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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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AERFAI |
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IAM @ iam @ SLM1997 |
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1649 |
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Gemma Sanchez; Ernest Valveny; Josep Llados; Enric Marti; Oriol Ramos Terrades; N.Lozano; Joan Mas |
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A system for virtual prototyping of architectural projects |
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2003 |
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Proceedings of Fifth IAPR International Workshop on Pattern Recognition |
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65-74 |
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IAM @ iam @ SVL2003 |
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1650 |
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Joan Serrat; Enric Marti |
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Title |
Elastic matching using interpolation splines |
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1991 |
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IV Spanish Symposium of Pattern Recognition and image Analysis |
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ADAS;IAM; |
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IAM @ iam @ SMV1991 |
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1651 |
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Ernest Valveny; Enric Marti |
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Title |
Learning of structural descriptions of graphic symbols using deformable template matching |
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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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Ernest Valveny; Enric Marti |
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Application of deformable template matching to symbol recognition in hand-written architectural draw |
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1999 |
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Proceedings of the Fifth International Conference on |
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We propose to use deformable template matching as a new approach to recognize characters and lineal symbols in hand-written line drawings, instead of traditional methods based on vectorization and feature extraction. Bayesian formulation of the deformable template matching allows combining fidelity to the ideal shape of the symbol with maximum flexibility to get the best fit to the input image. Lineal nature of symbols can be exploited to define a suitable representation of models and the set of deformations to be applied to them. Matching, however, is done over the original binary image to avoid losing relevant features during vectorization. We have applied this method to hand-written architectural drawings and experimental results demonstrate that symbols with high distortions from ideal shape can be accurately identified. |
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Bangalore (India) |
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DAG;IAM; |
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IAM @ iam @ VAM1999a |
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1657 |
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Author |
Ernest Valveny; Enric Marti |
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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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IAM @ iam @ VAM1999 |
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1658 |
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Ernest Valveny; Enric Marti |
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Dimensions analysis in hand-drawn architectural drawings |
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1997 |
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VII National Simposium of Pattern Recognition and image Analysis, SNRFAI´97 |
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90-91 |
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CVC-UAB |
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DAG;IAM; |
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IAM @ iam @ VAM1997 |
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1659 |
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Ernest Valveny; Ricardo Toledo; Ramon Baldrich; Enric Marti |
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Combining recognition-based in segmentation-based approaches for graphic symol recognition using deformable template matching |
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2002 |
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Proceeding of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002 |
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502–507 |
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DAG;RV;CAT;IAM;CIC;ADAS |
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IAM @ iam @ VTB2002 |
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1660 |
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Author |
David Roche; Debora Gil; Jesus Giraldo |
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An inference model for analyzing termination conditions of Evolutionary Algorithms |
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2011 |
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14th Congrès Català en Intel·ligencia Artificial |
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216-225 |
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Evolutionary Computation Convergence, Termination Conditions, Statistical Inference |
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In real-world problems, it is mandatory to design a termination condition for Evolutionary Algorithms (EAs) ensuring stabilization close to the unknown optimum. Distribution-based quantities are good candidates as far as suitable parameters are used. A main limitation for application to real-world problems is that such parameters strongly depend on the topology of the objective function, as well as, the EA paradigm used.
We claim that the termination problem would be fully solved if we had a model measuring to what extent a distribution-based quantity asymptotically behaves like the solution accuracy. We present a regression-prediction model that relates any two given quantities and reports if they can be statistically swapped as termination conditions. Our framework is applied to two issues. First, exploring if the parameters involved in the computation of distribution-based quantities influence their asymptotic behavior. Second, to what extent existing distribution-based quantities can be asymptotically exchanged for the accuracy of the EA solution. |
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Lleida, Catalonia (Spain) |
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Associació Catalana Intel·ligència Artificial |
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978-1-60750-841-0 |
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CCIA |
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IAM @ iam @ RGG2011a |
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1677 |
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David Roche; Debora Gil; Jesus Giraldo |
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Using statistical inference for designing termination conditions ensuring convergence of Evolutionary Algorithms |
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2011 |
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11th European Conference on Artificial Life |
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A main challenge in Evolutionary Algorithms (EAs) is determining a termination condition ensuring stabilization close to the optimum in real-world applications. Although for known test functions distribution-based quantities are good candidates (as far as suitable parameters are used), in real-world problems an open question still remains unsolved. How can we estimate an upper-bound for the termination condition value ensuring a given accuracy for the (unknown) EA solution?
We claim that the termination problem would be fully solved if we defined a quantity (depending only on the EA output) behaving like the solution accuracy. The open question would be, then, satisfactorily answered if we had a model relating both quantities, since accuracy could be predicted from the alternative quantity. We present a statistical inference framework addressing two topics: checking the correlation between the two quantities and defining a regression model for predicting (at a given confidence level) accuracy values from the EA output. |
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Paris, France |
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ECAL |
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IAM @ iam @ RGG2011b |
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1678 |
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