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I. King, & Zhong Jin. (2003). Integrated Probability Function and Its Application to Content-Based Image Retrieval By Relevance Feedback. Pattern Recognition, 36(9): 2177–2186 (IF: 1.611).
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X. Jing, David Zhang, & Zhong Jin. (2003). Improvements on the uncorrelated optimal discriminant vectors. Pattern Recognition, 36(8): 1921–1923 (IF: 1.611).
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X. Jing, David Zhang, & Zhong Jin. (2003). Improved algorithm and generalized theory. Pattern Recognition, 36(11): 2593–2602 (IF: 1.611).
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Yong Xu, Jing-Yu Yang, & Zhong Jin. (2003). Theory analysis on FSLDA and ULDA. Pattern Recognition, 36(12): 3031–3033 (IF: 1.611).
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Zhong Jin, Franck Davoine, & Zhen Lou. (2003). Facial expression analysis by using KPCA.
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Oriol Pujol, & Petia Radeva. (2003). Texture Segmentation by Statistic Deformable Models. International Journal of Image and Graphics (IJIG).
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Oriol Ramos Terrades. (2003). Descripcio i classificacio de simbols tecnics usant la transformada de crestetes.
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David Alcalde. (2003). Image classification in terms of rotation-invariant pattern matching.
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David Masip. (2003). Dimensionality reduction techniques applied to nearest neighbor classification.
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Misael Rosales, & Petia Radeva. (2003). Empirical simulation model of intravascular ultrasound.
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J.L. Pech-Pacheco, J. Alvarez-Borrego, Gabriel Cristobal, & Matthias S. Keil. (2003). Automatic object identification irrespective to geometric changes. Optical Engineering, 42(2): 551–559 (IF: 0.877).
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Jaume Garcia, David Rotger, Francesc Carreras, R.Leta, & Petia Radeva. (2003). Contrast echography segmentation and tracking by trained deformable models. In Proc. Computers in Cardiology (Vol. 30, pp. 173–176). Centre de Visió per Computador – Dept. Informàtica, UAB Edifici O – Campus UAB, 08193 Bellater.
Abstract: The objective of this work is to segment the human left ventricle myocardium (LVM) in contrast echocardiography imaging and thus track it along a cardiac cycle in order to extract quantitative data about heart function. Ultrasound images are hard to work with due to their speckle appearance. To overcome this we report the combination of active contour models (ACM) or snakes and active shape models (ASM). The ability of ACM in giving closed and smooth curves in addition to the power of the ASM in producing shapes similar to the ones learned, evoke to a robust algorithm. Meanwhile the snake is attracted towards image main features, ASM acts as a correction factor. The algorithm was tested independently on 180 frames and satisfying results were obtained: in 95% the maximum difference between automatic and experts segmentation was less than 12 pixels.
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Debora Gil, & Petia Radeva. (2003). Curvature based Distance Maps. Computer Vision Center.
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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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