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In this paper we introduce a novel non-linear mapping technique to effectively decolorize images. Designed in a multi-scale fusion fashion, we first derive three input images represented by the color channels R, G and B. In order to transfer to the decolorized image only the relevant features of the derived inputs, we define two weight maps based on information theoretic approaches. The first weight map extracts visually salient regions based on a information maximization strategy while the second weight map filters the amount of local variation of each derived input computing local entropy per patch. Finally, to reduce the local distortions that might be introduced by the weight maps discontinuities, our decolorization strategy is designed in a multi-scale fusion. We also introduce a blind measure to accurately evaluate image decolorization methods. Our comprehensive qualitative and quantitative validation demonstrates that our method yields very competitive results ; Part of this work has was supported by 2020 European Union Research and Innovation Horizon 2020 under the grant agreement Marie Sklodowska-Curie No 712949 (TECNIOspring PLUS), as well as the Agency for the Competitiveness of the Company of the Generalitat de Catalunya - ACCIO: TECSPR17-1-0054. Part of this work was supported by research grant GNaC2018 - ARUT, no. 1361-01.02.2019, financed by Politehnica University of Timisoara and also by Spanish project from Ministery of Economy and Competitivity, MINECO, TIN2016-75866- C3-3-R

This paper introduces a volume rendering framework based on the information channel constructed between the volumetric data set and a set of viewpoints. From this channel, the information associated to each voxel can be interpreted as an ambient occlusion value that allows to obtain illustrative volume visualizations. The use of the voxel information combined with the assignation of color to each viewpoint and non-photorealistic effects produces an enhanced visualization of the volume data set. Voxel information is also applied to modulate the transfer function and to select the most informative views ; This work has been supported by TIN2007-68066-C04-01 and TIN2007-67982-C02 of the Ministry of Education and Science (Spanish Government) and by the 2009 SGR 643 from the Generalitat of Catalunya

This paper introduces a volume rendering framework based on the information channel constructed between the volumetric data set and a set of viewpoints. From this channel, the information associated to each voxel can be interpreted as an ambient occlusion value that allows to obtain illustrative volume visualizations. The use of the voxel information combined with the assignation of color to each viewpoint and non-photorealistic effects produces an enhanced visualization of the volume data set. Voxel information is also applied to modulate the transfer function and to select the most informative views ; This work has been supported by TIN2007-68066-C04-01 and TIN2007-67982-C02 of the Ministry of Education and Science (Spanish Government) and by the 2009 SGR 643 from the Generalitat of Catalunya

This paper introduces a volume rendering framework based on the information channel constructed between the volumetric data set and a set of viewpoints. From this channel, the information associated to each voxel can be interpreted as an ambient occlusion value that allows to obtain illustrative volume visualizations. The use of the voxel information combined with the assignation of color to each viewpoint and non-photorealistic effects produces an enhanced visualization of the volume data set. Voxel information is also applied to modulate the transfer function and to select the most informative views ; This work has been supported by TIN2007-68066-C04-01 and TIN2007-67982-C02 of the Ministry of Education and Science (Spanish Government) and by the 2009 SGR 643 from the Generalitat of Catalunya

This paper uses quantitative eye tracking indicators to analyze the relationship between images of paintings and human viewing. First, we build the eye tracking fixation sequences through areas of interest (AOIs) into an information channel, the gaze channel. Although this channel can be interpreted as a generalization of a first-order Markov chain, we show that the gaze channel is fully independent of this interpretation, and stands even when first-order Markov chain modeling would no longer fit. The entropy of the equilibrium distribution and the conditional entropy of a Markov chain are extended with additional information-theoretic measures, such as joint entropy, mutual information, and conditional entropy of each area of interest. Then, the gaze information channel is applied to analyze a subset of Van Gogh paintings. Van Gogh artworks, classified by art critics into several periods, have been studied under computational aesthetics measures, which include the use of Kolmogorov complexity and permutation entropy. The gaze information channel paradigm allows the information-theoretic measures to analyze both individual gaze behavior and clustered behavior from observers and paintings. Finally, we show that there is a clear correlation between the gaze information channel quantities that come from direct human observation, and the computational aesthetics measures that do not rely on any human observation at all ; This work is supported by the National Natural Science Foundation of China under grant No. 61702359, and by grant TIN2016-75866-C3-3-R from Spanish Government.

