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| Author | Eugenio Alcala; Laura Sellart; Vicenc Puig; Joseba Quevedo; Jordi Saludes; David Vazquez; Antonio Lopez | ||||
| Title | Comparison of two non-linear model-based control strategies for autonomous vehicles | Type | Conference Article | ||
| Year | 2016 | Publication | 24th Mediterranean Conference on Control and Automation | Abbreviated Journal | |
| Volume | Issue | Pages | 846-851 | ||
| Keywords | Autonomous Driving; Control | ||||
| Abstract | This paper presents the comparison of two nonlinear model-based control strategies for autonomous cars. A control oriented model of vehicle based on a bicycle model is used. The two control strategies use a model reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a non-linear control law that is designed by means of the Lyapunov direct approach. The second approach is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high order sliding mode control. To test and compare the proposed control strategies, different path following scenarios are used in simulation. | ||||
| Address | Athens; Greece; June 2016 | ||||
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| Area | Expedition | Conference | MED | ||
| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ ASP2016 | Serial | 2750 | ||
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| Author | Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier | ||||
| Title | Filtrage de descripteurs locaux pour l'amélioration de la détection de documents | Type | Conference Article | ||
| Year | 2016 | Publication | Colloque International Francophone sur l'Écrit et le Document | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Local descriptors; mobile capture; document matching; keypoint selection | ||||
| Abstract | In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework.In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements. | ||||
| Address | Toulouse; France; March 2016 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CIFED | ||
| Notes | DAG; 600.084; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RCO2016 | Serial | 2755 | ||
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| Author | Marc Masana; Joost Van de Weijer; Andrew Bagdanov | ||||
| Title | On-the-fly Network pruning for object detection | Type | Conference Article | ||
| Year | 2016 | Publication | International conference on learning representations | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Object detection with deep neural networks is often performed by passing a few
thousand candidate bounding boxes through a deep neural network for each image. These bounding boxes are highly correlated since they originate from the same image. In this paper we investigate how to exploit feature occurrence at the image scale to prune the neural network which is subsequently applied to all bounding boxes. We show that removing units which have near-zero activation in the image allows us to significantly reduce the number of parameters in the network. Results on the PASCAL 2007 Object Detection Challenge demonstrate that up to 40% of units in some fully-connected layers can be entirely eliminated with little change in the detection result. |
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| Address | Puerto Rico; May 2016 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICLR | ||
| Notes | LAMP; 600.068; 600.106; 600.079 | Approved | no | ||
| Call Number | Admin @ si @MWB2016 | Serial | 2758 | ||
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| Author | Marc Oliu; Ciprian Corneanu; Laszlo A. Jeni; Jeffrey F. Cohn; Takeo Kanade; Sergio Escalera | ||||
| Title | Continuous Supervised Descent Method for Facial Landmark Localisation | Type | Conference Article | ||
| Year | 2016 | Publication | 13th Asian Conference on Computer Vision | Abbreviated Journal | |
| Volume | 10112 | Issue | Pages | 121-135 | |
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| Abstract | Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size. | ||||
| Address | Taipei; Taiwan; November 2016 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ACCV | ||
| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ OCJ2016 | Serial | 2838 | ||
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| Author | Ozan Caglayan; Walid Aransa; Yaxing Wang; Marc Masana; Mercedes Garcıa-Martinez; Fethi Bougares; Loic Barrault; Joost Van de Weijer | ||||
| Title | Does Multimodality Help Human and Machine for Translation and Image Captioning? | Type | Conference Article | ||
| Year | 2016 | Publication | 1st conference on machine translation | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR. | ||||
| Address | Berlin; Germany; August 2016 | ||||
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| Area | Expedition | Conference | WMT | ||
| Notes | LAMP; 600.106 ; 600.068 | Approved | no | ||
| Call Number | Admin @ si @ CAW2016 | Serial | 2761 | ||
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| Author | Esteve Cervantes; Long Long Yu; Andrew Bagdanov; Marc Masana; Joost Van de Weijer | ||||
| Title | Hierarchical Part Detection with Deep Neural Networks | Type | Conference Article | ||
| Year | 2016 | Publication | 23rd IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Object Recognition; Part Detection; Convolutional Neural Networks | ||||
| Abstract | Part detection is an important aspect of object recognition. Most approaches apply object proposals to generate hundreds of possible part bounding box candidates which are then evaluated by part classifiers. Recently several methods have investigated directly regressing to a limited set of bounding boxes from deep neural network representation. However, for object parts such methods may be unfeasible due to their relatively small size with respect to the image. We propose a hierarchical method for object and part detection. In a single network we first detect the object and then regress to part location proposals based only on the feature representation inside the object. Experiments show that our hierarchical approach outperforms a network which directly regresses the part locations. We also show that our approach obtains part detection accuracy comparable or better than state-of-the-art on the CUB-200 bird and Fashionista clothing item datasets with only a fraction of the number of part proposals. | ||||
| Address | Phoenix; Arizona; USA; September 2016 | ||||
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| Area | Expedition | Conference | ICIP | ||
| Notes | LAMP; 600.106 | Approved | no | ||
| Call Number | Admin @ si @ CLB2016 | Serial | 2762 | ||
