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| Author | Xavier Baro; Jordi Gonzalez; Junior Fabian; Miguel Angel Bautista; Marc Oliu; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera | ||||
| Title | ChaLearn Looking at People 2015 challenges: action spotting and cultural event recognition | Type | Conference Article | ||
| Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue | Pages | 1-9 | ||
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| Abstract | Following previous series on Looking at People (LAP) challenges [6, 5, 4], ChaLearn ran two competitions to be presented at CVPR 2015: action/interaction spotting and cultural event recognition in RGB data. We ran a second round on human activity recognition on RGB data sequences. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes the two performed challenges and obtained results. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/. | ||||
| Address | Boston; EEUU; June 2015 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | HuPBA;MV | Approved | no | ||
| Call Number | Serial | 2652 | |||
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| Author | Andres Traumann; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | A New Retexturing Method for Virtual Fitting Room Using Kinect 2 Camera | Type | Conference Article | ||
| Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue | Pages | 75-79 | ||
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| Address | Boston; EEUU; June 2015 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ TEA2015 | Serial | 2653 | ||
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| Author | Ramin Irani; Kamal Nasrollahi; Chris Bahnsen; D.H. Lundtoft; Thomas B. Moeslund; Marc O. Simon; Ciprian Corneanu; Sergio Escalera; Tanja L. Pedersen; Maria-Louise Klitgaard; Laura Petrini | ||||
| Title | Spatio-temporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition | Type | Conference Article | ||
| Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue | Pages | 88-95 | ||
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| Abstract | Pain is a vital sign of human health and its automatic detection can be of crucial importance in many different contexts, including medical scenarios. While most available computer vision techniques are based on RGB, in this paper, we investigate the effect of combining RGB, depth, and thermal
facial images for pain detection and pain intensity level recognition. For this purpose, we extract energies released by facial pixels using a spatiotemporal filter. Experiments on a group of 12 elderly people applying the multimodal approach show that the proposed method successfully detects pain and recognizes between three intensity levels in 82% of the analyzed frames improving more than 6% over RGB only analysis in similar conditions. |
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| Address | Boston; EEUU; June 2015 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ INB2015 | Serial | 2654 | ||
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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Albert Clapes; Kamal Nasrollahi; Michael Holte; Thomas B. Moeslund | ||||
| Title | Keep it Accurate and Diverse: Enhancing Action Recognition Performance by Ensemble Learning | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue | Pages | 22-29 | ||
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| Abstract | The performance of different action recognition techniques has recently been studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of action learning techniques, each performing the recognition task from a different perspective.
The underlying idea is that instead of aiming a very sophisticated and powerful representation/learning technique, we can learn action categories using a set of relatively simple and diverse classifiers, each trained with different feature set. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a learner on an unseen action recognition scenario. This leads to having a more robust and general-applicable framework. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing enhanced performance of the proposed methodology. |
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| Address | Boston; EEUU; June 2015 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGE2015 | Serial | 2655 | ||
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| Author | Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Mehreen Saeed; Alexander Statnikov; Evelyne Viegas | ||||
| Title | AutoML Challenge 2015: Design and First Results | Type | Conference Article | ||
| Year | 2015 | Publication | 32nd International Conference on Machine Learning, ICML workshop, JMLR proceedings ICML15 | Abbreviated Journal | |
| Volume | Issue | Pages | 1-8 | ||
| Keywords | AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning | ||||
| Abstract | ChaLearn is organizing the Automatic Machine Learning (AutoML) contest 2015, which challenges participants to solve classi cation and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing diculty are introduced throughout the six rounds of the challenge. (Participants can
enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen (AutoML), and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance (Tweakathon). This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML. |
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| Address | Lille; France; July 2015 | ||||
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| Area | Expedition | Conference | ICML | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ GBC2015c | Serial | 2656 | ||
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| Author | Victor Ponce; Hugo Jair Escalante; Sergio Escalera; Xavier Baro | ||||
| Title | Gesture and Action Recognition by Evolved Dynamic Subgestures | Type | Conference Article | ||
| Year | 2015 | Publication | 26th British Machine Vision Conference | Abbreviated Journal | |
| Volume | Issue | Pages | 129.1-129.13 | ||
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| Abstract | This paper introduces a framework for gesture and action recognition based on the evolution of temporal gesture primitives, or subgestures. Our work is inspired on the principle of producing genetic variations within a population of gesture subsequences, with the goal of obtaining a set of gesture units that enhance the generalization capability of standard gesture recognition approaches. In our context, gesture primitives are evolved over time using dynamic programming and generative models in order to recognize complex actions. In few generations, the proposed subgesture-based representation