Information theory, and the concept of information channel, allows us to calculate the mutual information between the source (input) and the receiver (output), both represented by probability distributions over their possible states. In this paper, we use the theory behind the information channel to provide an enhanced interpretation to a Social Accounting Matrix (SAM), a square matrix whose columns and rows present the expenditure and receipt accounts of economic actors. Under our interpretation, the SAM's coefficients, which, conceptually, can be viewed as a Markov chain, can be interpreted as an information channel, allowing us to optimize the desired level of aggregation within the SAM. In addition, the developed information measures can describe accurately the evolution of a SAM over time. Interpreting the SAM matrix as an ergodic chain could show the effect of a shock on the economy after several periods or economic cycles. Under our new framework, finding the power limit of the matrix allows one to check (and confirm) whether the matrix is well-constructed (irreducible and aperiodic), and obtain new optimization functions to balance the SAM matrix. In addition to the theory, we also provide two empirical examples that support our channel concept and help to understand the associated measures ; M.S., M.F. and M.V. acknowledge support from the project PID2019-106426RB-C31 from the Spanish Government

This paper presents a study and a comparison of the use of different information-theoretic measures for polygonal mesh simplification. Generalized measures from Information Theory such as Havrda–Charvát–Tsallis entropy and mutual information have been applied. These measures have been used in the error metric of a surface simplification algorithm. We demonstrate that these measures are useful for simplifying three-dimensional polygonal meshes. We have also compared these metrics with the error metrics used in a geometry-based method and in an image-driven method. Quantitative results are presented in the comparison using the root-mean-square error (RMSE) ; This work was supported by the Spanish Ministry of Science and Innovation (Project TIN2010-21089-C03-03 and TIN2010-21089-C03-01) and Feder Funds, Bancaixa (Project P1.1B2010-08), Generalitat Valenciana (Project PROMETEO/2010/028) and Project 2009-SGR-643 of Generalitat de Catalunya (Catalan Government)

This paper presents a study and a comparison of the use of different information-theoretic measures for polygonal mesh simplification. Generalized measures from Information Theory such as Havrda–Charvát–Tsallis entropy and mutual information have been applied. These measures have been used in the error metric of a surface simplification algorithm. We demonstrate that these measures are useful for simplifying three-dimensional polygonal meshes. We have also compared these metrics with the error metrics used in a geometry-based method and in an image-driven method. Quantitative results are presented in the comparison using the root-mean-square error (RMSE) ; This work was supported by the Spanish Ministry of Science and Innovation (Project TIN2010-21089-C03-03 and TIN2010-21089-C03-01) and Feder Funds, Bancaixa (Project P1.1B2010-08), Generalitat Valenciana (Project PROMETEO/2010/028) and Project 2009-SGR-643 of Generalitat de Catalunya (Catalan Government)

This paper presents a study and a comparison of the use of different information-theoretic measures for polygonal mesh simplification. Generalized measures from Information Theory such as Havrda–Charvát–Tsallis entropy and mutual information have been applied. These measures have been used in the error metric of a surface simplification algorithm. We demonstrate that these measures are useful for simplifying three-dimensional polygonal meshes. We have also compared these metrics with the error metrics used in a geometry-based method and in an image-driven method. Quantitative results are presented in the comparison using the root-mean-square error (RMSE) ; This work was supported by the Spanish Ministry of Science and Innovation (Project TIN2010-21089-C03-03 and TIN2010-21089-C03-01) and Feder Funds, Bancaixa (Project P1.1B2010-08), Generalitat Valenciana (Project PROMETEO/2010/028) and Project 2009-SGR-643 of Generalitat de Catalunya (Catalan Government)

Viewpoint selection has been an emerging area in computer graphics for some years, and it is now getting maturity with applications in fields such as scene navigation, scientific visualization, object recognition, mesh simplification, and camera placement. In this survey, we review and compare twenty-two measures to select good views of a polygonal 3D model, classify them using an extension of the categories defined by Secord et al., and evaluate them against the Dutagaci et al. benchmark. Eleven of these measures have not been reviewed in previous surveys. Three out of the five short-listed best viewpoint measures are directly related to information. We also present in which fields the different viewpoint measures have been applied. Finally, we provide a publicly available framework where all the viewpoint selection measures are implemented and can be compared against each other ; This work has been partially funded by grant TIN2016-75866-C3-3-R from the Spanish Government, grant 2017-SGR-1101 from Catalan Government and by the National Natural Science Foundation of China (Nos. 61571439, 61471261 and 61771335)

