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| Author | Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal | ||||
| Title | Local Binary Pattern for Word Spotting in Handwritten Historical Document | Type | Conference Article | ||
| Year | 2016 | Publication | Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) | Abbreviated Journal | |
| Volume | Issue | Pages | 574-583 | ||
| Keywords | Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data | ||||
| Abstract | Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm. | ||||
| Address | Merida; Mexico; December 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | S+SSPR | ||
| Notes | DAG; 600.097; 602.006; 603.053 | Approved | no | ||
| Call Number | Admin @ si @ DNL2016 | Serial | 2876 | ||
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| Author | Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras | ||||
| Title | Information Theoretic Rotationwise Robust Binary Descriptor Learning | Type | Conference Article | ||
| Year | 2016 | Publication | Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) | Abbreviated Journal | |
| Volume | Issue | Pages | 368-378 | ||
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| Abstract | In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications. | ||||
| Address | Mérida; Mexico; November 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | S+SSPR | ||
| Notes | DAG; ADAS; 600.097; 600.086 | Approved | no | ||
| Call Number | Admin @ si @ RLL2016 | Serial | 2871 | ||
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| Author | Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez | ||||
| Title | Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition | Type | Conference Article | ||
| Year | 2016 | Publication | 14th European Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 697-716 | ||
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| Abstract | Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including recent deep models trained on millions of manually labelled images and videos. | ||||
| Address | Amsterdam; The Netherlands; October 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCV | ||
| Notes | ADAS; 600.076; 600.085 | Approved | no | ||
| Call Number | Admin @ si @ SGV2016 | Serial | 2824 | ||
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| Author | Dennis H. Lundtoft; Kamal Nasrollahi; Thomas B. Moeslund; Sergio Escalera | ||||
| Title | Spatiotemporal Facial Super-Pixels for Pain Detection | Type | Conference Article | ||
| Year | 2016 | Publication | 9th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Facial images; Super-pixels; Spatiotemporal filters; Pain detection | ||||
| Abstract | Best student paper award.
Pain detection using facial images is of critical importance in many Health applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBCMcMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios. |
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| Address | Palma de Mallorca; Spain; July 2016 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | AMDO | ||
| Notes | HUPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ LNM2016 | Serial | 2847 | ||
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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
| Title | Action Recognition by Pairwise Proximity Function Support Vector Machines with Dynamic Time Warping Kernels | Type | Conference Article | ||
| Year | 2016 | Publication | 29th Canadian Conference on Artificial Intelligence | Abbreviated Journal | |
| Volume | 9673 | Issue | Pages | 3-14 | |
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| Abstract | In the context of human action recognition using skeleton data, the 3D trajectories of joint points may be considered as multi-dimensional time series. The traditional recognition technique in the literature is based on time series dis(similarity) measures (such as Dynamic Time Warping). For these general dis(similarity) measures, k-nearest neighbor algorithms are a natural choice. However, k-NN classifiers are known to be sensitive to noise and outliers. In this paper, a new class of Support Vector Machine that is applicable to trajectory classification, such as action recognition, is developed by incorporating an efficient time-series distances measure into the kernel function. More specifically, the derivative of Dynamic Time Warping (DTW) distance measure is employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite (PSD) kernels in the SVM formulation. The recognition results of the proposed technique on two action recognition datasets demonstrates the ourperformance of our methodology compared to the state-of-the-art methods. Remarkably, we obtained 89 % accuracy on the well-known MSRAction3D dataset using only 3D trajectories of body joints obtained by Kinect | ||||
| Address | Victoria; Canada; May 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | AI | ||
| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ BGE2016b | Serial | 2770 | ||
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| Author | Carlos David Martinez Hinarejos; Josep Llados; Alicia Fornes; Francisco Casacuberta; Lluis de Las Heras; Joan Mas; Moises Pastor; Oriol Ramos Terrades; Joan Andreu Sanchez; Enrique Vidal; Fernando Vilariño | ||||
| Title | Context, multimodality, and user collaboration in handwritten text processing: the CoMUN-HaT project | Type | Conference Article | ||
| Year | 2016 | Publication | 3rd IberSPEECH | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Processing of handwritten documents is a task that is of wide interest for many
purposes, such as those related to preserve cultural heritage. Handwritten text recognition techniques have been successfully applied during the last decade to obtain transcriptions of handwritten documents, and keyword spotting techniques have been applied for searching specific terms in image collections of handwritten documents. However, results on transcription and indexing are far from perfect. In this framework, the use of new data sources arises as a new paradigm that will allow for a better transcription and indexing of handwritten documents. Three main different data sources could be considered: context of the document (style, writer, historical time, topics,. . . ), multimodal data (representations of the document in a different modality, such as the speech signal of the dictation of the text), and user feedback (corrections, amendments,. . . ). The CoMUN-HaT project aims at the integration of these different data sources into the transcription and indexing task for handwritten documents: the use of context derived from the analysis of the documents, how multimodality can aid the recognition process to obtain more accurate transcriptions (including transcription in a modern version of the language), and integration into a userin-the-loop assisted text transcription framework. This will be reflected in the construction of a transcription and indexing platform that can be used by both professional and nonprofessional users, contributing to crowd-sourcing activities to preserve cultural heritage and to obtain an accessible version of the involved corpus. |
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| Address | Lisboa; Portugal; November 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IberSPEECH | ||
| Notes | DAG; MV; 600.097;SIAI | Approved | no | ||
| Call Number | Admin @ si @MLF2016 | Serial | 2813 | ||
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| Author | Victor Campmany; Sergio Silva; Juan Carlos Moure; Toni Espinosa; David Vazquez; Antonio Lopez | ||||
| Title | GPU-based pedestrian detection for autonomous driving | Type | Conference Article | ||
| Year | 2016 | Publication | GPU Technology Conference | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Pedestrian Detection; GPU | ||||
| Abstract | Pedestrian detection for autonomous driving is one of the hardest tasks within computer vision, and involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system that includes LBP and HOG as feature descriptors and SVM and Random forest as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture. The aim is to deploy a real-time system providing reliable results. | ||||
| Address | Silicon Valley; San Francisco; USA; April 2016 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | GTC | ||
| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ CSM2016 | Serial | 2737 | ||
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| Author | Daniel Hernandez; Juan Carlos Moure; Toni Espinosa; Alejandro Chacon; David Vazquez; Antonio Lopez | ||||
| Title | Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching | Type | Conference Article | ||
| Year | 2016 | Publication | GPU Technology Conference | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Stereo; Autonomous Driving; GPU; 3d reconstruction | ||||
| Abstract | Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms | ||||
| Address | Silicon Valley; San Francisco; USA; April 2016 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | GTC | ||
| Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ HME2016 | Serial | 2738 | ||
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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 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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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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| 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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| 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 | |
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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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