Journal | |
| Volume | 6713 | Issue | Pages | 227-236 | |
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| Abstract | Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results. | ||||
| Address | Napoles, Italy | ||||
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| Publisher | Springer-Verlag Berlin, Heidelberg | Place of Publication | Editor | Carlo Sansone; Josef Kittler; Fabio Roli | |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | 0302-9743 | ISBN | 978-3-642-21556-8 | Medium | |
| Area | Expedition | Conference | MCS | ||
| Notes | MILAB; OR;HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ BPB2011b | Serial | 1887 | ||
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| Author | Ruth Aylett; Ginevra Castellano; Bogdan Raducanu; Ana Paiva; Marc Hanheide | ||||
| Title | Long-term socially perceptive and interactive robot companions: challenges and future perspectives | Type | Conference Article | ||
| Year | 2011 | Publication | 13th International Conference on Multimodal Interaction | Abbreviated Journal | |
| Volume | Issue | Pages | 323-326 | ||
| Keywords | human-robot interaction, multimodal interaction, social robotics | ||||
| Abstract | This paper gives a brief overview of the challenges for multi-model perception and generation applied to robot companions located in human social environments. It reviews the current position in both perception and generation and the immediate technical challenges and goes on to consider the extra issues raised by embodiment and social context. Finally, it briefly discusses the impact of systems that must function continually over months rather than just for a few hours. | ||||
| Address | Alicante | ||||
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| Publisher | ACM | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-1-4503-0641-6 | Medium | ||
| Area | Expedition | Conference | ICMI | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ ACR2011 | Serial | 1888 | ||
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| Author | Fadi Dornaika; Alireza Bosaghzadeh; Bogdan Raducanu | ||||
| Title | LSDA Solution Schemes for Modelless 3D Head Pose Estimation | Type | Conference Article | ||
| Year | 2012 | Publication | IEEE Workshop on the Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 393-398 | ||
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| Address | Breckenridge; USA; | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ DBR2012 | Serial | 1889 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| Title | Appearance-based Face Recognition Using A Supervised Manifold Learning Framework | Type | Conference Article | ||
| Year | 2012 | Publication | IEEE Workshop on the Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 465-470 | ||
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| Abstract | Many natural image sets, depicting objects whose appearance is changing due to motion, pose or light variations, can be considered samples of a low-dimension nonlinear manifold embedded in the high-dimensional observation space (the space of all possible images). The main contribution of our work is represented by a Supervised Laplacian Eigemaps (S-LE) algorithm, which exploits the class label information for mapping the original data in the embedded space. Our proposed approach benefits from two important properties: i) it is discriminative, and ii) it adaptively selects the neighbors of a sample without using any predefined neighborhood size. Experiments were conducted on four face databases and the results demonstrate that the proposed algorithm significantly outperforms many linear and non-linear embedding techniques. Although we've focused on the face recognition problem, the proposed approach could also be extended to other category of objects characterized by large variance in their appearance. | ||||
| Address | Breckenridge; CO; USA | ||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | ||
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| ISSN | 1550-5790 | ISBN | 978-1-4673-0233-3 | Medium | |
| Area | Expedition | Conference | WACV | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ RaD2012d | Serial | 1890 | ||
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| Author | Antonio Hernandez; Carlos Primo; Sergio Escalera | ||||
| Title | Automatic user interaction correction via Multi-label Graph cuts | Type | Conference Article | ||
| Year | 2011 | Publication | In ICCV 2011 1st IEEE International Workshop on Human Interaction in Computer Vision HICV | Abbreviated Journal | |
| Volume | Issue | Pages | 1276-1281 | ||
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| Abstract | Most applications in image segmentation requires from user interaction in order to achieve accurate results. However, user wants to achieve the desired segmentation accuracy reducing effort of manual labelling. In this work, we extend standard multi-label α-expansion Graph Cut algorithm so that it analyzes the interaction of the user in order to modify the object model and improve final segmentation of objects. The approach is inspired in the fact that fast user interactions may introduce some pixel errors confusing object and background. Our results with different degrees of user interaction and input errors show high performance of the proposed approach on a multi-label human limb segmentation problem compared with classical α-expansion algorithm. | ||||
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| ISSN | ISBN | 978-1-4673-0062-9 | Medium | ||
| Area | Expedition | Conference | HICV | ||
| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ HPE2011 | Serial | 1892 | ||
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| Author | Miguel Reyes; Gabriel Dominguez; Sergio Escalera | ||||
| Title | Feature Weighting in Dynamic Time Warping for Gesture Recognition in Depth Data | Type | Conference Article | ||
| Year | 2011 | Publication | 1st IEEE Workshop on Consumer Depth Cameras for Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1182-1188 | ||
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| Abstract | We present a gesture recognition approach for depth video data based on a novel Feature Weighting approach within the Dynamic Time Warping framework. Depth features from human joints are compared through video sequences using Dynamic Time Warping, and weights are assigned to features based on inter-intra class gesture variability. Feature Weighting in Dynamic Time Warping is then applied for recognizing begin-end of gestures in data sequences. The obtained results recognizing several gestures in depth data show high performance compared with classical Dynamic Time Warping approach. | ||||
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| ISSN | ISBN | 978-1-4673-0062-9 | Medium | ||
| Area | Expedition | Conference | CDC4CV | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ RDE2011 | Serial | 1893 | ||
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