Classification of Ordered Texture Images Using Regression Modelling and Granulometric Features

Khatun, Mahmuda; Gray, Alison; Marshall, Stephen

doi:10.1109/imvip.2011.20

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Beyond the classic applications in sedimentology [1][2][3], other fields such as waste management [4], nano-materials [5], corrosion [6] and food engineering [7] have been attracting considerable research interest. In recent years much attention has arisen around the concept of 'urban mining', which concerns all the activities related to the recovery of energy and products generated by urban catabolism [8].…”

Section: Introductionmentioning

confidence: 99%

Grain-size assessment of fine and coarse aggregates through bipolar area morphology

Bianconi

Maria

Micale

et al. 2015

Machine Vision and Applications

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This paper presents a new methodology for computing grey-scale granulometries and estimating the mean size of fine and coarse aggregates. The proposed approach employs area morphology and combines the information derived from both openings and closings to determine the size distribution. The method, which we refer to as Bipolar Area Morphology (BAM), is general and can operate on particles of different size and shape. The effectiveness of the procedure was validate on a set of 13 classes of aggregates of size ranging from 0.125mm to 16mm and made a comparison with standard, fixed- shape granulometry. In the experiments our model con- sistently outperformed the standard approach and pre- dicted the correct size class with overall accuracy over 92%. Tests on three classes from real samples also con- firmed the potential of the method for application in real scenarios

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Section: Introductionmentioning

confidence: 99%

Grain-size assessment of fine and coarse aggregates through bipolar area morphology

Bianconi

Maria

Micale

et al. 2015

Machine Vision and Applications

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“…The employed features used for the discrimination of Pointillism are described in the followings: a) Granulometric Feature. Granulometry is employed to calculate the size distribution of paintbrushes in the artwork [12]. Granulometric analysis, based on the sequence of morphological opening and closing operations and the quantification of particles of different sizes [6], is used to estimate the intensity surface area distribution of paintbrushes of the artistic image as a function of size.…”

Section: Adopted Featuresmentioning

confidence: 99%

Classification of Pointillist paintings using colour and texture features

Georgoulaki

2022

IJECER

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Fine art paintings classification based on artistic style is a field of growing interest. Pointillist style is one of the most easily recognized painting styles by humans, due to its characteristic tiny detached paintbrushes of pure colour. In this paper automatic discrimination of artworks belonging to the style of Pointillism is investigated. The opposite styles considered are Cubism, Purism, Naïve art and Impressionism. Several colour and texture features are considered and a feature selection procedure is employed to reveal the most relevant ones to pointillist movement. Binary classification is performed, both in supervised and unsupervised mode, to assess the features’ discriminative ability. A small number of selected features is shown, by simulations results, to be quite powerful predictors resulting in a classification accuracy of 94% for a SVM classifier, 93.5% for a KNN classifier and 87% for a k-means classifier.

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“…Let us recall that a morphological opening at scale L removes local maxima over connected sets up to that scale (Southam and Harvey, 2009;Khatun et al, 2011;Bianconi et al, 2015). After an opening the resulting image has fewer intensity extrema than the original, and its total volume (sum of grey-levels) is reduced.…”

Section: Granulometrymentioning

confidence: 99%

Quality assessment for recycling aggregates from construction and demolition waste: An image-based approach for particle size estimation

et al. 2016

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Classification of Ordered Texture Images Using Regression Modelling and Granulometric Features

Cited by 4 publications

References 27 publications

Grain-size assessment of fine and coarse aggregates through bipolar area morphology

Grain-size assessment of fine and coarse aggregates through bipolar area morphology

Classification of Pointillist paintings using colour and texture features

Quality assessment for recycling aggregates from construction and demolition waste: An image-based approach for particle size estimation

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