3D line drawing from point clouds using chromatic stereo and shading

1 PDF document (8 pp., English).-- Contributed to: VSMM'08: 14th International Conference on Virtual Systems and Multimedia (Limassol, Cyprus, Oct 20-25, 2008)

Linear Chromatic Adaptation Transform Based on Delaunay Triangulation

Computer vision algorithms that use color information require color constant images to operate correctly. Color constancy of the images is usually achieved in two steps: first the illuminant is detected and then image is transformed with the chromatic adaptation transform ( CAT). Existing CAT methods use a single transformation matrix for all the colors of the input image. The method proposed in this paper requires multiple corresponding color pairs between source and target illuminants given by patches of the Macbeth color checker. It uses Delaunay triangulation to divide the color gamut of the input image into small triangles. Each color of the input image is associated with the triangle containing the color point and transformed with a full linear model associated with the triangle. Full linear model is used because diagonal models are known to be inaccurate if channel color matching functions do not have narrow peaks. Objective evaluation showed that the proposed method outperforms existing CAT methods by more than 21%; that is, it performs statistically significantly better than other existing methods.

Linear Chromatic Adaptation Transform Based on Delaunay Triangulation

Computer vision algorithms that use color information require color constant images to operate correctly. Color constancy of the images is usually achieved in two steps: first the illuminant is detected and then image is transformed with the chromatic adaptation transform ( CAT). Existing CAT methods use a single transformation matrix for all the colors of the input image. The method proposed in this paper requires multiple corresponding color pairs between source and target illuminants given by patches of the Macbeth color checker. It uses Delaunay triangulation to divide the color gamut of the input image into small triangles. Each color of the input image is associated with the triangle containing the color point and transformed with a full linear model associated with the triangle. Full linear model is used because diagonal models are known to be inaccurate if channel color matching functions do not have narrow peaks. Objective evaluation showed that the proposed method outperforms existing CAT methods by more than 21%; that is, it performs statistically significantly better than other existing methods.

Algorithms for colour image processing based on neurological models

373 p. : il., gráf., fot., tablas

Basque nominalizations and the role of structural Case in the licensing of null arguments

[EN] This article investigates the question of the licensing of null arguments in the so-called pro-drop languages. By focusing on the licensing of null subjects in the different types of -T(Z)E nominalizations in Basque, it aims at defining in a precise way the crucial feature that makes pro-drop possible in a clause. The central claim is that what licenses subject-drop is the assignment of structural Case. That is, it is argued that a subject can be null if and only if it is assigned structural Case. Different aspects of T(Z)E nominalizations are also explored, which show that even if these clauses are similar in the surface, they can be syntactically very different and furthermore, that infinitive clauses marked with the same nominalizing morpheme can also have diverging structures.

Latín, nacionalismo y arte alusiva en la historiografía de Antonio de Nebrija

"The Role of Latin in the Early Modern World: Linguistic identity and nationalism 1350-1800". Contributions from the conference held at the Universitat Autònoma de Barcelona, Casa Convalescència, 5-6 May 2010. Edited by Alejandro Coroleu, Carlo Caruso & Andrew Laird

Biologically-inspired data decorrelation for hyper-spectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification