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| Author | Jun Wan; Yibing Zhao; Shuai Zhou; Isabelle Guyon; Sergio Escalera | ||||
| Title | ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition | Type | Conference Article | ||
| Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops | Abbreviated Journal | |
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| Abstract | In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD)and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset
(CGD) that has a total of more than 50000 gestures for the “one-shot-learning” competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences.Using these datasets we will open two competitions on the CodaLab platform so that researchers can test and compare their methods for “user independent” gesture recognition. The first challenge is designed for gesture spotting and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. The baseline method based on the bag of visual words model is also presented. |
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| Address | Las Vegas; USA; July 2016 | ||||
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| Area | Expedition | Conference | CVPRW | ||
Notes  |
HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ WZZ2016 | Serial | 2771 | ||
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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
| Title | Support Vector Machines with Time Series Distance Kernels for Action Classification | Type | Conference Article | ||
| Year | 2016 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1-7 | ||
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| Abstract | Despite the outperformance of Support Vector Machine (SVM) on many practical classification problems, the algorithm is not directly applicable to multi-dimensional trajectories having different lengths. In this paper, a new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function.
Dynamic Time Warping and Longest Common Subsequence distance measures along with their derivatives are employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation. The proposed method is employed for a challenging classification problem: action recognition by depth cameras using only skeleton data; and evaluated on three benchmark action datasets. Experimental results demonstrate the outperformance of our methodology compared to the state-ofthe-art on the considered datasets. |
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| Address | Lake Placid; NY (USA); March 2016 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes |
HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ BGE2016a | Serial | 2773 | ||
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| Author | Baiyu Chen; Sergio Escalera; Isabelle Guyon; Victor Ponce; N. Shah; Marc Oliu | ||||
| Title | Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits | Type | Conference Article | ||
| Year | 2016 | Publication | 14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Calibration of labels; Label bias; Ordinal labeling; Variance Models; Bradley-Terry-Luce model; Continuous labels; Regression; Personality traits; Crowd-sourced labels | ||||
| Abstract | We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly dicult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p = N (N-1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is a ordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge. | ||||
| Address | Amsterdam; The Netherlands; October 2016 | ||||
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| Area | Expedition | Conference | ECCVW | ||
| Notes |
HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ CEG2016 | Serial | 2829 | ||
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| Author | Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | Fusion of Classifier Predictions for Audio-Visual Emotion Recognition | Type | Conference Article | ||
| Year | 2016 | Publication | 23rd International Conference on Pattern Recognition Workshops | Abbreviated Journal | |
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| Abstract | In this paper is presented a novel multimodal emotion recognition system which is based on the analysis of audio and visual cues. MFCC-based features are extracted from the audio channel and facial landmark geometric relations are
computed from visual data. Both sets of features are learnt separately using state-of-the-art classifiers. In addition, we summarise each emotion video into a reduced set of key-frames, which are learnt in order to visually discriminate emotions by means of a Convolutional Neural Network. Finally, confidence outputs of all classifiers from all modalities are used to define a new feature space to be learnt for final emotion prediction, in a late fusion/stacking fashion. The conducted experiments on eNTERFACE’05 database show significant performance improvements of our proposed system in comparison to state-of-the-art approaches. |
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| Address | Cancun; Mexico; December 2016 | ||||
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| Area | Expedition | Conference | ICPRW | ||
| Notes |
HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ NMN2016 | Serial | 2839 | ||
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| Author | Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | SASE: RGB-Depth Database for Human Head Pose Estimation | Type | Conference Article | ||
| Year | 2016 | Publication | 14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
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| Abstract | Slides | ||||
| Address | Amsterdam; The Netherlands; October 2016 | ||||
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| Area | Expedition | Conference | ECCVW | ||
| Notes |
HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ LEA2016a | Serial | 2840 | ||
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| Author | Marc Oliu; Ciprian Corneanu; Kamal Nasrollahi; Olegs Nikisins; Sergio Escalera; Yunlian Sun; Haiqing Li; Zhenan Sun; Thomas B. Moeslund; Modris Greitans | ||||
| Title | Improved RGB-D-T based Face Recognition | Type | Journal Article | ||
| Year | 2016 | Publication | IET Biometrics | Abbreviated Journal | BIO |
| Volume | 5 | Issue | 4 | Pages | 297 - 303 |
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| Abstract | Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes. | ||||
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HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ OCN2016 | Serial | 2854 | ||
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| Author | Florin Popescu; Stephane Ayache; Sergio Escalera; Xavier Baro; Cecile Capponi; Patrick Panciatici; Isabelle Guyon | ||||
| Title | From geospatial observations of ocean currents to causal predictors of spatio-economic activity using computer vision and machine learning | Type | Conference Article | ||
| Year | 2016 | Publication | European Geosciences Union General Assembly | Abbreviated Journal | |
| Volume | 18 | Issue | Pages | ||
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| Abstract | The big data transformation currently revolutionizing science and industry forges novel possibilities in multimodal analysis scarcely imaginable only a decade ago. One of the important economic and industrial problems that stand to benefit from the recent expansion of data availability and computational prowess is the prediction of electricity demand and renewable energy generation. Both are correlates of human activity: spatiotemporal energy consumption patterns in society are a factor of both demand (weather dependent) and supply, which determine cost – a relation expected to strengthen along with increasing renewable energy dependence. One of the main drivers of European weather patterns is the activity of the Atlantic Ocean and in particular its dominant Northern Hemisphere current: the Gulf Stream. We choose this particular current as a test case in part due to larger amount of relevant data and scientific literature available for refinement of analysis techniques.