Brain networks are widely used models to understand the topology and organization of the brain. These networks can be represented by a graph, where nodes correspond to brain regions and edges to structural or functional connections. Several measures have been proposed to describe the topological features of these networks, but unfortunately, it is still unclear which measures give the best representation of the brain. In this paper, we propose a new set of measures based on information theory. Our approach interprets the brain network as a stochastic process where impulses are modeled as a random walk on the graph nodes. This new interpretation provides a solid theoretical framework from which several global and local measures are derived. Global measures provide quantitative values for the whole brain network characterization and include entropy, mutual information, and erasure mutual information. The latter is a new measure based on mutual information and erasure entropy. On the other hand, local measures are based on different decompositions of the global measures and provide different properties of the nodes. Local measures include entropic surprise, mutual surprise, mutual predictability, and erasure surprise. The proposed approach is evaluated using synthetic model networks and structural and functional human networks at different scales. Results demonstrate that the global measures can characterize new properties of the topology of a brain network and, in addition, for a given number of nodes, an optimal number of edges is found for small-world networks. Local measures show different properties of the nodes such as the uncertainty associated to the node, or the uniqueness of the path that the node belongs. Finally, the consistency of the results across healthy subjects demonstrates the robustness of the proposed measures ; This work was supported by the Spanish Government (Grant No. TIN2016-75866-C3-3-R ) and by the Catalan Government (Grant No. 2017-SGR-1101). Data were provided, in part, by the Human ...

Mutual information is one of the mostly used measures for evaluating image similarity. In this paper, we investigate the application of three different Tsallis-based generalizations of mutual information to analyze the similarity between scanned documents. These three generalizations derive from the Kullback–Leibler distance, the difference between entropy and conditional entropy, and the Jensen–Tsallis divergence, respectively. In addition, the ratio between these measures and the Tsallis joint entropy is analyzed. The performance of all these measures is studied for different entropic indexes in the context of document classification and registration ; This work has been funded in part with Grant Numbers TIN2010-21089-C03-01 from the Spanish Government and 2009-SGR-643 from the Catalan Government.

Mutual information is one of the mostly used measures for evaluating image similarity. In this paper, we investigate the application of three different Tsallis-based generalizations of mutual information to analyze the similarity between scanned documents. These three generalizations derive from the Kullback–Leibler distance, the difference between entropy and conditional entropy, and the Jensen–Tsallis divergence, respectively. In addition, the ratio between these measures and the Tsallis joint entropy is analyzed. The performance of all these measures is studied for different entropic indexes in the context of document classification and registration ; This work has been funded in part with Grant Numbers TIN2010-21089-C03-01 from the Spanish Government and 2009-SGR-643 from the Catalan Government.

Mutual information is one of the mostly used measures for evaluating image similarity. In this paper, we investigate the application of three different Tsallis-based generalizations of mutual information to analyze the similarity between scanned documents. These three generalizations derive from the Kullback–Leibler distance, the difference between entropy and conditional entropy, and the Jensen–Tsallis divergence, respectively. In addition, the ratio between these measures and the Tsallis joint entropy is analyzed. The performance of all these measures is studied for different entropic indexes in the context of document classification and registration ; This work has been funded in part with Grant Numbers TIN2010-21089-C03-01 from the Spanish Government and 2009-SGR-643 from the Catalan Government.

Exploded views are often used in illustration to overcome the problem of occlusion when depicting complex structures. In this paper, we propose a volume visualization technique inspired by exploded views that partitions the volume into a number of parallel slabs and shows them apart from each other. The thickness of slabs is driven by the similarity between partitions. We use an information-theoretic technique for the generation of exploded views. First, the algorithm identifies the viewpoint which gives the most structured view of the data. Then, the partition of the volume into the most informative slabs for exploding is obtained using two complementary similarity-based strategies. The number of slabs and the similarity parameter are freely adjustable by the user ; This work has been supported by TIN2007-68066-C04-01 and TIN2007-67982-C02 of the Ministry of Education and Science (Spanish Government), by the MedViz Initiative in Bergen (medviz.uib.no), and by the Austrian Science Fund (FWF) grant no. P18322