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Author |
Fernando Vilariño; Enric Marti |
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New didactic techniques in the EHES applying mobile technologies |
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2008 |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestió d’Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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MILAB;IAM;MV;SIAI |
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IAM @ iam @ VIM2008 |
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1664 |
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Author |
Enric Marti; Carme Julia; Debora Gil |
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PBL en la docencia de gráficos por computador |
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Miscellaneous |
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2007 |
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VII Jornadas de Aprendizaje Cooperativo (JAC07) |
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1 |
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53-62 |
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IAM;ADAS; |
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IAM @ iam @ MJG2007b |
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1604 |
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Enric Marti; J.Roncaries; Debora Gil; Aura Hernandez-Sabate; Antoni Gurgui; Ferran Poveda |
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Title |
PBL On Line: A proposal for the organization, part-time monitoring and assessment of PBL group activities |
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2015 |
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Journal of Technology and Science Education |
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JOTSE |
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5 |
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2 |
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87-96 |
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IAM; ADAS; 600.076; 600.075 |
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Admin @ si @ MRG2015 |
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2608 |
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Author |
Josep Llados; Enric Marti |
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Playing with error-tolerant subgraph isomorphism in line drawings |
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1997 |
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VII National Symposium on Pattern Recognition and image Analysis |
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DAG;IAM; |
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IAM @ iam @ LlM1997 |
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1570 |
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Enric Marti |
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Project Based Learning in engineering |
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2008 |
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Simposium on Problem Based learning (PBL) |
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Lleida |
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IAM @ iam @ MAR2008a |
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1586 |
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Ernest Valveny; Enric Marti |
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Title |
Recognition of lineal symbols in hand-written drawings using deformable template matching |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes |
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DAG;IAM; |
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IAM @ iam @ VAM1999 |
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1658 |
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Author |
Enric Marti; Jordi Vitria; Alberto Sanfeliu |
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Title |
Reconocimiento de Formas y Análisis de Imágenes |
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Book Whole |
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1998 |
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Asociación Española de Reconocimientos de Formas y Análisis de Imágenes |
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Los sistemas actuales de reconocimiento automático del lenguaje oral se basan en dos etapas básicas de procesado: la parametrización, que extrae la evolución temporal de los parámetros que caracterizan la voz, y el reconocimiento propiamente dicho, que identifica la cadena de palabras de la elocución recibida con ayuda de los modelos que representan el conocimiento adquirido en la etapa de aprendizaje. Tomando como línea divisoria la palabra, dichos modelos son de tipo acústicofonético o gramatical. Los primeros caracterizan las palabras incluidas en el vocabulario de la aplicación o tarea a la que está orientado el sistema de reconocimiento, usando a menudo para ello modelos de unidades de habla de extensión inferior a la palabra, es decir, de unidades subléxicas. Por otro lado, la gramática incluye el conocimiento acerca de las combinaciones permitidas de palabras para formar las frases o su probabilidad. Queda fuera del esquema la denominada comprensión del habla, que utiliza adicionalmente el conocimiento semántico y pragmático para captar el significado de la elocución de entrada al sistema a partir de la cadena (o cadenas alternativas) de palabras que suministra el reconocedor. |
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AERFAI |
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84–922529–4–4 |
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IAM;OR;MV |
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IAM @ iam @ MVS1998 |
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1620 |
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Author |
Gemma Sanchez; Josep Llados; Enric Marti |
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Title |
Segmentation and analysis of linial texture in plans |
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1997 |
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Intelligence Artificielle et Complexité. |
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Structural Texture, Voronoi, Hierarchical Clustering, String Matching. |
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The problem of texture segmentation and interpretation is one of the main concerns in the field of document analysis. Graphical documents often contain areas characterized by a structural texture whose recognition allows both the document understanding, and its storage in a more compact way. In this work, we focus on structural linial textures of regular repetition contained in plan documents. Starting from an atributed graph which represents the vectorized input image, we develop a method to segment textured areas and recognize their placement rules. We wish to emphasize that the searched textures do not follow a predefined pattern. Minimal closed loops of the input graph are computed, and then hierarchically clustered. In this hierarchical clustering, a distance function between two closed loops is defined in terms of their areas difference and boundary resemblance computed by a string matching procedure. Finally it is noted that, when the texture consists of isolated primitive elements, the same method can be used after computing a Voronoi Tesselation of the input graph. |
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Paris, France |
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Paris |
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AERFAI |
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DAG;IAM; |
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IAM @ iam @ SLM1997 |
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1649 |
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Author |
Josep Llados; Horst Bunke; Enric Marti |
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Structural Recognition of hand drawn floor plans |
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1996 |
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VI National Symposium on Pattern Recognition and Image Analysis |
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Rotational Symmetry; Reflectional Symmetry; String Matching. |
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A system to recognize hand drawn architectural drawings in a CAD environment has been deve- loped. In this paper we focus on its high level interpretation module. To interpret a floor plan, the system must identify several building elements, whose description is stored in a library of pat- terns, as well as their spatial relationships. We propose a structural approach based on subgraph isomorphism techniques to obtain a high-level interpretation of the document. The vectorized input document and the patterns to be recognized are represented by attributed graphs. Discrete relaxation techniques (AC4 algorithm) have been applied to develop the matching algorithm. The process has been divided in three steps: node labeling, local consistency and global consistency verification. The hand drawn creation causes disturbed line drawings with several accuracy errors, which must be taken into account. Here we have identified them and the AC4 algorithm has been adapted to manage them. |
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Cordoba |
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IAM @ iam @ LIM1995 |
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1565 |
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Author |
Josep Llados; Enric Marti; Juan J.Villanueva |
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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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IAM @ iam @ LMV2001 |
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1581 |
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