Lacunarity as a Texture Measure for Address Block Segmentation

Facon, Jacques; Menoti, David; Araújo, Arnaldo de Albuquerque

doi:10.1007/11578079_12

Cited by 9 publications

(4 citation statements)

References 8 publications

(9 reference statements)

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“…Krasowska, Borys, and Grzywna (2004) did a systematic study of lacunarity as a supplement for describing texture. Lacunarity as a texture measure was used also by Plotnick, Gardner, and O'Neill (1993) for quantifying landscape pattern, by Henebry and Kux (1995) for connecting image phenomenology and scene dynamics, and by Facon, Menoti, and Araújo (2005) for address block segmentation. Furthermore, Dàvila and Parés (2007) used lacunarity analysis to complement the microstructure description of heat-induced plasma proteins gels.…”

Section: Introductionmentioning

confidence: 99%

Texture appearance characterization of pre-sliced pork ham images using fractal metrics: Fourier analysis dimension and lacunarity

Valous

Mendoza

Sun

et al. 2009

Food Research International

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Section: Introductionmentioning

confidence: 99%

Texture appearance characterization of pre-sliced pork ham images using fractal metrics: Fourier analysis dimension and lacunarity

Valous

Mendoza

Sun

et al. 2009

Food Research International

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“…Multifractal analysis, as an extension of monofractal analysis, has been extensivel applied to quantify complex natural phenomena or to processes that exhibit similarit over a wide range of scales [47,66,[77][78][79]. Before multifractal calculations, the sliding win dow algorithm is employed to assist in capturing sufficient local features within the mas sive predictive units in this study [80][81][82][83]. The sliding procedure can be described as fo lows (Figure 3a): (i) The feature under analysis is entirely covered by a series of grids.…”

Section: Fractal and Multifractal Methodsmentioning

confidence: 99%

Fractal-Based Multi-Criteria Feature Selection to Enhance Predictive Capability of AI-Driven Mineral Prospectivity Mapping

Sun,

Feng,

et al. 2024

Fractal Fract

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AI-driven mineral prospectivity mapping (MPM) is a valid and increasingly accepted tool for delineating the targets of mineral exploration, but it suffers from noisy and unrepresentative input features. In this study, a set of fractal and multifractal methods, including box-counting calculation, concentration–area fractal modeling, and multifractal analyses, were employed to excavate the underlying nonlinear mineralization-related information from geological features. Based on these methods, multiple feature selection criteria, namely prediction–area plot, K-means clustering, information gain, chi-square, and the Pearson correlation coefficient, were jointly applied to rank the relative importance of ore-related features and their fractal representations, so as to choose the optimal input feature dataset readily used for training predictive AI models. The results indicate that fault density, the multifractal spectrum width (∆α) of the Yanshanian intrusions, information dimension (D1) of magnetic anomalies, correlation dimension (D2) of iron-oxide alteration, and the D2 of argillic alteration serve as the most effective predictor features representative of the corresponding ore-controlling elements. The comparative results of the model assessment suggest that all the AI models trained by the fractal datasets outperform their counterparts trained by raw datasets, demonstrating a significant improvement in the predictive capability of fractal-trained AI models in terms of both classification accuracy and predictive efficiency. A Shapley additive explanation was employed to trace the contributions of these features and to explain the modeling results, which imply that fractal representations provide more discriminative and definitive feature values that enhance the cognitive capability of AI models trained by these data, thereby improving their predictive performance, especially for those indirect predictor features that show subtle correlations with mineralization in the raw dataset. In addition, fractal-trained models can benefit practical mineral exploration by outputting low-risk exploration targets that achieve higher capturing efficiency and by providing new mineralization clues extracted from remote sensing data. This study demonstrates that the fractal representations of geological features filtered by multi-criteria feature selection can provide a feasible and promising means of improving the predictive capability of AI-driven MPM.

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“…Facon et al [6] have proposed an approach based on lacunarity to locate the AB in Brazil postal envelopes. Lacunarity depends on mean and variance for the window size throughout the images.…”

Section: Review Of Various Methods Used For Dab Segmentationmentioning

confidence: 99%

Review of OCR Techniques Used in Automatic Mail Sorting of Postal Envelopes

Radha¹,

Aparna²

2013

SIPIJ

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This paper presents a review of various OCR techniques used in the automatic mail sorting process. Acomplete description on various existing methods for address block extraction and digit recognition thatwere used in the literature is discussed. The objective of this study is to provide a complete overview aboutthe methods and techniques used by many researchers for automating the mail sorting process in postalservice in various countries. The significance of Zip code or Pincode recognition is discussed

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Lacunarity as a Texture Measure for Address Block Segmentation

Cited by 9 publications

References 8 publications

Texture appearance characterization of pre-sliced pork ham images using fractal metrics: Fourier analysis dimension and lacunarity

Texture appearance characterization of pre-sliced pork ham images using fractal metrics: Fourier analysis dimension and lacunarity

Fractal-Based Multi-Criteria Feature Selection to Enhance Predictive Capability of AI-Driven Mineral Prospectivity Mapping

Review of OCR Techniques Used in Automatic Mail Sorting of Postal Envelopes

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