This data richness is due not only to its economic importance but also to its size being clearly visible in radar and infrared satellite imagery, which makes it easier to detect using Computer Vision (CV). The power of CV techniques makes basic analysis thus developed scalable to other smaller and less known, but still influential, currents, which are not just curves on a map, but complex, evolving, moving branching trees in 3D projected onto a 2D image. We investigate means of extracting, from several image modalities (including recently available Copernicus radar and earlier Infrared satellites), a parameterized presentation of the state of the Gulf Stream and its environment that is useful as feature space representation in a machine learning context, in this case with the EC’s H2020-sponsored ‘See.4C’ project, in the context of which data scientists may find novel predictors of spatiotemporal energy flow. Although automated extractors of Gulf Stream position exist, they differ in methodology and result. We shall attempt to extract more complex feature representation including branching points, eddies and parameterized changes in transport and velocity. Other related predictive features will be similarly developed, such as inference of deep water flux long the current path and wider spatial scale features such as Hough transform, surface turbulence indicators and temperature gradient indexes along with multi-time scale analysis of ocean height and temperature dynamics. The geospatial imaging and ML community may therefore benefit from a baseline of open-source techniques useful and expandable to other related prediction and/or scientific analysis tasks. |
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| Address | Vienna; Austria; April 2016 | ||||
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| Area | Expedition | Conference | EGU | ||
| Notes |
HuPBA;MV; | Approved | no | ||
| Call Number | Admin @ si @ PAE2016 | Serial | 2772 | ||
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| Author | Sergio Escalera; Mercedes Torres-Torres; Brais Martinez; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Georgios Tzimiropoulos; Ciprian Corneanu; Marc Oliu Simón; Mohammad Ali Bagheri; Michel Valstar | ||||
| Title | ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016 | Type | Conference Article | ||
| Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
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| Abstract | We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory classification and smile and gender classification, respectively. We present two crowd-sourcing methodologies used to collect manual annotations. A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age). For the Faces of the World data, the citizen-science Zooniverse platform was used. This paper summarizes the three challenges and the data used, as well as the results achieved by the participants of the competitions. Details of the ChaLearn LAP FotW competitions can be found at http://gesture.chalearn.org. | ||||
| Address | Las Vegas; USA; June 2016 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes |
HuPBA;MV; | Approved | no | ||
| Call Number | ETM2016 | Serial | 2849 | ||
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| Author | Victor Ponce; Baiyu Chen; Marc Oliu; Ciprian Corneanu; Albert Clapes; Isabelle Guyon; Xavier Baro; Hugo Jair Escalante; Sergio Escalera | ||||
| Title | ChaLearn LAP 2016: First Round Challenge on First Impressions – Dataset and Results | Type | Conference Article | ||
| Year | 2016 | Publication | 14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
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| Keywords | Behavior Analysis; Personality Traits; First Impressions | ||||
| Abstract | This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the rst round of the competition. The goal of the competition was to automatically evaluate ve \apparent“ personality traits (the so-called \Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by tting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source
platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the nal phase. Despite the diculty of the task, the teams made great advances in this round of the challenge. |
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| Address | Amsterdam; The Netherlands; October 2016 | ||||
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| Area | Expedition | Conference | ECCVW | ||
| Notes |
HuPBA;MV; 600.063 | Approved | no | ||
| Call Number | Admin @ si @ PCP2016 | Serial | 2828 | ||
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| Author | Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
| Title | Stable Airway Center Tracking for Bronchoscopic Navigation | Type | Conference Article | ||
| Year | 2016 | Publication | 28th Conference of the international Society for Medical Innovation and Technology | Abbreviated Journal | |
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| Abstract | Bronchoscopists use X‐ray fluoroscopy to guide bronchoscopes to the lesion to be biopsied without any kind of incisions. Reducing exposure to X‐ray is important for both patients and doctors but alternatives like electromagnetic navigation require specific equipment and increase the cost of the clinical procedure. We propose a guiding system based on the extraction of airway centers from intra‐operative videos. Such anatomical landmarks could be
matched to the airway centerline extracted from a pre‐planned CT to indicate the best path to the lesion. We present an extraction of lumen centers from intra‐operative videos based on tracking of maximal stable regions of energy maps. |
