Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane

Zunino, Luciano; Ribeiro, Haroldo V.

doi:10.1016/j.chaos.2016.09.005

Cited by 64 publications

(54 citation statements)

References 42 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Due to this underlying simplicity and also because our approach is very fast and scalable from the computational point of view, we believe it can be easily implemented and adapted for other more complex experimental situations involving the study of liquid crystals and perhaps for probing physical properties of different materials. The normalized permutation entropy H [25] and statistical complexity C [33] are two complexity measures originally proposed for characterizing time series [26], and that were more recently generalized for considering higher dimensional data such as images [27,28]. We refer the more detail-oriented reader to the previouslycited references, where a complete description of these techniques can be found.…”

Section: Discussionmentioning

confidence: 99%

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Sigaki

Souza

et al. 2019

Phys. Rev. E

View full text Add to dashboard Cite

Imaging techniques are essential tools for inquiring a number of properties from different materials. Liquid crystals are often investigated via optical and image processing methods. In spite of that, considerably less attention has been paid to the problem of extracting physical properties of liquid crystals directly from textures images of these materials. Here we present an approach that combines two physics-inspired image quantifiers (permutation entropy and statistical complexity) with machine learning techniques for extracting physical properties of nematic and cholesteric liquid crystals directly from their textures images. We demonstrate the usefulness and accuracy of our approach in a series of applications involving simulated and experimental textures, in which physical properties of these materials (namely: average order parameter, sample temperature, and cholesteric pitch length) are predicted with significant precision. Finally, we believe our approach can be useful in more complex liquid crystal experiments as well as for probing physical properties of other materials that are investigated via imaging techniques.

show abstract

Section: Discussionmentioning

confidence: 99%

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Sigaki

Souza

et al. 2019

Phys. Rev. E

View full text Add to dashboard Cite

show abstract

“…This is not surprising, since the number of images and thus the ground area increase with the number of GCPs, and, hence, automatically include a stronger horizontal and vertical distribution and thus higher elevation ranges. The Shannon entropy is a measure of image texture [14] and only shows a small, non-significant correlation with the RMSE; disregarding one outlier DTM (14 September 1977), the correlation becomes much stronger (R 2 = 0.55 (p ≤ 0.05); see Figure 3c,d). Image overlap is also expected to have an effect on DTM quality [3].…”

Section: Bundle Adjustmentmentioning

confidence: 99%

Structure-from-Motion Using Historical Aerial Images to Analyse Changes in Glacier Surface Elevation

Mölg

Bolch

2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:The application of structure-from-motion (SfM) to generate digital terrain models (DTMs) derived from different image sources has strongly increased, the major reason for this being that processing is substantially easier with SfM than with conventional photogrammetry. To test the functionality in a demanding environment, we applied SfM and conventional photogrammetry to archival aerial images from Zmuttgletscher, a mountain glacier in Switzerland, for nine dates between 1946 and 2005 using the most popular software packages, and compared the results regarding bundle adjustment and final DTM quality. The results suggest that by using SfM it is possible to produce DTMs of similar quality as with conventional photogrammetry. Higher point cloud density and less noise allow a higher ground resolution of the final DTM, and the time effort from the user is 3-6 times smaller, while the controls of the commercial software packages Agisoft PhotoScan (Version 1.2; Agisoft, St. Petersburg, Russia) and Pix4Dmapper (Version 3.0; Pix4D, Lausanne, Switzerland) are limited in comparison to ERDAS photogrammetry. SfM performs less reliably when few images with little overlap are processed. Even though SfM facilitates the largely automated production of high quality DTMs, the user is not exempt from a thorough quality check, at best with reference data where available. The resulting DTM time series revealed an average change in surface elevation at the glacier tongue of −67.0 ± 5.3 m. The spatial pattern of changes over time reflects the influence of flow dynamics and the melt of clean ice and that under debris cover. With continued technological advances, we expect to see an increasing use of SfM in glaciology for a variety of purposes, also in processing archival aerial imagery.

show abstract

“…The interpretation of these diagrams is based on the following intuitive considerations: zero entropy and complexity correspond to a completely regular structure, and high entropy and zero complexity correspond to a completely random spatially independent noise [19,20].…”

Section: "Complexity-entropy" Diagramsmentioning

confidence: 99%

“Complexity–entropy” diagrams and their application to the study of coal tectonic disturbance

Malinnikova

Uchaev

et al. 2019

E3S Web Conf.

View full text Add to dashboard Cite

It is investigated the possibility of using so–called "complexity–entropy" diagrams for the quantitative description of the degree of coal disturbance using coal images obtained by means of scanning electron microscope (SEM). These diagrams plot structural complexity measure (vertical axis) versus entropy measure (horizontal axis) for distribution of probability given in some way on the image. In this paper, the values of both measures were calculated on the basis of the shearlet transform, and the Jensen divergence was used as the basic divergence for calculating the complexity measure. All calculations were performed for more than 140 images of coal specimens with various degrees of disturbance, obtained from the quiet zone of the seam and the outburst zone. As a result of research, it was found that two-dimensional distributions for measures of complexity and entropy in most cases are informative data sets for differentiating coals by degree of complexity. Moreover, such characteristics of these distributions as mathematical expectation and, to a less degree, mode can be used as simple quantitative descriptors of coals with various degrees of disturbance. These characteristics can be used to show the closeness of the spatial structure for the analyzed coal specimens to strictly periodic or absolutely chaotic ones. On the basis of the obtained results, conclusions about the possibility to separate coals according to the degree of their outburst hazard were done.

show abstract

Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane

Cited by 64 publications

References 42 publications

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Structure-from-Motion Using Historical Aerial Images to Analyse Changes in Glacier Surface Elevation

“Complexity–entropy” diagrams and their application to the study of coal tectonic disturbance

Contact Info

Product

Resources

About