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| Author | Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez | ||||
| Title | Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books | Type | Conference Article | ||
| Year | 2016 | Publication | 15th international conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
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| Abstract | Handwritten marriage licenses books have been used for centuries by ecclesiastical and secular institutions to register marriages. The information contained in these historical documents is useful for demography studies and
genealogical research, among others. Despite the generally simple structure of the text in these documents, automatic transcription and semantic information extraction is difficult due to the distinct and evolutionary vocabulary, which is composed mainly of proper names that change along the time. In previous works we studied the use of category-based language models to both improve the automatic transcription accuracy and make easier the extraction of semantic information. Here we analyze the main causes of the semantic errors observed in previous results and apply a Grammatical Inference technique known as MGGI to improve the semantic accuracy of the language model obtained. Using this language model, full handwritten text recognition experiments have been carried out, with results supporting the interest of the proposed approach. |
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| Address | Shenzhen; China; October 2016 | ||||
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| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.097; 602.006 | Approved | no | ||
| Call Number | Admin @ si @ RFV2016 | Serial | 2909 | ||
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| Author | Xavier Baro; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Lukasz Romaszko; Lisheng Sun; Sebastien Treguer; Evelyne Viegas | ||||
| Title | Coompetitions in machine learning: case studies | Type | Conference Article | ||
| Year | 2016 | Publication | 30th Annual Conference on Neural Information Processing Systems Worshops | Abbreviated Journal | |
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| Address | Barcelona; Spain; December 2016 | ||||
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| Area | Expedition | Conference | NIPSW | ||
| Notes | HuPBA | Approved | no | ||
| Call Number | Admin @ si @ BEG2016 | Serial | 2911 | ||
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| Author | Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira | ||||
| Title | Incremental texture mapping for autonomous driving | Type | Journal Article | ||
| Year | 2016 | Publication | Robotics and Autonomous Systems | Abbreviated Journal | RAS |
| Volume | 84 | Issue | Pages | 113-128 | |
| Keywords | Scene reconstruction; Autonomous driving; Texture mapping | ||||
| Abstract | Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. | ||||
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| Notes | ADAS; 600.086 | Approved | no | ||
| Call Number | Admin @ si @ OSS2016b | Serial | 2912 | ||
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| Author | Wenjuan Gong; Xuena Zhang; Jordi Gonzalez; Andrews Sobral; Thierry Bouwmans; Changhe Tu; El-hadi Zahzah | ||||
| Title | Human Pose Estimation from Monocular Images: A Comprehensive Survey | Type | Journal Article | ||
| Year | 2016 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 16 | Issue | 12 | Pages | 1966 |
| Keywords | human pose estimation; human bodymodels; generativemethods; discriminativemethods; top-down methods; bottom-up methods | ||||
| Abstract | Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling
methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used. |
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| Notes | ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ GZG2016 | Serial | 2933 | ||
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| Author | Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund | ||||
| Title | Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams | Type | Journal Article | ||
| Year | 2016 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 75 | Issue | 22 | Pages | 14985-14990 |
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| Notes | ISE; HUPBA | Approved | no | ||
| Call Number | Admin @ si @ DDB2016 | Serial | 2934 | ||
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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 | ||
| Notes | IAM; | Approved | no | ||
| Call Number | Admin @ si @ SGG2016 | Serial | 2942 | ||
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| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Sparse representation over learned dictionary for symbol recognition | Type | Journal Article | ||
| Year | 2016 | Publication | Signal Processing | Abbreviated Journal | SP |
| Volume | 125 | Issue | Pages | 36-47 | |
| Keywords | Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points | ||||
| Abstract | In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. | ||||
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| Notes | DAG; 600.061; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ DTR2016 | Serial | 2946 | ||
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| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title | Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary | Type | Book Chapter | ||
| Year | 2016 | Publication | Recent Trends in Image Processing and Pattern Recognition | Abbreviated Journal | |
| Volume | 709 | Issue | Pages | ||
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| Area | Expedition | Conference | RTIP2R | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ HTR2016 | Serial | 2956 | ||
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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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| Notes | IAM; 600.096; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ DGC2016 | Serial | 3099 | ||
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| Author | L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip | ||||
| Title | Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation | Type | Journal Article | ||
| Year | 2016 | Publication | Computers & Industrial Engineering | Abbreviated Journal | CIE |
| Volume | 94 | Issue | Pages | 93-104 | |
| Keywords | Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning | ||||
| Abstract | In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here di ers in terms of the proposed solutions from the traditional one. | ||||
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| Publisher | PERGAMON-ELSEVIER SCIENCE LTD | Place of Publication | Editor | ||
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CIE | ||
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| ISSN | 0360-8352 | ISBN | Medium | ||
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| Notes | OR;MV; | Approved | no | ||
| Call Number | Admin @ si @ CFG2016 | Serial | 2749 | ||
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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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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 | 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 | ||||
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LNCS | ||
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| Area | Expedition | Conference | ECCV | ||
| Notes | ADAS; 600.076; 600.085 | Approved | no | ||
| Call Number | Admin @ si @ SGV2016 | Serial | 2824 | ||
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| Author | Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio | ||||
| Title | EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game | Type | Conference Article | ||
| Year | 2016 | Publication | 5th International Conference Games and Learning Alliance | Abbreviated Journal | |
| Volume | 10056 | Issue | Pages | 50-59 | |
| Keywords | Simulation environment; Automated Driving; Driver-Vehicle interaction | ||||
| Abstract | Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. | ||||
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LNCS | ||
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| Area | Expedition | Conference | GALA | ||
| Notes | ADAS;IAM; | Approved | no | ||
| Call Number | HAC2016 | Serial | 2864 | ||
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| Author | Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier | ||||
| Title | LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode | Type | Conference Article | ||
| Year | 2016 | Publication | 14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | 9915 | Issue | Pages | 894-900 | |
| Keywords | Simulation environment; Automated Driving; Driver-Vehicle interaction | ||||
| Abstract | Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. | ||||
| Address | Amsterdam; The Netherlands; October 2016 | ||||
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LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | ADAS;IAM; 600.085; 600.076 | Approved | no | ||
| Call Number | MHE2016 | Serial | 2865 | ||
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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 | ||||
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LNCS | ||
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| 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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