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Evaluation of Goat Leather Quality Based on Computational Vision Techniques

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Abstract

The evaluation of finished (i.e., chemically treated) goat/sheep leather can be highly subjective, resulting in disagreements that can eventually lead to the interruption of production programs in the tannery and leather industry. As a result, much research has been carried out in the leather industry aiming at developing an automated system to evaluate goat/sheep leather. In this paper, a computational vision system is proposed in order to classify the quality of the leather automatically. Initially, three filtering steps are used to segment the region of interest (ROI). After highlighting the ROI, the Haralick texture obtained from the gray level co-occurrence matrix is extracted. Some descriptions (e.g., Haralick) are used in this work, namely energy, homogeneity, contrast, cluster tendency, cluster shade, correlation, information measures of correlation, and maximal correlation coefficient. After that, the performances of machine learning algorithms, such as the Naive Bayes classifier, classifier optimum-path forest and support vector machines are compared. The hit rate of the results to automatically classify goat leather quality was similar to other approaches in the literature. However, the proposed system used ten attributes, six less than the best approach found in the literature and in shorter processing time. In summary, the proposed methodology was considered reliable to automatically classify goat leather quality, as it had an accuracy of 93.22% and total processing time was 3.78 s.

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Notes

  1. Notation

    • p(i, j): (i, j) entry in a normalized gray-tone spatial-dependence matrix, \(= P(i,j)/R\).

    • \(p_{x}(i)\): ith entry in the marginal-probability matrix obtained by summing the rows of p(i, j), = \(\sum _{j=1}^{N_g}P(i,j)\).

    • \(N_g\): number of distinct gray levels in the quantized image.

    • \(p_y(j)\): jth entry in the marginal-probability matrix obtained by summing the lines of p(i, j), = \(\sum _{i=1}^{N_g}P(i,j)\).

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Acknowledgements

This work was supported in part by Brazilian National Council for Research and Development (CNPq) via the Grant Nos. 301928/2014-2, 309335/2017-5, 309451/2015-9, 304315/2017-6 and 430274/2018-1; by the Federal Institute of Education, Science and Technology of Ceara via grants PROINFRA/2017 and PROINFRA PPG/2017; and by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

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Authors and Affiliations

  1. Department of Teleinformatics Engineering, Center of Technology, Federal University of Ceará (UFC), Campus of Pici, Fortaleza, CE, Brazil

    Edmilson Q. Santos Filho & Guilherme A. Barreto

  2. Laboratory of Image Processing, Signals and Applied Computing, Federal Institute of Ceará, Fortaleza, CE, Brazil

    Pedro Henrique Feijó de Sousa & Pedro P. Rebouças Filho

  3. Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza, CE, Brazil

    Victor Hugo C. de Albuquerque

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  1. Edmilson Q. Santos Filho
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  5. Victor Hugo C. de Albuquerque
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Correspondence to Victor Hugo C. de Albuquerque.

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Santos Filho, E.Q., de Sousa, P.H.F., Rebouças Filho, P.P. et al. Evaluation of Goat Leather Quality Based on Computational Vision Techniques. Circuits Syst Signal Process 39, 651–673 (2020). https://doi.org/10.1007/s00034-019-01180-4

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