P. Ricaurte, C. Chilan, Cristhian A. Aguilera-Carrasco, Boris X. Vintimilla, & Angel Sappa. (2014). Performance Evaluation of Feature Point Descriptors in the Infrared Domain. In 9th International Conference on Computer Vision Theory and Applications (Vol. 1, pp. 545–550).
Abstract: This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered.
Keywords: Infrared Imaging; Feature Point Descriptors
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Antoni Gurgui, Debora Gil, & Enric Marti. (2015). Laplacian Unitary Domain for Texture Morphing. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications VISIGRAPP2015 (Vol. 1, pp. 693–699). SciTePress.
Abstract: Deformation of expressive textures is the gateway to realistic computer synthesis of expressions. By their good mathematical properties and flexible formulation on irregular meshes, most texture mappings rely on solutions to the Laplacian in the cartesian space. In the context of facial expression morphing, this approximation can be seen from the opposite point of view by neglecting the metric. In this paper, we use the properties of the Laplacian in manifolds to present a novel approach to warping expressive facial images in order to generate a morphing between them.
Keywords: Facial; metamorphosis;LaplacianMorphing
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Aymen Azaza, Joost Van de Weijer, Ali Douik, Javad Zolfaghari Bengar, & Marc Masana. (2020). Saliency from High-Level Semantic Image Features. SN - SN Computer Science, 1–12.
Abstract: Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS).
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Josep Llados, J. Lopez-Krahe, & Enric Marti. (1999). A Hough-based method for hatched pattern detection in maps and diagrams..
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Felipe Lumbreras, Ramon Baldrich, Maria Vanrell, Joan Serrat, & Juan J. Villanueva. (1999). Multiresolution colour texture representations for tile classification.
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Daniel Ponsa, A.F. Sole, Antonio Lopez, Cristina Cañero, Petia Radeva, & Jordi Vitria. (1999). Regularized EM.
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David Guillamet, & Jordi Vitria. (1999). Using Eigenspace analysis of color distributions for object recognition.
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A. Pujol, Felipe Lumbreras, Javier Varona, & Juan J. Villanueva. (1999). Template matching through invariant eigenspace projection..
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Josep Llados, Felipe Lumbreras, & Javier Varona. (1999). A multidocument platform for automatic reading of identity cards..
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A. Martinez, & Jordi Vitria. (1998). Learning mixture models with the EM algorithm and genetic algorithms.
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A.F. Sole, Antonio Lopez, Cristina Cañero, Petia Radeva, & J. Saludes. (1999). Crease enhancement diffusion.
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Javier Varona, A. Pujol, & Juan J. Villanueva. (1999). Visual tracking in application domains..
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Antonio Lopez, David Lloret, & Joan Serrat. (1998). Creaseness measures for CT and MR image registration..
Abstract: Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature kM of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy
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Antonio Lopez, Felipe Lumbreras, & Joan Serrat. (1998). Creaseness form level set extrinsec curvature..
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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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