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Xavier Otazu, & Maria Vanrell. (2006). Several lightness induction effects with a computational multiresolution wavelet framework. 29th European Conference on Visual Perception (ECVP’06), Perception Suppl s, 32: 56–56.
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J. Nuñez, O. Fors, Xavier Otazu, Vicenç Pala, Roman Arbiol, & M.T. Merino. (2006). A Wavelet-Based Method for the Determination of the Relative Resolution Between Remotely Sensed Images. IEEE Transactions on Geoscience and Remote Sensing, 44(9): 2539–2548.
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Maria Alberich-Carramiñana, Guillem Alenya, Juan Andrade, E. Martinez, & Carme Torras. (2006). Affine Epipolar Direction from Two Views of a Planar Contour. In Proceedings of the Advanced Concepts for Intelligent Vision Systems Conference, LNCS 4179: 944–955.
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Jordi Gonzalez, Dani Rowe, Juan Andrade, & Juan J. Villanueva. (2006). Efficient Management of Multiple Agent Tracking Through Observation Handling.
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Michael Villamizar, A. Sanfeliu, & Juan Andrade. (2006). Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection.
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Michael Villamizar, A. Sanfeliu, & Juan Andrade. (2006). Orientation Invariant Features for Multiclass Object Recognition.
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Juan Andrade, & F. Thomas. (2006). Wire-Based Tracking using Mutual Information.
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T. Alejandra Vidal, Andrew J. Davison, Juan Andrade, & David W. Murray. (2006). Active Control for Single Camera SLAM.
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Marçal Rusiñol. (2006). A Model of Vectorial Signatures in Terms of Expressive Sub-Shapes: Symbol Indexation in Technical Documents.
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Jose Antonio Rodriguez. (2006). Pen-based Interfaces and Recognition: Application to Proofreading Interpretation.
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Miquel Ferrer. (2006). Spectral Median Graphs and its Application to Graphical Symbol Recognition.
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Angel Sappa, David Geronimo, Fadi Dornaika, & Antonio Lopez. (2006). Real Time Vehicle Pose Using On-Board Stereo Vision System. In International Conference on Image Analysis and Recognition (205–216).
Abstract: This paper presents a robust technique for a real time estimation of both camera’s position and orientation—referred as pose. A commercial stereo vision system is used. Unlike previous approaches, it can be used either for urban or highway scenarios. The proposed technique consists of two stages. Initially, a compact 2D representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the road. At the same time,
relative camera’s position and orientation are computed. The proposed technique is intended to be used on a driving assistance scheme for applications such as obstacle or pedestrian detection. Experimental results on urban environments with different road geometries are presented.
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David Geronimo, Angel Sappa, Antonio Lopez, & Daniel Ponsa. (2006). Pedestrian Detection Using AdaBoost Learning of Features and Vehicle Pitch Estimation.
Abstract: In this paper we propose a combination of different Haar filter sets and Edge Orientation Histograms (EOH) in order to learn a model for pedestrian detection. As we will show, with the addition of EOH we obtain better ROCs than using Haar filters alone. Hence, a model consisting of discriminant features, selected by AdaBoost, is applied at pedestrian-sized image windows in order to perform
the classification. Additionally, taking into account the final application, a driver assistance system with realtime requirements, we propose a novel stereo-based camera pitch estimation to reduce the number of explored windows.
With this approach, the system can work in urban roads, as will be illustrated by current results.
Keywords: ADAS, pedestrian detection, adaboost learning, pitch estimation, haar wavelets, edge orientation histograms.
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V. Kober, Mikhail Mozerov, J. Alvarez-Borrego, & I.A. Ovseyevich. (2006). Adaptive Correlation Filters for Pattern Recognition. Pattern Recognition and Image Analysis, 425–431.
Abstract: Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance.
Keywords: Pattern recognition, Correlation filters, A adaptive filters
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V. Kober, Mikhail Mozerov, J. Alvarez-Borrego, & I.A. Ovseyevich. (2006). Pattern Recognition of Fragmented Objects with Adaptive Correlation Filters.
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