Identification and Classification of Animal Fibres Using Artificial Neural Networks

She, Fenghua; Chow, S.; Wang, Bo; Kong, Lingxue

doi:10.4188/jte.47.35

Cited by 9 publications

(8 citation statements)

References 1 publication

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“…In this study, we mainly discuss on deep‐learning architecture rather than classifier, so we use the simplest classifier to distinguish features. For measuring the similarity of histograms M and T in order to test image I T and model image I M , the NN classifier was used the distance method as chi‐square [11] as

D ()(, T, M) = \sum_{n = 1}^{N} \frac{{(T_{N} - M_{N})}^{2}}{T_{N} + M_{N}}

N determines the number of bins, at the n th bin T n and M n are the values of T and M . I T is a test image which is assigned to the class of I M .…”

Section: Materials and Methodologymentioning

confidence: 99%

“…A non‐linear artificial neural network (NANN) used to characterize mohair and merino with a non‐linear discrimination function instead of linear discrimination function. Results proved that the non‐linear discrimination function was better during the characterization process of mohair and merino [11]. In order to classify the animal‐based fibres, fuzzy‐based pattern recognition has been used [12].…”

Section: Introductionmentioning

confidence: 99%

“…The feature patterns have been obtained with the help of image processing. The ANN model has been used for classifying mohair and merino fibres [11]. Xing et al .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of wool and mohair fibres with texture feature extraction and deep learning

Yildiz

2020

IET Image Processing

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D ()(, T, M) = \sum_{n = 1}^{N} \frac{{(T_{N} - M_{N})}^{2}}{T_{N} + M_{N}}

N determines the number of bins, at the n th bin T n and M n are the values of T and M . I T is a test image which is assigned to the class of I M .…”

Section: Materials and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of wool and mohair fibres with texture feature extraction and deep learning

Yildiz

2020

IET Image Processing

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“…As shown in Figure 1, the patterns of a Merino fiber are visually different from those of a Mohair fiber. Scales of the Mohair fiber have distant margins, a regular diameter and irregular mosaics while scale edges of the Merino fiber are more likely to be parallel to each other [3].…”

Section: Methodsmentioning

confidence: 99%

Feature extraction for animal fiber identification

et al. 2002

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Fiber identification has been a very important task in many industries such as wool growing, textile processing, archaeology, histochernical engineering, and zoology. Over the years, animal fibers have been identified using physical and chemical approaches. Recently, objective identification of animal fibers has been developed based on the cuticular information of fibers. Effective and accurate extraction of representative features is essential to animal fiber identification and classification. In the current work, two different strategies are developed for this purpose. In the first method, explicit features are extracted using image processing. However, only implicit features are used in the second method with an unsupervised artificial neural network. It is found that the use of explicit features increases the accuracy of fiber identification but requires more effort on processing images and solid knowledge of what features are representative ones.

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“…Differences in physical and chemical properties are basis to identify textile fibers by various technologies. The common purpose of all technologies is to determine as distinctly as possible a fiber’s morphological, optical and chemical properties [ 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Using Spectral Imaging to Classify Dyes in Colored Fibers

Rahaman

Parkkinen

Hauta-Kasari

2020

Sensors

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In the field of cultural heritage, applied dyes on textiles are studied to explore their great artistic and historic values. Dye analysis is essential and important to plan correct restoration, preservation and display strategy in museums and art galleries. However, most of the existing diagnostic technologies are destructive to the historical objects. In contrast to that, spectral reflectance imaging is potential as a non-destructive and spatially resolved technique. There have been hardly any studies in classification of dyes in textile fibers using spectral imaging. In this study, we show that spectral imaging with machine learning technique is capable in preliminary screening of dyes into the natural or synthetic class. At first, sparse logistic regression algorithm is applied on reflectance data of dyed fibers to determine some discriminating bands. Then support vector machine algorithm (SVM) is applied for classification considering the reflectance of the selected spectral bands. The results show nine selected bands in short wave infrared region (SWIR, 1000–2500 nm) classify dyes with 97.4% accuracy (kappa 0.94). Interestingly, the results show that fairly accurate dye classification can be achieved using the bands at 1480nm, 1640 nm, and 2330 nm. This indicates possibilities to build an inexpensive handheld screening device for field studies.

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Identification and Classification of Animal Fibres Using Artificial Neural Networks

Cited by 9 publications

References 1 publication

Identification of wool and mohair fibres with texture feature extraction and deep learning

Identification of wool and mohair fibres with texture feature extraction and deep learning

Feature extraction for animal fiber identification

A Novel Approach to Using Spectral Imaging to Classify Dyes in Colored Fibers

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