Aesthetical Issues of Leonardo Da Vinci’s and Pablo Picasso’s Paintings with Stochastic Evaluation

Sargentis, G.-Fivos; Dimitriadis, Panayiotis; Koutsoyiannis, Demetris

doi:10.3390/heritage3020017

Cited by 20 publications

(13 citation statements)

References 70 publications

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“…A stochastic computational tool abbreviated 2D-C [54,55] is used to study the clustering in 2D space, using images from various sources. 2D-C measures the degree of variability (change in variability vs. scale) in images.…”

Section: Methodsmentioning

confidence: 99%

“…This method has been introduced in the evaluation of the aesthetic of art paintings and landscape [54,55], so in order to exam the impact of clustering of MFP, we will see the following example.…”

Section: Methodsmentioning

confidence: 99%

“…Different clustering arrangements of the herd are examined (Figure 2a,b), aiming to quantify the visual transformation to the examined scenery, using the original image that does not have any transformation as a baseline (Figure 2c). A stochastic method has been developed for quantifying the transformation of the images based on 2D climacograms [54,55]. For the quantification of the difference between climacograms, we subtracted their values for each scale:…”

Section: Clustering In the Analysis Of Views Of Objectsmentioning

confidence: 99%

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Landscape Planning of Infrastructure through Focus Points’ Clustering Analysis. Case Study: Plastiras Artificial Lake (Greece)

et al. 2021

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Even though landscape quality is largely a subjective issue, the integration of infrastructure into landscapes has been identified as a key element of sustainability. In a spatial planning context, the landscape impacts that are generated by infrastructures are commonly quantified through visibility analysis. In this study, we develop a new method of visibility analysis and apply it in a case study of a reservoir (Plastiras dam in Greece). The methodology combines common visibility analysis with a stochastic tool for visual-impacts evaluation; points that generate high visual contrasts in landscapes are considered Focus Points (FPs) and their clustering in landscapes is analyzed trying to answer two questions: (1) How does the clustering of Focus Points (FPs) impact the aesthetic value of the landscape? (2) How can the visual impacts of these FPs be evaluated? Visual clustering is calculated utilizing a stochastic analysis of generated Zones of Theoretical Visibility. Based on the results, we argue that if the visual effect of groups of FPs is positive, then the optimal sitting of FPs should be in the direction of faint clustering, whereas if the effect is negative, the optimal sitting of FPs should be directed to intense clustering. In order to optimize the landscape integration of infrastructure, this method could be a useful analytical tool for environmental impact assessment or a monitoring tool for a project’s managing authorities. This is demonstrated through the case study of Plastiras’ reservoir, where the clustering of positively perceived FPs is found to be an overlooked attribute of its perception as a highly sustainable infrastructure project.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Clustering In the Analysis Of Views Of Objectsmentioning

confidence: 99%

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Landscape Planning of Infrastructure through Focus Points’ Clustering Analysis. Case Study: Plastiras Artificial Lake (Greece)

et al. 2021

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“…Α stochastic computational tool called 2D-C [52][53][54][55][56][57][58] is used to analyze images of art paintings quantifying the variability of the brightness vs. scale in a specific image and in a group of images as well. Here, we refer to spatial (not time) scale, defined as the ratio of the area of k × k adjacent cells (i.e., scale k) that are averaged to form the (scaled) spatial field, over the spatial resolution of the original field (i.e., at scale 1).…”

Section: Quantifying the Variability In Art Painting Through The 2d Cmentioning

confidence: 99%

A Stochastic View of Varying Styles in Art Paintings

Sargentis¹,

Dimitriadis²,

Iliopoulou³

et al. 2021

Heritage

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A physical process is characterized as complex when it is difficult to analyze and explain in a simple way, and even more difficult to predict. The complexity within an art painting is expected to be high, possibly comparable to that of nature. Herein, we apply a 2D stochastic methodology to images of both portrait photography and artistic portraits, the latter belonging to different genres of art, with the aim to better understand their variability in quantitative terms. To quantify the dependence structure and variability, we estimate the Hurst parameter, which is a common dependence metric for hydrometeorological processes. We also seek connections between the identified stochastic patterns and the desideratum that each art movement aimed to express. Results show remarkable stochastic similarities between portrait paintings, linked to philosophical, cultural and theological characteristics of each period.

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“…A stochastic computational tool called 2D-Climacogram, abbreviated as 2D-C [38,39], is used to study the clustering in 2D space, using images from various sources. 2D-C measures the degree of variability (change in variability vs. scale) in images using stochastic analysis.…”

Section: Stochastic Analysis Of Clustering In 2d Space: the 2d-c Toolmentioning

confidence: 99%

Evolution of Clustering Quantified by a Stochastic Method—Case Studies on Natural and Human Social Structures

et al. 2020

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Clustering structures appearing from small to large scales are ubiquitous in the physical world. Interestingly, clustering structures are omnipresent in human history too, ranging from the mere organization of life in societies (e.g., urbanization) to the development of large-scale infrastructure and policies for meeting organizational needs. Indeed, in its struggle for survival and progress, mankind has perpetually sought the benefits of unions. At the same time, it is acknowledged that as the scale of the projects grows, the cost of the delivered products is reduced while their quantities are maximized. Thus, large-scale infrastructures and policies are considered advantageous and are constantly being pursued at even great scales. This work develops a general method to quantify the temporal evolution of clustering, using a stochastic computational tool called 2D-C, which is applicable for the study of both natural and human social spatial structures. As case studies, the evolution of the structure of the universe, of ecosystems and of human clustering structures such as urbanization, are investigated using novel sources of spatial information. Results suggest the clear existence both of periods of clustering and declustering in the natural world and in the human social structures; yet clustering is the general trend. In view of the ongoing COVID-19 pandemic, societal challenges arising from large-scale clustering structures are discussed.

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Aesthetical Issues of Leonardo Da Vinci’s and Pablo Picasso’s Paintings with Stochastic Evaluation

Cited by 20 publications

References 70 publications

Landscape Planning of Infrastructure through Focus Points’ Clustering Analysis. Case Study: Plastiras Artificial Lake (Greece)

Landscape Planning of Infrastructure through Focus Points’ Clustering Analysis. Case Study: Plastiras Artificial Lake (Greece)

A Stochastic View of Varying Styles in Art Paintings

Evolution of Clustering Quantified by a Stochastic Method—Case Studies on Natural and Human Social Structures

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