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Author Arnau Ramisa; Alex Goldhoorn; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras edit  doi
openurl 
  Title Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas Type Journal Article
  Year 2011 Publication Journal of Intelligent and Robotic Systems Abbreviated Journal JIRC  
  Volume 64 Issue 3-4 Pages 625-649  
  Keywords  
  Abstract Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Netherlands Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0921-0296 ISBN Medium  
  Area Expedition Conference  
  Notes RV;ADAS Approved no  
  Call Number Admin @ si @ RGA2011 Serial 1728  
Permanent link to this record
 

 
Author Arnau Ramisa; Adriana Tapus; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras edit  doi
openurl 
  Title Robust Vision-Based Localization using Combinations of Local Feature Regions Detectors Type Journal Article
  Year 2009 Publication Autonomous Robots Abbreviated Journal AR  
  Volume 27 Issue 4 Pages 373-385  
  Keywords  
  Abstract This paper presents a vision-based approach for mobile robot localization. The model of the environment is topological. The new approach characterizes a place using a signature. This signature consists of a constellation of descriptors computed over different types of local affine covariant regions extracted from an omnidirectional image acquired rotating a standard camera with a pan-tilt unit. This type of representation permits a reliable and distinctive environment modelling. Our objectives were to validate the proposed method in indoor environments and, also, to find out if the combination of complementary local feature region detectors improves the localization versus using a single region detector. Our experimental results show that if false matches are effectively rejected, the combination of different covariant affine region detectors increases notably the performance of the approach by combining the different strengths of the individual detectors. In order to reduce the localization time, two strategies are evaluated: re-ranking the map nodes using a global similarity measure and using standard perspective view field of 45°.
In order to systematically test topological localization methods, another contribution proposed in this work is a novel method to see the degradation in localization performance as the robot moves away from the point where the original signature was acquired. This allows to know the robustness of the proposed signature. In order for this to be effective, it must be done in several, variated, environments that test all the possible situations in which the robot may have to perform localization.
 
  Address  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0929-5593 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RTA2009 Serial 1245  
Permanent link to this record
 

 
Author Jaume Amores edit   pdf
doi  openurl
  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.  
  Address  
  Corporate Author Thesis  
  Publisher Springer London Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  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 David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados edit  doi
openurl 
  Title A Study of Bag-of-Visual-Words Representations for Handwritten Keyword Spotting Type Journal Article
  Year 2015 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 18 Issue 3 Pages 223-234  
  Keywords Bag-of-Visual-Words; Keyword spotting; Handwritten documents; Performance evaluation  
  Abstract The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1433-2833 ISBN Medium  
  Area Expedition Conference  
  Notes DAG; ADAS; 600.055; 600.061; 601.223; 600.077; 600.097 Approved no  
  Call Number Admin @ si @ ART2015 Serial 2679  
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Author Lluis Pere de las Heras; Oriol Ramos Terrades; Sergi Robles; Gemma Sanchez edit  doi
openurl 
  Title CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool Type Journal Article
  Year 2015 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 18 Issue 1 Pages 15-30  
  Keywords  
  Abstract Recent results on structured learning methods have shown the impact of structural information in a wide range of pattern recognition tasks. In the field of document image analysis, there is a long experience on structural methods for the analysis and information extraction of multiple types of documents. Yet, the lack of conveniently annotated and free access databases has not benefited the progress in some areas such as technical drawing understanding. In this paper, we present a floor plan database, named CVC-FP, that is annotated for the architectural objects and their structural relations. To construct this database, we have implemented a groundtruthing tool, the SGT tool, that allows to make specific this sort of information in a natural manner. This tool has been made for general purpose groundtruthing: It allows to define own object classes and properties, multiple labeling options are possible, grants the cooperative work, and provides user and version control. We finally have collected some of the recent work on floor plan interpretation and present a quantitative benchmark for this database. Both CVC-FP database and the SGT tool are freely released to the research community to ease comparisons between methods and boost reproducible research.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1433-2833 ISBN Medium  
  Area Expedition Conference  
  Notes DAG; ADAS; 600.061; 600.076; 600.077 Approved no  
  Call Number Admin @ si @ HRR2015 Serial 2567  
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Author Lluis Pere de las Heras; Ahmed Sheraz; Marcus Liwicki; Ernest Valveny; Gemma Sanchez edit   pdf
doi  openurl
  Title Statistical Segmentation and Structural Recognition for Floor Plan Interpretation Type Journal Article
  Year 2014 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 17 Issue 3 Pages 221-237  
  Keywords  
  Abstract A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1433-2833 ISBN Medium  
  Area Expedition Conference  
  Notes DAG; ADAS; 600.076; 600.077 Approved no  
  Call Number HSL2014 Serial 2370  
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Author Carme Julia; Felipe Lumbreras; Angel Sappa edit  doi
openurl 
  Title A Factorization-based Approach to Photometric Stereo Type Journal Article
  Year 2011 Publication International Journal of Imaging Systems and Technology Abbreviated Journal IJIST  
  Volume 21 Issue 1 Pages 115-119  
  Keywords  
  Abstract This article presents an adaptation of a factorization technique to tackle the photometric stereo problem. That is to recover the surface normals and reflectance of an object from a set of images obtained under different lighting conditions. The main contribution of the proposed approach is to consider pixels in shadow and saturated regions as missing data, in order to reduce their influence to the result. Concretely, an adapted Alternation technique is used to deal with missing data. Experimental results considering both synthetic and real images show the viability of the proposed factorization-based strategy. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 115–119, 2011.  
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  Notes ADAS Approved no  
  Call Number Admin @ si @ JLS2011; ADAS @ adas @ Serial 1711  
Permanent link to this record
 

 
Author Felipe Lumbreras; Joan Serrat edit  openurl
  Title Segmentation of petrographical images of marbles Type Journal Article
  Year 1996 Publication Computers and Geosciences. 22(5):547–558 Abbreviated Journal  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ LuS1996b Serial 82  
Permanent link to this record
 

 
Author J. Pladellorens; Joan Serrat; A. Castell; M.J. Yzuel edit  openurl
  Title Using mathematical morphology to determine left ventricular contours. Type Journal
  Year 1993 Publication Physics in Medicine and Biology. Abbreviated Journal  
  Volume 37 Issue Pages 1877––1894  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ PSC1993 Serial 146  
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Author J. Pladellorens; M.J. Yzuel; J. Castell; Joan Serrat edit  openurl
  Title Calculo automatico del volumen del ventriculo izquierdo. Comparacion con expertos. Type Journal
  Year 1993 Publication Optica Pura y Aplicada. Abbreviated Journal  
  Volume 26 Issue 3 Pages 685–691  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ PYC1993 Serial 149  
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