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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 The information contained in these historical documents is useful for demography studies and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 | Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari | ||||
Title | Human Head Pose Estimation on SASE database using Random Hough Regression Forests | Type | Conference Article | ||
Year | 2016 | Publication | 23rd International Conference on Pattern Recognition Workshops | Abbreviated Journal | |
Volume | 10165 | Issue | Pages | ||
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Abstract | In recent years head pose estimation has become an important task in face analysis scenarios. Given the availability of high resolution 3D sensors, the design of a high resolution head pose database would be beneficial for the community. In this paper, Random Hough Forests are used to estimate 3D head pose and location on a new 3D head database, SASE, which represents the baseline performance on the new data for an upcoming international head pose estimation competition. The data in SASE is acquired with a Microsoft Kinect 2 camera, including the RGB and depth information of 50 subjects with a large sample of head poses, allowing us to test methods for real-life scenarios. We briefly review the database while showing baseline head pose estimation results based on Random Hough Forests. | ||||
Address | Cancun; Mexico; December 2016 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | ICPRW | ||
Notes | HuPBA; | Approved | no | ||
Call Number | Admin @ si @ LEA2016b | Serial | 2910 | ||
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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 specically, 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 | Jaume Amores | ||||
Title | MILDE: multiple instance learning by discriminative embedding | Type | Journal Article | ||
Year | 2015 | Publication | Knowledge and Information Systems | Abbreviated Journal | KAIS |
Volume | 42 | Issue | 2 | Pages | 381-407 |
Keywords | Multi-instance learning; Codebook; Bag of words | ||||
Abstract | While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. | ||||
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Publisher | Springer London | Place of Publication | Editor | ||
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ISSN | 0219-1377 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 601.042; 600.057; 600.076 | Approved | no | ||
Call Number | Admin @ si @ Amo2015 | Serial | 2383 | ||
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Author | Eloi Puertas; Sergio Escalera; Oriol Pujol | ||||
Title | Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
Volume | 18 | Issue | 2 | Pages | 247-261 |
Keywords | Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification | ||||
Abstract | In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches. | ||||
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Publisher | Springer-Verlag | Place of Publication | Editor | ||
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Series Volume | Series Issue | Edition | |||
ISSN | 1433-7541 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ PEP2013 | Serial | 2251 | ||
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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
Title | Combining Local and Global Learners in the Pairwise Multiclass Classification | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
Volume | 18 | Issue | 4 | Pages | 845-860 |
Keywords | Multiclass classification; Pairwise approach; One-versus-one | ||||
Abstract | Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes. | ||||
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Publisher | Springer London | Place of Publication | Editor | ||
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ISSN | 1433-7541 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ BGE2014 | Serial | 2441 | ||
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Author | Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores | ||||
Title | Spatiotemporal Stacked Sequential Learning for Pedestrian Detection | Type | Conference Article | ||
Year | 2015 | Publication | Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 | Abbreviated Journal | |
Volume | Issue | Pages | 3-12 | ||
Keywords | SSL; Pedestrian Detection | ||||
Abstract | Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. | ||||
Address | Santiago de Compostela; España; June 2015 | ||||
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Area | ACDC | Expedition | Conference | IbPRIA | |
Notes | ADAS; 600.057; 600.054; 600.076 | Approved | no | ||
Call Number | GRV2015; ADAS @ adas @ GRV2015 | Serial | 2454 | ||
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Author | Naveen Onkarappa; Angel Sappa | ||||
Title | Synthetic sequences and ground-truth flow field generation for algorithm validation | Type | Journal Article | ||
Year | 2015 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
Volume | 74 | Issue | 9 | Pages | 3121-3135 |
Keywords | Ground-truth optical flow; Synthetic sequence; Algorithm validation | ||||
Abstract | Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems. | ||||
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Publisher | Springer US | Place of Publication | Editor | ||
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ISSN | 1380-7501 | ISBN | Medium | ||
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Notes | ADAS; 600.055; 601.215; 600.076 | Approved | no | ||
Call Number | Admin @ si @ OnS2014b | Serial | 2472 | ||
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