Bart M. Ter Haar Romeny, W. Niessen, J. Weickert, P. Van Roermund, W. Van Enk, Antonio Lopez, et al. (1996). Orientation detection of trabecular bone.
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Michael Villamizar, A. Sanfeliu, & Juan Andrade. (2006). Orientation Invariant Features for Multiclass Object Recognition.
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Zhong Jin, & Franck Davoine. (2004). Orthogonal ICA Representation Of Images.
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Joan Serrat, Javier Varona, Antonio Lopez, Xavier Roca, & Juan J. Villanueva. (2001). P3: a three-dimensional digitizer prototype..
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Robert Benavente, & Maria Vanrell. (2007). Parametrizacion del Espacio de Categorias de Color.
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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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Enric Marti, Carme Julia, & Debora Gil. (2007). PBL en la docencia de gráficos por computador (Vol. 1). Valladolid.
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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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Xavier Baro, David Masip, Elena Planas, & Julia Minguillon. (2013). PeLP: Plataforma para el Aprendizaje de Lenguajes de Programación.
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Saiping Zhang, L. H., Marta Mrak, Marc Gorriz Blanch, Shuai Wan, Fuzheng Yang. (2022). PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation-and Attention-based Network.
Abstract: In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos. Our framework includes attention and adaptation to different quantization parameters (QPs) in a single model. The attention module exploits global receptive fields that can capture and align long-range correlations between consecutive frames, which can be beneficial for enhancing perceptual quality of videos. The frame to be enhanced is fed into the deep network together with its neighboring frames, and in the first stage features at different depths are extracted. Then extracted features are fed into attention blocks to explore global temporal correlations, followed by a series of upsampling and convolution layers. Finally, the resulting features are processed by the QP-conditional adaptation module which leverages the corresponding QP information. In this way, a single model can be used to enhance adaptively to various QPs without requiring multiple models specific for every QP value, while having similar performance. Experimental results demonstrate the superior performance of the proposed PeQuENet compared with the state-of-the-art compressed video quality enhancement algorithms.
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Ramon Baldrich, Ricardo Toledo, Ernest Valveny, & Maria Vanrell. (2002). Perceptual Colour Image Segmentation..
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Josep Llados. (2006). Perspectives on the Analysis of Graphical Documents.
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X. Orriols, Lluis Barcelo, & X. Binefa. (2001). Polynomial Fiber Description of Motion for Video Mosaicing, Proceeding ICIP 2001..
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C. Molina, & J.B. Subirana. (1995). Polynomial-Time Algorithm for 2D object recognition..
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David Lloret, Antonio Lopez, & Joan Serrat. (1998). Precise registration of CT and MR volumes based on a new creaseness measure.
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