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A. Pujol, Jordi Vitria, Petia Radeva, Xavier Binefa, Robert Benavente, Ernest Valveny, et al. (1999). Real time pharmaceutical product recognition using color and shape indexing. In Proceedings of the 2nd International Workshop on European Scientific and Industrial Collaboration (WESIC´99), Promotoring Advanced Technologies in Manufacturing..
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Jordi Vitria, Petia Radeva, X. Binefa, A. Pujol, Ernest Valveny, Robert Benavente, et al. (1999). Real time recognition of pharmaceutical products by subspace methods.
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Xavier Roca, Jordi Vitria, Maria Vanrell, & Juan J. Villanueva. (1999). Visual behaviours for binocular navigation with autonomous systems..
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Maria Vanrell, Jordi Vitria, & Xavier Roca. (1997). A multidimensional scaling approach to explore the behavior of a texture perception algorithm. Machine Vision and Applications, 9, 262–271.
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Xavier Roca, Jordi Vitria, Maria Vanrell, & Juan J. Villanueva. (1999). Gaze control in a binocular robot systems.
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Maria Vanrell, Jordi Vitria, & Xavier Roca. (1993). A General Morphological Framework for Perceptual Texture Discrimination based on Granulometries..
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Xavier Roca, Jordi Vitria, Maria Vanrell, & Juan J. Villanueva. (2000). Visual behaviours for binocular navigation with autonomous systems..
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Miquel Ferrer, Robert Benavente, Ernest Valveny, J. Garcia, Agata Lapedriza, & Gemma Sanchez. (2008). Aprendizaje Cooperativo Aplicado a la Docencia de las Asignaturas de Programacion en Ingenieria Informatica.
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Robert Benavente, Ernest Valveny, Jaume Garcia, Agata Lapedriza, Miquel Ferrer, & Gemma Sanchez. (2008). Una experiencia de adaptacion al EEES de las asignaturas de programacion en Ingenieria Informatica.
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Maria Vanrell, & Jordi Vitria. (1993). Mathematical Morphology, Granulometries and Texture Perception..
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X. Binefa, Jordi Vitria, & Maria Vanrell. (1992). Reconstruccion tridimensional de imagenes Microscopicas..
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Maria Vanrell, & Jordi Vitria. (1997). Optimal 3x3 decomposable disks for morphological transformations. Image and Vision Computing, 15(2): 845–854.
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Agata Lapedriza, Jaume Garcia, Ernest Valveny, Robert Benavente, Miquel Ferrer, & Gemma Sanchez. (2008). Una experiencia de aprenentatge basada en projectes en el ambit de la informatica.
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Ricardo Toledo, Ramon Baldrich, Ernest Valveny, & Petia Radeva. (2002). Enhancing snakes for vessel detection in angiography images..
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Robert Benavente, Laura Igual, & Fernando Vilariño. (2008). Current Challenges in Computer Vision.
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Xavier Otazu, & Oriol Pujol. (2006). Wavelet based approach to cluster analysis. Application on low dimensional data sets. PRL - Pattern Recognition Letters, 27(14), 1590–1605.
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Jaykishan Patel, Alban Flachot, Javier Vazquez, David H. Brainard, Thomas S. A. Wallis, Marcus A. Brubaker, et al. (2023). A deep convolutional neural network trained to infer surface reflectance is deceived by mid-level lightness illusions. JV - Journal of Vision, 23(9), 4817.
Abstract: A long-standing view is that lightness illusions are by-products of strategies employed by the visual system to stabilize its perceptual representation of surface reflectance against changes in illumination. Computationally, one such strategy is to infer reflectance from the retinal image, and to base the lightness percept on this inference. CNNs trained to infer reflectance from images have proven successful at solving this problem under limited conditions. To evaluate whether these CNNs provide suitable starting points for computational models of human lightness perception, we tested a state-of-the-art CNN on several lightness illusions, and compared its behaviour to prior measurements of human performance. We trained a CNN (Yu & Smith, 2019) to infer reflectance from luminance images. The network had a 30-layer hourglass architecture with skip connections. We trained the network via supervised learning on 100K images, rendered in Blender, each showing randomly placed geometric objects (surfaces, cubes, tori, etc.), with random Lambertian reflectance patterns (solid, Voronoi, or low-pass noise), under randomized point+ambient lighting. The renderer also provided the ground-truth reflectance images required for training. After training, we applied the network to several visual illusions. These included the argyle, Koffka-Adelson, snake, White’s, checkerboard assimilation, and simultaneous contrast illusions, along with their controls where appropriate. The CNN correctly predicted larger illusions in the argyle, Koffka-Adelson, and snake images than in their controls. It also correctly predicted an assimilation effect in White's illusion. It did not, however, account for the checkerboard assimilation or simultaneous contrast effects. These results are consistent with the view that at least some lightness phenomena are by-products of a rational approach to inferring stable representations of physical properties from intrinsically ambiguous retinal images. Furthermore, they suggest that CNN models may be a promising starting point for new models of human lightness perception.
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Marcos V Conde, Florin Vasluianu, Javier Vazquez, & Radu Timofte. (2023). Perceptual image enhancement for smartphone real-time applications. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 1848–1858).
Abstract: Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones.
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