of actions and gestures outperforms the state of the art results on the MSRDaily3D and MSRAction3D datasets. |
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| Address | Swansea; uk; September 2015 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ PEE2015 | Serial | 2657 | ||
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| Author | Huamin Ren; Weifeng Liu; Soren Ingvor Olsen; Sergio Escalera; Thomas B. Moeslund | ||||
| Title | Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event Detection | Type | Conference Article | ||
| Year | 2015 | Publication | 26th British Machine Vision Conference | Abbreviated Journal | |
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| Address | Swansea; uk; September 2015 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ RLO2015 | Serial | 2658 | ||
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| Author | Eduardo Tusa; Arash Akbarinia; Raquel Gil Rodriguez; Corina Barbalata | ||||
| Title | Real-Time Face Detection and Tracking Utilising OpenMP and ROS | Type | Conference Article | ||
| Year | 2015 | Publication | 3rd Asia-Pacific Conference on Computer Aided System Engineering | Abbreviated Journal | |
| Volume | Issue | Pages | 179 - 184 | ||
| Keywords | RGB-D; Kinect; Human Detection and Tracking; ROS; OpenMP | ||||
| Abstract | The first requisite of a robot to succeed in social interactions is accurate human localisation, i.e. subject detection and tracking. Later, it is estimated whether an interaction partner seeks attention, for example by interpreting the position and orientation of the body. In computer vision, these cues usually are obtained in colour images, whose qualities are degraded in ill illuminated social scenes. In these scenarios depth sensors offer a richer representation. Therefore, it is important to combine colour and depth information. The
second aspect that plays a fundamental role in the acceptance of social robots is their real-time-ability. Processing colour and depth images is computationally demanding. To overcome this we propose a parallelisation strategy of face detection and tracking based on two different architectures: message passing and shared memory. Our results demonstrate high accuracy in low computational time, processing nine times more number of frames in a parallel implementation. This provides a real-time social robot interaction. |
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| Address | Quito; Ecuador; July 2015 | ||||
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| Area | Expedition | Conference | APCASE | ||
| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ TAG2015 | Serial | 2659 | ||
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| Author | Arash Akbarinia; C. Alejandro Parraga | ||||
| Title | Biologically Plausible Colour Naming Model | Type | Conference Article | ||
| Year | 2015 | Publication | European Conference on Visual Perception ECVP2015 | Abbreviated Journal | |
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| Address | Liverpool; UK; August 2015 | ||||
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| Area | Expedition | Conference | ECVP | ||
| Notes | NEUROBIT; 600.068 | Approved | no | ||
| Call Number | Admin @ si @ AkP2015 | Serial | 2660 | ||
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| Author | Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias | ||||
| Title | Scene Representations for Autonomous Driving: an approach based on polygonal primitives | Type | Conference Article | ||
| Year | 2015 | Publication | 2nd Iberian Robotics Conference ROBOT2015 | Abbreviated Journal | |
| Volume | 417 | Issue | Pages | 503-515 | |
| Keywords | Scene reconstruction; Point cloud; Autonomous vehicles | ||||
| Abstract | In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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| Address | Lisboa; Portugal; November 2015 | ||||
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| Area | Expedition | Conference | ROBOT | ||
| Notes | ADAS; 600.076; 600.086 | Approved | no | ||
| Call Number | Admin @ si @ OSS2015a | Serial | 2662 | ||
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| Author | J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa | ||||
| Title | Visible-Thermal Fusion based Monocular Visual Odometry | Type | Conference Article | ||
| Year | 2015 | Publication | 2nd Iberian Robotics Conference ROBOT2015 | Abbreviated Journal | |
| Volume | 417 | Issue | Pages | 517-528 | |
| Keywords | Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion. | ||||
| Abstract | The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. |
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| Address | Lisboa; Portugal; November 2015 | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | ||
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| ISSN | 2194-5357 | ISBN | 978-3-319-27145-3 | Medium | |
| Area | Expedition | Conference | ROBOT | ||
| Notes | ADAS; 600.076; 600.086 | Approved | no | ||
| Call Number | Admin @ si @ PAD2015 | Serial | 2663 | ||
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| Author | Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom | ||||
| Title | Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains | Type | Conference Article | ||
| Year | 2015 | Publication | International Conference on Intelligent Robots and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 2488 - 2495 | ||
| Keywords | Visual Learning; Computer Vision; Autonomous Agents | ||||
| Abstract | In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks. | ||||
| Address | Hamburg; Germany; October 2015 | ||||
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| Area | Expedition | Conference | IROS | ||
| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ OSL2015 | Serial | 2664 | ||
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| Author | Mohammad Rouhani; Angel Sappa | ||||
| Title | The Richer Representation the Better Registration | Type | Journal Article | ||
| Year | 2013 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 22 | Issue | 12 | Pages | 5036-5049 |
