Debora Gil, & Petia Radeva. (2003). Curvature Vector Flow to Assure Convergent Deformable Models for Shape Modelling. In B. Springer (Ed.), Energy Minimization Methods In Computer Vision And Pattern Recognition (Vol. 2683, pp. 357–372). LNCS. Lisbon, PORTUGAL: Springer, Berlin.
Abstract: Poor convergence to concave shapes is a main limitation of snakes as a standard segmentation and shape modelling technique. The gradient of the external energy of the snake represents a force that pushes the snake into concave regions, as its internal energy increases when new inexion points are created. In spite of the improvement of the external energy by the gradient vector ow technique, highly non convex shapes can not be obtained, yet. In the present paper, we develop a new external energy based on the geometry of the curve to be modelled. By tracking back the deformation of a curve that evolves by minimum curvature ow, we construct a distance map that encapsulates the natural way of adapting to non convex shapes. The gradient of this map, which we call curvature vector ow (CVF), is capable of attracting a snake towards any contour, whatever its geometry. Our experiments show that, any initial snake condition converges to the curve to be modelled in optimal time.
Keywords: Initial condition; Convex shape; Non convex analysis; Increase; Segmentation; Gradient; Standard; Standards; Concave shape; Flow models; Tracking; Edge detection; Curvature
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Debora Gil, Petia Radeva, & Fernando Vilariño. (2003). Anisotropic Contour Completion. In Proceedings of the IEEE International Conference on Image Processing. Barcelona, Spain.
Abstract: In this paper we introduce a novel application of the diffusion tensor for anisotropic image processing. The Anisotropic Contour Completion (ACC) we suggest consists in extending the characteristic function of the open curve by means of a degenerated diffusion tensor that prevents any diffusion in the normal direction. We show that ACC is equivalent to a dilation with a continuous elliptic structural element that takes into account the local orientation of the contours to be closed. Experiments on contours extracted from real images show that ACC produces shapes able to adapt to any curve in an active contour framework. 1.
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Josep Llados, Ernest Valveny, Gemma Sanchez, & Enric Marti. (2003). A Case Study of Pattern Recognition: Symbol Recognition in Graphic Documentsa. In Proceedings of Pattern Recognition in Information Systems (pp. 1–13). ICEIS Press.
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Oriol Rodriguez-Leon, Josefina Mauri, Eduard Fernandez-Nofrerias, C.Garcia, R.Villuendas, Vicente del Valle, et al. (2003). Reconstruction of a spatio-temporal model of the intima layer from intravascular ultrasound sequences. European Heart Journal, .
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David Rotger, Misael Rosales, Jaume Garcia, Oriol Pujol, Josefina Mauri, & Petia Radeva. (2003). Active Vessel: A New Multimedia Workstation for Intravascular Ultrasound and Angiography Fusion. Computers in Cardiology, 30, 65–68.
Abstract: AcriveVessel is a new multimedia workstation which enables the visualization, acquisition and handling of both image modalities, on- and ofline. It enables DICOM v3.0 decompression and browsing, video acquisition,repmduction and storage for IntraVascular UltraSound (IVUS) and angiograms with their corresponding ECG,automatic catheter segmentation in angiography images (using fast marching algorithm). BSpline models definition for vessel layers on IVUS images sequence and an extensively validated tool to fuse information. This approach defines the correspondence of every IVUS image with its correspondent point in the angiogram and viceversa. The 3 0 reconstruction of the NUS catheterhessel enables real distance measurements as well as threedimensional visualization showing vessel tortuosity in the space.
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Gemma Sanchez, Ernest Valveny, Josep Llados, Enric Marti, Oriol Ramos Terrades, N.Lozano, et al. (2003). A system for virtual prototyping of architectural projects. In Proceedings of Fifth IAPR International Workshop on Pattern Recognition (pp. 65–74).
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Ernest Valveny, & Enric Marti. (2003). A model for image generation and symbol recognition through the deformation of lineal shapes. PRL - Pattern Recognition Letters, 24(15), 2857–2867.
Abstract: We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents.
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Lluis Barcelo, & X. Binefa. (2002). Bayesian Video Mosaicing with moving objects. International Journal of Pattern Recognition and Artificial Intelligence, 16(3): 341–348 (IF: 0.359).
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Cristina Cañero, Fernando Vilariño, & Petia Radeva. (2002). Predictive (un) distortion model and 3D Reconstruction by Biplane Snakes. IEEE Transactions on Medical Imaging (IF: 2.911).
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J. Martinez. (2002). Automotive sector and Machine Vision..
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J. Martinez, & F. Thomas. (2002). Efficient Computation of Local Geometric Moments. IEEE Transactions on Image Porcessing, 11(9): 1102–1111 (IF: 2.553).
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Xavier Otazu, M. Ribo, M. Peracaula, J.M. Paredes, & J. Nuñez. (2002). Detection of superimposed periodic signals using wavelets. Monthly Notices of the Royal Astronomical Society, 333, 2: 365–372 (IF: 4.671).
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A. Pujol, & Juan J. Villanueva. (2002). A supervised Modification of the Hausdorff distance for visual shape classification. International Journal of Pattern Recognition and Artificial Intelligence, 349–359.
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J.M. Sanchez, X. Binefa, & Jordi Vitria. (2002). Shot Partitioning Based Recognition of Tv Commercials. Multimedia Tools and Applications, 18: 233–247, Kluwer Academic Publishers (IF: 0.421).
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Gemma Sanchez, Josep Llados, & K. Tombre. (2002). A mean string algorithm to compute the average among a set of 2D shapes. PRL - Pattern Recognition Letters, 23(1-3), 203–214.
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