Leather texture classification using wavelet feature extraction technique

Jawahar, Malathy; Babu, N. K. Chandra; Vani, K.

doi:10.1109/iccic.2014.7238475

Cited by 18 publications

(19 citation statements)

References 10 publications

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“…They achieved normalized energy of 96.2% and claimed realtime performance. Jawahar et al gave a feature extraction method for leather defects identification using wavelet feature extraction [10]. Wavelet Co-occurrence Feature (WCF) and Wavelet Statistical Feature (WSF) were used as feature extractors.…”

Section: B Wavelet Based Methodsmentioning

confidence: 99%

“…The outstanding performance of these techniques can also be attributed to their high dimensional feature sets such as the 139-dimensional attribute set in the work of Viana et al [13] and a 66-dimensional feature set employed by the technique of Pistori et al [12]. The technique of Jawahar et al [10] employed high dimensional Wavelet based statistical and co-occurrence features to tackle the problem of leather defect classification. The high dimensional representation in the feature space enabled the SVM classifier with a Gaussian kernel to achieve a test accuracy of 98.8% on 200 leather images.…”

Section: Comparative Analysis Of Image Processing Based Methods For Leather Defect Detectionmentioning

confidence: 99%

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On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey

et al. 2019

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Reliably and effectively detecting and classifying leather surface defects is of great importance to tanneries and industries that use leather as a major raw material such as leather footwear and handbag manufacturers. This paper presents a detailed and methodical review of the leather surface defects, their effects on leather quality grading and automated visual inspection methods for leather defect inspection. A detailed review of inspection methods based on leather defect detection using image analysis methods is presented, which are usually classified as heuristic or basic machine learning based methods. Due to the recent success of deep learning methods in various related fields, various architectures of deep learning are discussed that are tailored to image classification, detection, and segmentation. In general, visual inspection applications, where recent CNN architectures are classified, compared, and a detailed review is subsequently presented on the role of deep learning methods in leather defect detection. Finally, research guidelines are presented to fellow researchers regarding data augmentation, leather quality quantification, and simultaneous defect inspection methods, which need to be investigated in the future to make progress in this crucial area of research.

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Section: B Wavelet Based Methodsmentioning

confidence: 99%

Section: Comparative Analysis Of Image Processing Based Methods For Leather Defect Detectionmentioning

confidence: 99%

On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey

et al. 2019

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“…al. has provided a feature extraction method for the identification of leather defects using a wavelet feature extraction [7]. Wavelet Cooccurrence Feature (WCF) and Wavelet Statistical Feature (WSF) were used as a feature extractor.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many studies have been reported to date that perform the task of grading leather samples based on the detected defects. Almost all previous methods rely on traditional machine learning methods that employ feature engineering from the textural domain [1]- [3] commonly classified as texture-based methods, while a few rely on transform domain [4]- [7] for feature engineering, including wavelet based methods. Despite achieving reputable performance, the limited generalization ability of the hand-crafted features hinder their performance to meet the practical requirements of industrial leather classification.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings

et al. 2020

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Leather defect analysis is important for leather quality grading which directly effects the leather exports. Automated leather sample classification is vital due to slow and subjective nature of the manual process. The major challenges that exist in visual inspection of leather samples for categorization are: the morphology of defects significantly differs and their close examples are not available for transfer learning, unavailability of publicly available data and a benchmark. In this paper, we discuss three important aspects in the identification of industrial leather defects, i.e. the creation of an annotated wet-blue leather image dataset, the transfer of information from different domains to the leather image domain and the design of ensemble networks tailored to the task. We are therefore introducing a new database of wet-blue leather images (Wet-blue Leather Image Dataset (WBLID)) for the classification of defects along with expert annotation data. We proposed a new network EfficientNet-B3+ResNext-101. The proposed EfficientNet-B3+ResNext-101 ensemble architecture significantly outperforms all other state-of-the-art methods in terms of AUC and F1-score.

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“…The vegetable tanned process is also possible to coloring the leather into any colors. Tanned leather certainly has not a cheap price, it depends on the tanning method and done by the people who are experts in the tanning process [4]. The expensive price of the tanned leather become a reason for the production of synthetic or imitation leather.…”

mentioning

confidence: 99%

Tanned and Synthetic Leather Classification Based on Images Texture with Convolutional Neural Network

Faiz

Azhari

2020

Kno. Eng. Da. Sc.

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Tanned leather is an output from complex processes called tanning. Leather tanning is an important step that used to protect the fiber or protein structure of animal’s skin. Another reason of tanning process is to prevent the animal’s skin from any defect or rot. After the tanning is complete, the leather can be applied to produce a wide variety of leather products. Thus, the leather prices usually more expensive because it takes longer time in process. Another way to get cheaper price is make non-animal leather that usually known as synthetic or imitation leather. The purpose of this paper is to classify the tanned leather and synthetic leather by using Convolutional Neural Network. The tanned leather consist of cow, goat and sheep leathers. The proposed method will classify into four class, they are cow, goat, sheep and synthetic leathers. In each class consist of 160 images with 448x448 pixels size as the input data. With CNN method, this research shows a good result for the accuracy about 92.1%.

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Leather texture classification using wavelet feature extraction technique

Cited by 18 publications

References 10 publications

On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey

On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey

Ensemble Convolutional Neural Networks With Knowledge Transfer for Leather Defect Classification in Industrial Settings

Tanned and Synthetic Leather Classification Based on Images Texture with Convolutional Neural Network

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