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| Abstract | In this paper, the registration problem is formulated as a point to model distance minimization. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, this formulation avoids the correspondence search that is time-consuming. In the first stage, the target set is described through an implicit function by employing a linear least squares fitting. This function can be either an implicit polynomial or an implicit B-spline from a coarse to fine representation. In the second stage, we show how the obtained implicit representation is used as an interface to convert point-to-point registration into point-to-implicit problem. Furthermore, we show that this registration distance is smooth and can be minimized through the Levengberg-Marquardt algorithm. All the formulations presented for both stages are compact and easy to implement. In addition, we show that our registration method can be handled using any implicit representation though some are coarse and others provide finer representations; hence, a tradeoff between speed and accuracy can be set by employing the right implicit function. Experimental results and comparisons in 2D and 3D show the robustness and the speed of convergence of the proposed approach. | ||||
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| ISSN | 1057-7149 | ISBN | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ RoS2013 | Serial | 2665 | ||
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| Author | Carolina Malagelada; Michal Drozdzal; Santiago Segui; Sara Mendez; Jordi Vitria; Petia Radeva; Javier Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz | ||||
| Title | Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis | Type | Journal Article | ||
| Year | 2015 | Publication | American Journal of Physiology-Gastrointestinal and Liver Physiology | Abbreviated Journal | AJPGI |
| Volume | 309 | Issue | 6 | Pages | G413--G419 |
| Keywords | capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning | ||||
| Abstract | We have previously developed an original method to evaluate small bowel motor function based on computer vision analysis of endoluminal images obtained by capsule endoscopy. Our aim was to demonstrate intestinal motor abnormalities in patients with functional bowel disorders by endoluminal vision analysis. Patients with functional bowel disorders (n = 205) and healthy subjects (n = 136) ingested the endoscopic capsule (Pillcam-SB2, Given-Imaging) after overnight fast and 45 min after gastric exit of the capsule a liquid meal (300 ml, 1 kcal/ml) was administered. Endoluminal image analysis was performed by computer vision and machine learning techniques to define the normal range and to identify clusters of abnormal function. After training the algorithm, we used 196 patients and 48 healthy subjects, completely naive, as test set. In the test set, 51 patients (26%) were detected outside the normal range (P < 0.001 vs. 3 healthy subjects) and clustered into hypo- and hyperdynamic subgroups compared with healthy subjects. Patients with hypodynamic behavior (n = 38) exhibited less luminal closure sequences (41 ± 2% of the recording time vs. 61 ± 2%; P < 0.001) and more static sequences (38 ± 3 vs. 20 ± 2%; P < 0.001); in contrast, patients with hyperdynamic behavior (n = 13) had an increased proportion of luminal closure sequences (73 ± 4 vs. 61 ± 2%; P = 0.029) and more high-motion sequences (3 ± 1 vs. 0.5 ± 0.1%; P < 0.001). Applying an original methodology, we have developed a novel classification of functional gut disorders based on objective, physiological criteria of small bowel function. | ||||
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| Publisher | American Physiological Society | Place of Publication | Editor | ||
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| Notes | MILAB; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ MDS2015 | Serial | 2666 | ||
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| Author | R.A.Bendezu; E.Barba; E.Burri; D.Cisternas; Carolina Malagelada; Santiago Segui; Anna Accarino; S.Quiroga; E.Monclus; I.Navazo | ||||
| Title | Intestinal gas content and distribution in health and in patients with functional gut symptoms | Type | Journal Article | ||
| Year | 2015 | Publication | Neurogastroenterology & Motility | Abbreviated Journal | NEUMOT |
| Volume | 27 | Issue | 9 | Pages | 1249-1257 |
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| Abstract | BACKGROUND:
The precise relation of intestinal gas to symptoms, particularly abdominal bloating and distension remains incompletely elucidated. Our aim was to define the normal values of intestinal gas volume and distribution and to identify abnormalities in relation to functional-type symptoms. METHODS: Abdominal computed tomography scans were evaluated in healthy subjects (n = 37) and in patients in three conditions: basal (when they were feeling well; n = 88), during an episode of abdominal distension (n = 82) and after a challenge diet (n = 24). Intestinal gas content and distribution were measured by an original analysis program. Identification of patients outside the normal range was performed by machine learning techniques (one-class classifier). Results are expressed as median (IQR) or mean ± SE, as appropriate. KEY RESULTS: In healthy subjects the gut contained 95 (71, 141) mL gas distributed along the entire lumen. No differences were detected between patients studied under asymptomatic basal conditions and healthy subjects. However, either during a spontaneous bloating episode or once challenged with a flatulogenic diet, luminal gas was found to be increased and/or abnormally distributed in about one-fourth of the patients. These patients detected outside the normal range by the classifier exhibited a significantly greater number of abnormal features than those within the normal range (3.7 ± 0.4 vs 0.4 ± 0.1; p < 0.001). CONCLUSIONS & INFERENCES: The analysis of a large cohort of subjects using original techniques provides unique and heretofore unavailable information on the volume and distribution of intestinal gas in normal conditions and in relation to functional gastrointestinal symptoms. |
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ BBB2015 | Serial | 2667 | ||
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