Independent component analysis (ICA): A statistical approach to the analysis of superimposed rock paintings

Cuenca, Enrique Cerrillo; Sepúlveda, Marcela; Guerrero-Bueno, Zaray

doi:10.1016/j.jas.2020.105269

Cited by 7 publications

(7 citation statements)

References 24 publications

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“…In other words, it possesses the capability to reproject the input channels of the RGB image into components that, in turn, can be transformed into independent images. Recently, ICA has also been introduced [6,15], decomposing the original image into components with the highest possible independence in their information content [38]. ICA and PCA have been implemented using the scikit-image library for Python [39].…”

Section: Reduction Of Variance and Maximization Of Independence: Pca ...mentioning

confidence: 99%

“…ICA and PCA have been implemented using the scikit-image library for Python [39]. The differences between both methods are well established [15]. While PCA reduces variance among image pixels, ICA maximizes component independence, influencing their digital image analysis roles.…”

Section: Reduction Of Variance and Maximization Of Independence: Pca ...mentioning

confidence: 99%

“…Tools like DStretch simplify using statistical techniques with minimal input [8][9][10], while fulfilling the needs of most archaeologists for digital image analysis. Additionally, computing implementation of statistic techniques, such as Principal Component Analysis (PCA) or Independent Component Analysis (ICA), have been explored, showing effectiveness in separating color tones [12][13][14][15], though they come with inherent limitations. For instance, PCA or ICA in standard imagery can isolate only as many components (new grayscale images) as there are channels in the original image, typically three in RGB images.…”

Section: Introductionmentioning

confidence: 99%

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Color-based discrimination of color hues in rock paintings through Gaussian mixture models: a case study from Chomache site (Chile)

Cerrillo-Cuenca,

Sepúlveda,

Cabello

et al. 2024

Herit Sci

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The article explores advanced image processing techniques for pigment discrimination in rock art paintings, emphasizing color separation using RGB (red, green, blue) and LHCUv (Luminance, Hue, Chroma) imagery. It highlights the use of dimensionality reduction methods such as Principal Components Analisys PCA and Independent Component Analysis (ICA), with a focus on Gaussian Mixture Models (GMM) for probabilistic classification of image elements. This approach, applied to the Chomache archaeological site on the northernmost coast of the Atacama Desert in Chile, reveals previously undetected motifs and details, offering a nuanced perspective in rock art documentation and analysis. This proposal reinforces the value of rock art panel not only as a finished product but as a process.

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Section: Reduction Of Variance and Maximization Of Independence: Pca ...mentioning

confidence: 99%

Section: Reduction Of Variance and Maximization Of Independence: Pca ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Color-based discrimination of color hues in rock paintings through Gaussian mixture models: a case study from Chomache site (Chile)

Cerrillo-Cuenca,

Sepúlveda,

Cabello

et al. 2024

Herit Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…As the fast-evolving of BSS technology, various approaches have been developed for estimating the source signals, such as, independent component analysis (ICA) [3], nonlinear principal component analysis (NPCA) [4], sparse component analysis (SCA) [27], etcetera. Among which ICA has attracted extensive attention of many experts and scholars due to combining the statistical signal processing with information theory, which contributes to enhance signal separation performance.…”

Section: Introductionmentioning

confidence: 99%

An improved blind Gaussian source separation approach based on generalized Jaccard similarity

Jianwei

2022

Preprint

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Blind source separation (BSS) consists of recovering the independent source signals from their linear mixtures with unknown mixing channel. As a momentous technical means of signal processing and analysis, BSS has been widely utilized in image recognition, feature extraction, biomedical engineering, etcetera. The existing BSS approaches rely on the fundamental assumption: the source signals are non-Gaussian, this limited the use of BSS seriously. To overcome this problem and the weakness of cosine index in measuring the dynamic similarity between signals, this study proposes the fuzzy statistical behavior of local extremum (FSBLE) based on generalized Jaccard similarity as the signal's feature to implement the separation of nonlinear chaotic Gaussian source signals. In particular, the imperialist competition algorithm is introduced to minimize the cost function which jointly considers the stationarity factor describing the dynamical similarity of each source signal separately and the independency factor describing the dynamical similarity between source signals. Simulation experiments on synthetic data verify the effectiveness and superiority of the improved BSS approach in terms of cross-talking error and root mean square error (RMSE) criterions.

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“…ICA is an active and interdisciplinary research topic [19][20][21][22][23] with numerous applications in research areas like remote sensing [24][25][26][27][28], medical imaging [29][30][31][32], and image processing [33][34][35]. In geophysical research, the applications are mainly dedicated to the exploration geophysics to handle the multidimensionality of data and reduce the unwanted artifacts from raw data [36,37] or extract useful frequency features from processed seismic data [38].…”

Section: Introductionmentioning

confidence: 99%

3D Geophysical Post-Inversion Feature Extraction for Mineral Exploration Through Fast-ICA

Abbassi¹,

Cheng²

2021

Preprint

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A major problem in the post-inversion geophysical interpretation is the extraction of geological information from inverted physical property models, which do not necessarily represent all underlying geological features. No matter how accurate the inversions are, each inverted physical property model is sensitive to limited aspects of subsurface geology and is insensitive to other geological features that are otherwise detectable with complementary physical property models. Therefore, specific parts of the geological model can be reconstructed from different physical property models. To show how this reconstruction works, we simulated a complex geological system that comprises an original layered earth model that has passed several geological deformations and alteration overprints. Linear combination of complex geological features comprised three physical property distributions: Electrical resistivity, induced polarization chargeability, and magnetic susceptibility models. This study proposes a multivariate feature extraction approach to extract information about the underlying geological features comprising the bulk physical properties. We evaluated our method in numerical simulations and compared three feature extraction algorithms to see the tolerance of each method to the geological artifacts and noises. We show that the fast-independent component analysis (fast-ICA) algorithm by negentropy maximization is a robust method in the geological feature extraction that can handle the added unknown geological noises. The post-inversion physical properties are also used to reconstruct the underlying geological sources. We show that the sharpness of the inverted images is an important constraint on the feature extraction process. Our method successfully separates geological features in multiple 3D physical property models. This methodology is reproducible for any number of lithologies and physical property combinations and can recover the latent geological features, including the background geological patterns from overprints of chemical alteration.

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Independent component analysis (ICA): A statistical approach to the analysis of superimposed rock paintings

Cited by 7 publications

References 24 publications

Color-based discrimination of color hues in rock paintings through Gaussian mixture models: a case study from Chomache site (Chile)

Color-based discrimination of color hues in rock paintings through Gaussian mixture models: a case study from Chomache site (Chile)

An improved blind Gaussian source separation approach based on generalized Jaccard similarity

3D Geophysical Post-Inversion Feature Extraction for Mineral Exploration Through Fast-ICA

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