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Author |
Petia Radeva; Enric Marti |
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Title |
An improved model of snakes for model-based segmentation |
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1995 |
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Proceedings of Computer Analysis of Images and Patterns |
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515-520 |
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The main advantage of segmentation by snakes consists in its ability to incorporate smoothness constraints on the detected shapes that can occur. Likewise, we propose to model snakes with other properties that reflect the information provided about the object of interest in a different extent. We consider different kinds of snakes, those searching for contours with a certain direction, those preserving an object’s model, those seeking for symmetry, those expanding open, etc. The availability of such a collection of snakes allows not only the more complete use of the knowledge about the segmented object, but also to solve some problems of the existing snakes. Our experiments on segmentation of facial features justify the usefulness of snakes with different properties. |
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CAIP |
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MILAB;IAM |
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IAM @ iam @ RaM1995b |
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1632 |
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Author |
Petia Radeva; Enric Marti |
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Title |
Facial Features Segmentation by Model-Based Snakes |
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1995 |
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International Conference on Computing Analysis and Image Processing |
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Deformable models have recently been accepted as a standard technique to segment different features in facial images. Despite they give a good approximation of the salient features in a facial image, the resulting shapes of the segmentation process seem somewhat artificial with respect to the natural feature shapes. In this paper we show that active contour models (in particular, rubber snakes) give more close and natural representation of the detected feature shape. Besides, using snakes for facial segmentation frees us from the problem of determination of the numerous weigths of deformable models. Another advantage of rubber snakes is their reduced computational cost. Our experiments using rubber snakes for segmentation of facial snapshots have shown a significant improvement compared to deformable models. |
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Bellaterra (Barcelona), Spain |
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MILAB;IAM |
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IAM @ iam @ RAM1995a |
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1633 |
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Author |
Petia Radeva; Joan Serrat; Enric Marti |
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Title |
A snake for model-based segmentation |
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1995 |
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Proc. Conf. Fifth Int Computer Vision |
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816-821 |
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snakes; elastic matching; model-based segmenta tion |
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Despite the promising results of numerous applications, the hitherto proposed snake techniques share some common problems: snake attraction by spurious edge points, snake degeneration (shrinking and attening), convergence and stability of the deformation process, snake initialization and local determination of the parameters of elasticity. We argue here that these problems can be solved only when all the snake aspects are considered. The snakes proposed here implement a new potential eld and external force in order to provide a deformation convergence, attraction by both near and far edges as well as snake behaviour selective according to the edge orientation. Furthermore, we conclude that in the case of model-based seg mentation, the internal force should include structural information about the expected snake shape. Experiments using this kind of snakes for segmenting bones in complex hand radiographs show a signi cant improvement. |
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MILAB;ADAS;IAM |
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IAM @ iam @ RSM1995 |
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1634 |
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Author |
F. Javier Sanchez; Jordi Vitria; Enric Marti |
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Title |
Transformaciones Morfológicas de Polígonos Isotéticos |
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1991 |
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Primer Congreso Español de Informática Gráfica. |
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OR;IAM;MV |
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IAM @ iam @ SVM1991 |
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1648 |
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Gemma Sanchez; Josep Llados; Enric Marti |
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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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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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Author |
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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IAM @ iam @ VAM1999a |
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1657 |
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