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Author |
Josep Llados; Enric Marti; Juan J.Villanueva |
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Title |
Symbol recognition by error-tolerant subgraph matching between region adjacency graphs |
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2001 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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23 |
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10 |
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1137-1143 |
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The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content. |
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DAG;IAM;ISE; |
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IAM @ iam @ LMV2001 |
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1581 |
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Author |
Ernest Valveny; Enric Marti |
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Title |
Learning of structural descriptions of graphic symbols using deformable template matching |
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Year |
2001 |
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Proc. Sixth Int Document Analysis and Recognition Conf |
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455-459 |
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Accurate symbol recognition in graphic documents needs an accurate representation of the symbols to be recognized. If structural approaches are used for recognition, symbols have to be described in terms of their shape, using structural relationships among extracted features. Unlike statistical pattern recognition, in structural methods, symbols are usually manually defined from expertise knowledge, and not automatically infered from sample images. In this work we explain one approach to learn from examples a representative structural description of a symbol, thus providing better information about shape variability. The description of a symbol is based on a probabilistic model. It consists of a set of lines described by the mean and the variance of line parameters, respectively providing information about the model of the symbol, and its shape variability. The representation of each image in the sample set as a set of lines is achieved using deformable template matching. |
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DAG;IAM; |
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IAM @ iam @ VMA2001 |
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1654 |
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Author |
Gemma Sanchez; Josep Llados; K. Tombre |
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Title |
A mean string algorithm to compute the average among a set of 2D shapes |
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2002 |
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Pattern Recognition Letters |
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PRL |
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23 |
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1-3 |
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203–214 |
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DAG; IF: 0.409 |
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DAG @ dag @ SLT2002 |
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275 |
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Josep Llados; Gemma Sanchez; K. Tombre |
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Title |
An Error-Correction Graph Grammar to Recognize Texture Symbols. |
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Miscellaneous |
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2002 |
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Graphics Recognition: Algorithms and Apllications, LNCS 2390: 128–138, Springer Verlag. |
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Berlin |
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DAG |
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DAG @ dag @ LST2002 |
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281 |
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Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados |
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Title |
Textual Descriptors for browsing people by visual appearence. |
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Conference Article |
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2002 |
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5è. Congrés Català d’Intel·ligència Artificial CCIA |
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Image retrieval, textual descriptors, colour naming, colour normalization, graph matching. |
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This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building. |
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DAG;CIC |
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CAT @ cat @ TBB2002a |
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287 |
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Author |
Ramon Baldrich; Ricardo Toledo; Ernest Valveny; Maria Vanrell |
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Title |
Perceptual Colour Image Segmentation. |
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2002 |
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Proceeding of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002: 145–150. |
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DAG;CIC;ADAS |
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CAT @ cat @ BTV2002 |
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290 |
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Ricardo Toledo; Ramon Baldrich; Ernest Valveny; Petia Radeva |
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Enhancing snakes for vessel detection in angiography images. |
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2002 |
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Proceedings of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002: 139–144. |
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MILAB;DAG;CIC;ADAS |
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BCNPCL @ bcnpcl @ TBV2002 |
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300 |
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Ernest Valveny; B. Lamiroy |
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Title |
Automatic Generation of Browsable Technical Documents. |
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2002 |
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Proceedings of the Sixteenth International Conference on Pattern Recognition ICPR 2002: 188–191. |
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Quebec, Canada |
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DAG @ dag @ VaL2002 |
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301 |
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Author |
Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados |
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Title |
Textual Descriptions for Browsing People by Visual Apperance. |
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2002 |
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Lecture Notes in Artificial Intelligence |
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2504 |
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419-429 |
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This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building |
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Springer Verlag |
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DAG;CIC |
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CAT @ cat @ TBB2002b |
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319 |
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Author |
Josep Llados; Ernest Valveny; Gemma Sanchez; Enric Marti |
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Title |
Symbol recognition: current advances and perspectives |
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2002 |
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Graphics Recognition Algorithms And Applications |
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LNCS |
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2390 |
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104-128 |
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The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content. |
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London, UK |
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Springer-Verlag |
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Dorothea Blostein and Young- Bin Kwon |
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Lecture Notes in Computer Science |
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LNCS |
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3-540-44066-6 |
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GREC |
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DAG; IAM; |
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IAM @ iam @ LVS2002 |
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1572 |
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