On Comparing Colour Spaces From a Performance Perspective: Application to Automated Classification of Polished Natural Stones

Bianconi, Francesco; Bello, Raquel; Fernández, Antonio; González, Elena

doi:10.1007/978-3-319-23222-5_9

Cited by 18 publications

(9 citation statements)

References 12 publications

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“…This is comprised of twenty-five classes of commercial polished stones [42,43]. Four samples per class (corresponding to as many tiles) were acquired under steady and controlled illumination conditions and at 10 in-plane rotation angles, i.e., 0…”

Section: Mondialmarmimentioning

confidence: 99%

Comparative Evaluation of Hand-Crafted Image Descriptors vs. Off-the-Shelf CNN-Based Features for Colour Texture Classification under Ideal and Realistic Conditions

et al. 2019

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Convolutional Neural Networks (CNN) have brought spectacular improvements in several fields of machine vision including object, scene and face recognition. Nonetheless, the impact of this new paradigm on the classification of fine-grained images—such as colour textures—is still controversial. In this work, we evaluate the effectiveness of traditional, hand-crafted descriptors against off-the-shelf CNN-based features for the classification of different types of colour textures under a range of imaging conditions. The study covers 68 image descriptors (35 hand-crafted and 33 CNN-based) and 46 compilations of 23 colour texture datasets divided into 10 experimental conditions. On average, the results indicate a marked superiority of deep networks, particularly with non-stationary textures and in the presence of multiple changes in the acquisition conditions. By contrast, hand-crafted descriptors were better at discriminating stationary textures under steady imaging conditions and proved more robust than CNN-based features to image rotation.

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Section: Mondialmarmimentioning

confidence: 99%

Comparative Evaluation of Hand-Crafted Image Descriptors vs. Off-the-Shelf CNN-Based Features for Colour Texture Classification under Ideal and Realistic Conditions

et al. 2019

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“…20 Accuracy (confidence interval for the mean) By contrast, the results were markedly lower (and well below 60%) when the data sets contained highly inhomogeneous images (such as STex and USPTex).…”

Section: Resultsmentioning

confidence: 94%

Experimental comparison of color spaces for material classification

Bello-Cerezo

Bianconi

Fernández

et al. 2016

J. Electron. Imaging

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This paper presents a comparison of color spaces for material classification. The study includes three device-independent (CIELAB, CIELUV, and CIE XYZ) and seven device-dependent spaces (RGB, HSV, YIQ, YUV, YC b C r , Ohta's I 1 I 2 I 3 , and RG-YeB-WhBl). The pros and cons of the different spaces and the procedures for converting color data among them are discussed in detail. An experiment based on 12 different image data sets was carried out to comparatively evaluate the performance of each space for material classification purposes. The results showed that CIELAB markedly outperformed the other spaces followed by HSV and CIELUV. Conversely, CIE XYZ came out as the worst performing space. Interestingly, no significant difference emerged among the performance of the other device-dependent spaces.

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“…Later, better results were obtained by using gray-scale texture classification, including cooccurrence matrices [ Most description approaches in the literature are simply based on texture features. Bianconi, Bello, Fernández, and González (2015) [43] addressed the problem of selecting the adequate color representation in granite grading. They joined the debate on different color spaces for granite classification by experimenting with simple color descriptors: mean, mean + standard deviation, mean + moments from second to fifth, quartiles and quintiles of each color channel.…”

Section: Literature Reviewmentioning

confidence: 99%

Sorting Speckled Granites (Nehbandan) and Measuring Their Surface Veining using Machine Vision

Moghaddam*¹,

Rajaei²,

Maraki³

2019

IJRTE

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Quality control and the appearance evaluation of stones are quite challenging in the industry today. The similar appearance of different stones containing the same minerals may result in economic losses, and if the customers fail to identify the type of slates delivered to them correctly, disagreements may arise between the buyers and granite vendors. This study is an attempt toward the automation of surface quality assessment of the Nehbandan(Iran) speckled granite and measurement of the surface patterns under fixed conditions using image processing techniques in order to classify the granite tiles based on their type and amount of impurities and veins. The experimental tests comparing the presented approach with the texture descriptors in the introduced dataset prove the efficiency of the proposed method and its applications under controlled conditions, including the classification of speckled granite tiles with different image resolutions.

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On Comparing Colour Spaces From a Performance Perspective: Application to Automated Classification of Polished Natural Stones

Cited by 18 publications

References 12 publications

Comparative Evaluation of Hand-Crafted Image Descriptors vs. Off-the-Shelf CNN-Based Features for Colour Texture Classification under Ideal and Realistic Conditions

Comparative Evaluation of Hand-Crafted Image Descriptors vs. Off-the-Shelf CNN-Based Features for Colour Texture Classification under Ideal and Realistic Conditions

Experimental comparison of color spaces for material classification

Sorting Speckled Granites (Nehbandan) and Measuring Their Surface Veining using Machine Vision

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