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| Address | Delft; Rotterdam; Leiden; The Netherlands; October 2016 | ||||
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| Area | Expedition | Conference | SMIT | ||
| Notes |
IAM; | Approved | no | ||
| Call Number | Admin @ si @ LSB2016a | Serial | 2856 | ||
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| Author | Antoni Gurgui; Debora Gil; Enric Marti; Vicente Grau | ||||
| Title | Left-Ventricle Basal Region Constrained Parametric Mapping to Unitary Domain | Type | Conference Article | ||
| Year | 2016 | Publication | 7th International Workshop on Statistical Atlases & Computational Modelling of the Heart | Abbreviated Journal | |
| Volume | 10124 | Issue | Pages | 163-171 | |
| Keywords | Laplacian; Constrained maps; Parameterization; Basal ring | ||||
| Abstract | Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries. | ||||
| Address | Athens; October 2016 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | STACOM | ||
| Notes |
IAM; | Approved | no | ||
| Call Number | Admin @ si @ GGM2016 | Serial | 2884 | ||
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| Author | Carles Sanchez; Debora Gil; T. Gache; N. Koufos; Marta Diez-Ferrer; Antoni Rosell | ||||
| Title | SENSA: a System for Endoscopic Stenosis Assessment | Type | Conference Article | ||
| Year | 2016 | Publication | 28th Conference of the international Society for Medical Innovation and Technology | Abbreviated Journal | |
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| Abstract | Documenting the severity of a static or dynamic Central Airway Obstruction (CAO) is crucial to establish proper diagnosis and treatment, predict possible treatment effects and better follow-up the patients. The subjective visual evaluation of a stenosis during video-bronchoscopy still remains the most common way to assess a CAO in spite of a consensus among experts for a need to standardize all calculations [1].
The Computer Vision Center in cooperation with the «Hospital de Bellvitge», has developed a System for Endoscopic Stenosis Assessment (SENSA), which computes CAO directly by analyzing standard bronchoscopic data without the need of using other imaging tecnologies. |
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| Address | Rotterdam; The Netherlands; October 2016 | ||||
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| Area | Expedition | Conference | SMIT | ||
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IAM; | Approved | no | ||
| Call Number | Admin @ si @ SGG2016 | Serial | 2942 | ||
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| Author | H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester; Rasmus R. Paulsena | ||||
| Title | Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior | Type | Journal Article | ||
| Year | 2016 | Publication | Patter Recognition Letters | Abbreviated Journal | PRL |
| Volume | 76 | Issue | 1 | Pages | 76-82 |
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IAM; 600.060 | Approved | no | ||
| Call Number | Admin @ si @ MFV2017b | Serial | 2941 | ||
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| Author | Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil | ||||
| Title | Stable Anatomical Structure Tracking for video-bronchoscopy Navigation | Type | Conference Article | ||
| Year | 2016 | Publication | 19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops | Abbreviated Journal | |
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| Keywords | Lung cancer diagnosis; video-bronchoscopy; airway lumen detection; region tracking | ||||
| Abstract | Bronchoscopy allows to examine the patient airways for detection of lesions and sampling of tissues without surgery. A main drawback in lung cancer diagnosis is the diculty to check whether the exploration is following the correct path to the nodule that has to be biopsied. The most extended guidance uses uoroscopy which implies repeated radiation of clinical sta and patients. Alternatives such as virtual bronchoscopy or electromagnetic navigation are very expensive and not completely robust to blood, mocus or deformations as to be extensively used. We propose a method that extracts and tracks stable lumen regions at di erent levels of the bronchial tree. The tracked regions are stored in a tree that encodes the anatomical structure of the scene which can be useful to retrieve the path to the lesion that the clinician should follow to do the biopsy. We present a multi-expert validation of our anatomical landmark extraction in 3 intra-operative ultrathin explorations. | ||||
| Address | Athens; Greece; October 2016 | ||||
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| Area | Expedition | Conference | MICCAIW | ||
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IAM; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ LSB2016b | Serial | 2857 | ||
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| Author | Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Cubero Noelia; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell | ||||
| Title | Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation | Type | Journal Article | ||
| Year | 2016 | Publication | Chest Journal | Abbreviated Journal | CHEST |
| Volume | 150 | Issue | 4 | Pages | 1003A |
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IAM; 600.096; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ DGC2016 | Serial | 3099 | ||
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