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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 |
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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Actes de la conférence 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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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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DAG;IAM |
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IAM @ iam @ SVL2003 |
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1650 |
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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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Hand-drawn symbol recognition in graphic documents using deformable template matching and a Bayesian framework |
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2000 |
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Proc. 15th Int Pattern Recognition Conf |
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2 |
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239-242 |
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Hand-drawn symbols can take many different and distorted shapes from their ideal representation. Then, very flexible methods are needed to be able to handle unconstrained drawings. We propose here to extend our previous work in hand-drawn symbol recognition based on a Bayesian framework and deformable template matching. This approach gets flexibility enough to fit distorted shapes in the drawing while keeping fidelity to the ideal shape of the symbol. In this work, we define the similarity measure between an image and a symbol based on the distance from every pixel in the image to the lines in the symbol. Matching is carried out using an implementation of the EM algorithm. Thus, we can improve recognition rates and computation time with respect to our previous formulation based on a simulated annealing algorithm. |
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0-7695-0750-6 |
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DAG;IAM; |
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IAM @ iam @ VAM2000 |
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1656 |
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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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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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(SNRFAI’97) 7th Spanish National Symposium on Pattern Recognition and Image Analysis |
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90-91 |
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CVC-UAB |
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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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Palaiahnakote Shivakumara; Anjan Dutta; Trung Quy Phan; Chew Lim Tan; Umapada Pal |
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A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification in Video |
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2011 |
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Pattern Recognition |
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PR |
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44 |
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8 |
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1671-1683 |
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In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels. |
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Admin @ si @ SDP2011 |
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1727 |
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