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| Author | Isabelle Guyon; Imad Chaabane; Hugo Jair Escalante; Sergio Escalera; Damir Jajetic; James Robert Lloyd; Nuria Macia; Bisakha Ray; Lukasz Romaszko; Michele Sebag; Alexander Statnikov; Sebastien Treguer; Evelyne Viegas | ||||
| Title | A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention | Type | Conference Article | ||
| Year | 2016 | Publication | AutoML Workshop | Abbreviated Journal | |
| Volume | Issue | 1 | Pages | 1-8 | |
| Keywords | AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning | ||||
| Abstract | The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully automatic, black-box learning machines for feature-based classification and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across different types of complexity. Over six rounds, participants succeeded in delivering AutoML software capable of being trained and tested without human intervention. Although improvements can still be made to close the gap between human-tweaked and AutoML models, this competition contributes to the development of fully automated environments by challenging practitioners to solve problems under specific constraints and sharing their approaches; the platform will remain available for post-challenge submissions at http://codalab.org/AutoML. | ||||
| Address | New York; USA; June 2016 | ||||
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| Area | Expedition | Conference | ICML | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ GCE2016 | Serial | 2769 | ||
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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 | Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez | ||||
| Title | Stereo Matching using SGM on the GPU | Type | Report | ||
| Year | 2016 | Publication | Programming and Tuning Massively Parallel Systems | Abbreviated Journal | PUMPS |
| Volume | Issue | Pages | |||
| Keywords | CUDA; Stereo; Autonomous Vehicle | ||||
| Abstract | Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy efficient GPU devices. Our design runs on a Tegra X1 at 42 frames per second (fps) for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method. | ||||
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| Area | Expedition | Conference | PUMPS | ||
| Notes | ADAS; 600.085; 600.087; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ HCE2016b | Serial | 2776 | ||
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| Author | Gloria Fernandez Esparrach; Jorge Bernal; Cristina Rodriguez de Miguel; Debora Gil; Fernando Vilariño; Henry Cordova; Cristina Sanchez Montes; Isis Ara | ||||
| Title | Utilidad de la visión por computador para la localización de pólipos pequeños y planos | Type | Conference Article | ||
| Year | 2016 | Publication | XIX Reunión Nacional de la Asociación Española de Gastroenterología, Gastroenterology Hepatology | Abbreviated Journal | |
| Volume | 39 | Issue | 2 | Pages | 94 |
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| Address | Madrid (Spain) | ||||
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| Area | Expedition | Conference | AEGASTRO | ||
| Notes | MV; IAM; 600.097;SIAI | Approved | no | ||
| Call Number | Admin @ si @FBR2016 | Serial | 2779 | ||
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| Author | E. Tavalera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva | ||||
| Title | Regularized Clustering for Egocentric Video Segmentation | Type | Book Chapter | ||
| Year | 2015 | Publication | Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | Issue | Pages | 327-336 | ||
| Keywords | Temporal video segmentation ; Egocentric videos ; Clustering | ||||
| Abstract | In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-319-19390-8 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @TDB2015a | Serial | 2781 | ||
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| Author | Maria Oliver; Gloria Haro; Mariella Dimiccoli; Baptiste Mazin; Coloma Ballester | ||||
| Title | A computational model of amodal completion | Type | Conference Article | ||
| Year | 2016 | Publication | SIAM Conference on Imaging Science | Abbreviated Journal | |
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| Abstract | This paper presents a computational model to recover the most likely interpretation of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling. | ||||
| Address | Albuquerque; New Mexico; USA; May 2016 | ||||
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| Area | Expedition | Conference | IS | ||
| Notes | MILAB; 601.235 | Approved | no | ||
| Call Number | Admin @ si @OHD2016a | Serial | 2788 | ||
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| Author | G. de Oliveira; A. Cartas; Marc Bolaños; Mariella Dimiccoli; Xavier Giro; Petia Radeva | ||||
| Title | LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task | Type | Conference Article | ||
| Year | 2016 | Publication | 12th NTCIR Conference on Evaluation of Information Access Technologies | Abbreviated Journal | |
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| Abstract | Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising. | ||||
| Address | Tokyo; Japan; June 2016 | ||||
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| Area | Expedition | Conference | NTCIR | ||
| Notes | MILAB; | Approved | no | ||
| Call Number | Admin @ si @OCB2016 | Serial | 2789 | ||
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| Author | G. de Oliveira; Mariella Dimiccoli; Petia Radeva | ||||
| Title | Egocentric Image Retrieval With Deep Convolutional Neural Networks | Type | Conference Article | ||
| Year | 2016 | Publication | 19th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
| Volume | Issue | Pages | 71-76 | ||
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| Address | Barcelona; Spain; October 2016 | ||||
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| Area | Expedition | Conference | CCIA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ODR2016 | Serial | 2790 | ||
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| Author | Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva | ||||
| Title | With whom do I interact with? Social interaction detection in egocentric photo-streams | Type | Conference Article | ||
| Year | 2016 | Publication | 23rd International Conference on Pattern Recognition | Abbreviated Journal | |
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| Abstract | Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams. | ||||
| Address | Cancun; Mexico; December 2016 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ADR2016a | Serial | 2791 | ||
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