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M. Torre, & Petia Radeva. (2000). Agricultural-Field Extraction on Aerial Images by Region Competition Algorithm..
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Robert Benavente, Gemma Sanchez, Ramon Baldrich, Maria Vanrell, & Josep Llados. (2000). Normalized colour segmentation for human appearance description..
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X. Orriols, Ricardo Toledo, X. Binefa, Petia Radeva, Jordi Vitria, & Juan J. Villanueva. (2000). Probabilistic Saliency Approach for Elongated Structure Detection using Deformable Models..
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C. Molina, & J.B. Subirana. (1995). Reduction of complexity for object recognition algorithms.
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J. Garcia, J.M. Sanchez, X. Orriols, & X. Binefa. (2000). Chromatic aberration and depth extraction..
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A. Pujol, Juan J. Villanueva, & H. Wechsler. (2000). Automatic View Based Caricaturing..
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X. Varona, Jordi Gonzalez, Xavier Roca, & Juan J. Villanueva. (2000). iTrack: Image-based Probabilistic Tracking of People..
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David Guillamet, & Jordi Vitria. (2000). A Comparison of Local versus Global Color Histograms for Object Recognition..
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V. Valev, B. Sankur, & Petia Radeva. (2000). Generalized Non Reducible Descriptors. 15 th International Conference on Pattern Recognition, 2: 394–397., .
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C. Molina, & J.B. Subirana. (1995). Polynomial-Time Algorithm for 2D object recognition..
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J.R. Serra, S. Casadei, & J.B. Subirana. (1995). Non-Cartesian Networks for Middle Level Vision..
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D. Lloret, Joan Serrat, Antonio Lopez, A. Soler, & Juan J. Villanueva. (2000). Retinal image registration using creases as anatomical landmarks..
Abstract: Retinal images are routinely used in ophthalmology to study the optical nerve head and the retina. To assess objectively the evolution of an illness, images taken at different times must be registered. Most methods so far have been designed specifically for a single image modality, like temporal series or stereo pairs of angiographies, fluorescein angiographies or scanning laser ophthalmoscope (SLO) images, which makes them prone to fail when conditions vary. In contrast, the method we propose has shown to be accurate and reliable on all the former modalities. It has been adapted from the 3D registration of CT and MR image to 2D. Relevant features (also known as landmarks) are extracted by means of a robust creaseness operator, and resulting images are iteratively transformed until a maximum in their correlation is achieved. Our method has succeeded in more than 100 pairs tried so far, in all cases including also the scaling as a parameter to be optimized
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Xose M. Pardo, & Petia Radeva. (2000). Discriminant snakes for 3D reconstruction in medical Images..
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Ricardo Toledo, X. Orriols, Petia Radeva, X. Binefa, Jordi Vitria, Cristina Cañero, et al. (2000). Eigensnakes for vessel segmentation in angiography..
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