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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Josep Llados; Marçal Rusiñol |
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Title |
Graphics Recognition Techniques |
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Book Chapter |
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2014 |
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Handbook of Document Image Processing and Recognition |
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D |
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489-521 |
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Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation |
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This chapter describes the most relevant approaches for the analysis of graphical documents. The graphics recognition pipeline can be splitted into three tasks. The low level or lexical task extracts the basic units composing the document. The syntactic level is focused on the structure, i.e., how graphical entities are constructed, and involves the location and classification of the symbols present in the document. The third level is a functional or semantic level, i.e., it models what the graphical symbols do and what they mean in the context where they appear. This chapter covers the lexical level, while the next two chapters are devoted to the syntactic and semantic level, respectively. The main problems reviewed in this chapter are raster-to-vector conversion (vectorization algorithms) and the separation of text and graphics components. The research and industrial communities have provided standard methods achieving reasonable performance levels. Hence, graphics recognition techniques can be considered to be in a mature state from a scientific point of view. Additionally this chapter provides insights on some related problems, namely, the extraction and recognition of dimensions in engineering drawings, and the recognition of hatched and tiled patterns. Both problems are usually associated, even integrated, in the vectorization process. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-858-4 |
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DAG; 600.077 |
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Admin @ si @ LlR2014 |
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2380 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Josep Llados; Marçal Rusiñol; Alicia Fornes; David Fernandez; Anjan Dutta |
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Title |
On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents |
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2012 |
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International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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26 |
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5 |
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1263002-126027 |
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Handwriting recognition; word spotting; historical documents; feature representation; shape descriptors Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025 |
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0,624 JCR
Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images. |
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Admin @ si @ LRF2012 |
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2128 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Josep Llados; Partha Pratim Roy; Jose Antonio Rodriguez; Gemma Sanchez |
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Title |
Word Spotting in Archive Documents using Shape Contexts |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4478:290–297 |
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Girona (Spain) |
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DAG @ dag @ LRR2007 |
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779 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Josep Llados; W. Liu; Jean-Marc Ogier |
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Title |
Seventh IAPR International Workshop on Graphics Recognition GREC 2007 |
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2007 |
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Curitiba (Brazil) |
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DAG @ dag @ LLO2007 |
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835 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Josep Llados; Young-Bin Kwon |
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Title |
Graphics Recognition. Recent Advances and Perspectives |
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Miscellaneous |
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2004 |
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LNCS 3080, ISBN: 3–540–22478–5 |
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Springer-Verlag |
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DAG @ dag @ LlK2004 |
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515 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Josep Llados;Horst Bunke; Enric Marti |
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Title |
Using Cyclic String Matching to Find Rotational and Reflectional Symmetries in Shapes |
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1997 |
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Intelligent Robots: Sensing, Modeling and Planning |
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164-179 |
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Dagstuhl Workshop |
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World Scientific Press |
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9810231857 |
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DAG;IAM; |
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IAM @ iam @ LBM1997b |
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1563 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Josep M. Gonfaus |
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Title |
Semantic Segmentation of Images Using Random Ferns |
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Report |
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2009 |
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CVC Technical Report |
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132 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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Admin @ si @ Gon2009 |
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2391 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Josep M. Gonfaus |
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Title |
Towards Deep Image Understanding: From pixels to semantics |
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2012 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Understanding the content of the images is one of the greatest challenges of computer vision. Recognition of objects appearing in images, identifying and interpreting their actions are the main purposes of Image Understanding. This thesis seeks to identify what is present in a picture by categorizing and locating all the objects in the scene.
Images are composed by pixels, and one possibility consists of assigning to each pixel an object category, which is commonly known as semantic segmentation. By incorporating information as a contextual cue, we are able to resolve the ambiguity within categories at the pixel-level. We propose three levels of scale in order to resolve such ambiguity.
Another possibility to represent the objects is the object detection task. In this case, the aim is to recognize and localize the whole object by accurately placing a bounding box around it. We present two new approaches. The first one is focused on improving the object representation of deformable part models with the concept of factorized appearances. The second approach addresses the issue of reducing the computational cost for multi-class recognition. The results given have been validated on several commonly used datasets, reaching international recognition and state-of-the-art within the field |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Jordi Gonzalez;Theo Gevers |
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Admin @ si @ Gon2012 |
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2208 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Josep M. Gonfaus; Marco Pedersoli; Jordi Gonzalez; Andrea Vedaldi; Xavier Roca |
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Title |
Factorized appearances for object detection |
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2015 |
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Computer Vision and Image Understanding |
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CVIU |
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138 |
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92–101 |
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Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts |
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Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.
A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure.
Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories. |
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ISE; 600.063; 600.078 |
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Admin @ si @ GPG2015 |
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2705 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Josep M. Gonfaus; Theo Gevers; Arjan Gijsenij; Xavier Roca; Jordi Gonzalez |
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Title |
Edge Classification using Photo-Geo metric features |
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Conference Article |
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2012 |
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21st International Conference on Pattern Recognition |
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1497 - 1500 |
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Edges are caused by several imaging cues such as shadow, material and illumination transitions. Classification methods have been proposed which are solely based on photometric information, ignoring geometry to classify the physical nature of edges in images. In this paper, the aim is to present a novel strategy to handle both photometric and geometric information for edge classification. Photometric information is obtained through the use of quasi-invariants while geometric information is derived from the orientation and contrast of edges. Different combination frameworks are compared with a new principled approach that captures both information into the same descriptor. From large scale experiments on different datasets, it is shown that, in addition to photometric information, the geometry of edges is an important visual cue to distinguish between different edge types. It is concluded that by combining both cues the performance improves by more than 7% for shadows and highlights. |
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1051-4651 |
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978-1-4673-2216-4 |
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ICPR |
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Admin @ si @ GGG2012b |
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2142 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
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Title |
Harmony Potentials for Joint Classification and Segmentation |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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3280–3287 |
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Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21. |
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San Francisco CA, USA |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS;CIC;ISE |
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ADAS @ adas @ GBW2010 |
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1296 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa |
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Title |
Fine-tuning based deep convolutional networks for lepidopterous genus recognition |
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2016 |
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21st Ibero American Congress on Pattern Recognition |
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467-475 |
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This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%. |
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Lima; Perú; November 2016 |
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CIARP |
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ADAS; 600.086 |
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Admin @ si @ CRS2016 |
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2913 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Juan Andrade; A. Sanfeliu |
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The effects of partial observability when building fully correlated maps |
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2005 |
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IEEE Transactions on Robotics, 21(4):771–777 (IF: 1.486) |
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Juan Andrade; F. Thomas |
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Wire-Based Tracking using Mutual Information |
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Miscellaneous |
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2006 |
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10th International Symposium on Advances in Robot Kinematics, 3–14 |
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Ljubljana (Slovenia) |
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Admin @ si @ AnT2006 |
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665 |
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Juan Andrade; T. Alejandra Vidal; A. Sanfeliu |
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Title |
Multirobot C-SLAM: Simultaneous localization, control, and mapping |
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Miscellaneous |
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2005 |
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in Proc. IEEE ICRA05 Workshop on Network Robot Systems |
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Barcelona (Spain) |
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no |
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Admin @ si @ AVS2005b |
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549 |
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