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Petia Radeva, & Jordi Vitria. (2003). “Inteligencia artificial” Centre de Visio per Computador.
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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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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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Judit Martinez, Eva Costa, P. Herreros, Antonio Lopez, & Juan J. Villanueva. (2003). TV-Screen Quality Inspection by Artificial Vision. In Proceedings SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision (QCAV 2003).
Abstract: A real-time vision system for TV screen quality inspection is introduced. The whole system consists of eight cameras and one processor per camera. It acquires and processes 112 images in 6 seconds. The defects to be inspected can be grouped into four main categories (bubble, line-out, line reduction and landing) although there exists a large variability among each particular type of defect. The complexity of the whole inspection process has been reduced by dividing images into smaller ones and grouping the defects into frequency and intensity relevant ones. Tools such as mathematical morphology, Fourier transform, profile analysis and classification have been used. The performance of the system has been successfully proved against human operators in normal production conditions.
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Daniel Ponsa, Robert Benavente, Felipe Lumbreras, Judit Martinez, & Xavier Roca. (2003). Quality control of safety belts by machine vision inspection for real-time production. Optical Engineering (IF: 0.877), 42(4), 1114–1120.
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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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Judit Martinez, Eva Costa, P. Herreros, F. Javier Sanchez, & Ramon Baldrich. (2003). A Modular and Scalable Architecture for PC-Based Real-Time Vision Systems. Real–Time Imaging, (IF: 0.512), 9, 99–112.
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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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M. Bressan, & Jordi Vitria. (2003). Nonparametric Discriminant Analysis and Nearest Neighbor Classification. PRL - Pattern Recognition Letters, 24(15), 2743–2749.
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David Guillamet, Jordi Vitria, & B. Shiele. (2003). Introducing a weighted non-negative matrix factorization for image classification. PRL - Pattern Recognition Letters, 24(14), 2447–2454.
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David Guillamet, & Jordi Vitria. (2003). Evaluation of distance metrics for recognition based on non-negative matrix factorization. PRL - Pattern Recognition Letters, 24(9-10), 1599 –1605.
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Cristina Cañero, & Petia Radeva. (2003). Vesselness enhancement diffusion. PRL - Pattern Recognition Letters, 24(16), 3141–3151.
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Javier Varona, Jordi Gonzalez, Xavier Roca, & Juan J. Villanueva. (2003). Appearance Tracking for Video Surveillance.
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Jordi Gonzalez, Javier Varona, Xavier Roca, & Juan J. Villanueva. (2003). Automatic Keyframing of Human Actions for Computer Animation.
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Jordi Gonzalez, Javier Varona, Xavier Roca, & Juan J. Villanueva. (2003). A Human Action Comparison Framework for Motion Understanding.
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