Establishing a Demerit Count Reference Standard for the Classification and Grading of Leather Hides

Yeh, Chih-Shen; Perng, Der‐Baau

doi:10.1007/s001700170016

Cited by 28 publications

(14 citation statements)

References 9 publications

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“…The industrial applications utilize the pattern recognition methods to yield better classification results with less computational complexity. In industrial applications, automatic grading of products namely ceramic tile [5], fabric [6], leather [7] and paper [8] have been discussed. Wood and granite are widely used industry products for the development of automatic machine vision system to identify the quality of the product.…”

Section: Introductionmentioning

confidence: 99%

Automatic Wood Classification using a Novel Color Texture Features

Shivashankar¹,

Kagale²

2018

IJCA

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A variety of texture classification approaches have been reported in the literature but many of them are focused on gray-scale textures. The aim of this work is to develop a novel color texture features by constructing a histogram based on the combined intensity and color channel information to effectively classify color texture images. Five features are computed from the histogram bin values to reduce the computational complexity. Experiments are conducted on a set of 164 color texture images from VisTex database. The K-Nearest Neighbor (K-NN) classification method is used as a classifier. The classification results are encouraging to use the proposed scheme with reduction in features. Further the proposed scheme is used in automatic wood classification to show the usefulness of the proposed scheme in industrial applications.

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Section: Introductionmentioning

confidence: 99%

Automatic Wood Classification using a Novel Color Texture Features

Shivashankar¹,

Kagale²

2018

IJCA

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“…A machine-vision-based approach to grading leather hides for footwear industry is proposed in Hoang and Nachimuthu (1996); tools for automatically inspecting leather surfaces have been proposed so far in literature (Branca et al, 1996;Hoang et al, 1997;Fuqiang et al, 2006;Yeh and Perng, 2001;Neto et al, 2005;Pistori et al, 2007) with the aim of detecting scars, mite nests, warts and other 'defects'. 'In recent years, the market has been flooded with several commercial nesting software with different capabilities' (Elamvazuthi et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A computer-based system for dyeing leather patch edges

Volpe

Furferi

Governi

2014

IJCAET

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Abstract:The visual appearance of seamless dyed edges of luxury leather goods represents a key issue in terms of quality grading since a high-quality leather has to be characterised by homogeneously coloured and shaped edges with uniform ink thickness. Despite a huge literature produced by scientific and technical community to automate many leather manufacturing processes, since leather patches are often characterised by a free-form shape, any attempt of automating leather edges dyeing produced unsatisfactory and inaccurate results. In order to overcome the drawbacks of the existing approaches, the main objective of the present work is to provide a computer-based system for automatically dyeing leather patches edges. The described system includes: 1) a machine vision (MV) hardware equipment, consisting of both illumination and a high resolution acquisition device, devoted to patches edge detection; 2) a pantograph whose dyeing tool is moved along leather edges; 3) a series of computer-based methods for the automatic extraction of the leather patches outlines. Extensive testing performed using the developed machine demonstrated its effectiveness in delivering fast, automatic and high quality edge finishing in a reliable and repeatable way.

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“…Therefore, classification procedure is the most important in automation system of tanning leather production because it is directly affects the price of final tanning leather products. Furthermore, a high return rate and disputes between customer and manufacturing industry which caused by failure in classification of leather usually cause additional costs [4].…”

Section: Introductionmentioning

confidence: 99%

“…Leather defect can be categorized into five types: lines, holes, stains, wears, and knots [3]. To locate the defects, researchers use morphological operation [4], [5], clustering [6], [7], or machine learning approach [3], [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Pre-Trained Convolutional Neural Network for Classification of Tanning Leather Image

Winiarti¹,

Prahara²,

Murinto³

et al. 2018

ijacsa

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Abstract-Leather craft products, such as belt, gloves, shoes, bag, and wallet are mainly originated from cow, crocodile, lizard, goat, sheep, buffalo, and stingray skin. Before the skins are used as leather craft materials, they go through a tanning process. With the rapid development of leather craft industry, an automation system for leather tanning factories is important to achieve large scale production in order to meet the demand of leather craft materials. The challenges in automatic leather grading system based on type and quality of leather are the skin color and texture after tanning process will have a large variety within the same skin category and have high similarity with the other skin categories. Furthermore, skin from different part of animal body may have different color and texture. Therefore, a leather classification method on tanning leather image is proposed. The method uses pre-trained deep convolution neural network (CNN) to extract rich features from tanning leather image and Support Vector Machine (SVM) to classify the features into several types of leather. Performance evaluation shows that the proposed method can classify various types of leather with good accuracy and superior to other state-of-the-art leather classification method in terms of accuracy and computational time.

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Establishing a Demerit Count Reference Standard for the Classification and Grading of Leather Hides

Cited by 28 publications

References 9 publications

Automatic Wood Classification using a Novel Color Texture Features

Automatic Wood Classification using a Novel Color Texture Features

A computer-based system for dyeing leather patch edges

Pre-Trained Convolutional Neural Network for Classification of Tanning Leather Image

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