Leather species identification based on surface morphological characteristics using image analysis technique / Malathy Jawahar in JOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION (JALCA), Vol. CXI, N° 8 (08/2016)  

[article]  inJOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION (JALCA) > Vol. CXI, N° 8 (08/2016) . - p. 308-314 Titre : Leather species identification based on surface morphological characteristics using image analysis technique Type de document : texte imprimé Auteurs : Malathy Jawahar, Auteur ; K. Vani, Auteur ; Chandra Babu Narasimhan Kannan, Auteur Année de publication : 2016 Article en page(s) : p. 308-314 Note générale : Bibliogr. Langues : Américain (ame) Catégories : Analyse d'image L'analyse d'image est la reconnaissance des éléments contenus dans l'image. Il ne faut pas confondre analyse (décomposition en éléments) et traitement (action sur les composantes) de l'image. Cuir -- Identification Cuirs et peaux -- Analyse Imagerie (technique) Morphologie (matériaux) Index. décimale : 675 Technologie du cuir et de la fourrure Résumé : Identification and classification of leathers based on the species becomes valuable and necessary due to concerns regarding consumer protection, product counterfeiting or authenticity issues, and dispute settlement in the leather industry and thus helping in trading standards and protecting endangered species. This is carried out mostly by microscopical examination, though, the use of DNA fingerprinting is a theoretical possibility. Identification of leather species through hair pore pattern by microscopical examination requires expertise, training and experience, and due to involvement of human judgment, subjectivity is inevitable. Recent advancements in instrumental techniques aided image analysis offers a good scope for standardizing objective criteria for species identification. In this study, an automatic recognition of leather species based on the surface hair pore pattern using image processing techniques has been investigated. The signature or distinctive feature of leathers from each of the important raw materials dealt within the leather industry were defined from SEM images using image processing technique in terms of number of hair pores, pore density, type and size of the pores (in terms of area, diameter), inter-pore distance and shape of the pore (in terms of circularity, roundness perimeter). Results of the image analysis revealed that all the four common raw materials have distinctive features in terms of specific parameters. Buffalo leather is characterized by the least pore density, largest pore (11110 sq ìm) and highest inter-pore distance (103sq ìm) whereas sheep skin has the smallest hair pore (200 sq ìm) with lowest porosity. Goat skin has both medium (1256 sq ìm) and small sized pores (364 sq ìm) arranged in clusters with trios pattern arrangement and cow leather is characterized by large hair pore density (2262/sq cm) with more uniform mid-sized pores (2190 sq ìm). Thus the developed image processing technique using the current state of art has the potential to provide quantitative estimates of the leather surface morphological characteristics in terms of quantifiable parameters to identify the animal origin. Note de contenu : - Fundamentals of image processing - MATERIALS AND METHODS : Materials - SEM studies - Methods - Pre-processing & special calibration - Image enhancement - Image segmentation - Feature extraction - RESULTS AND DISCUSSION : Mean pore area - Least inter-pore distance - Hair pore density - Percent porosity/porous area fraction - Hair pore shape attributes En ligne : https://drive.google.com/file/d/1VoEoX0Ccediv88f3o1s2tPzrZqos-k09/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=26896 [article]

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Machine vision inspection system for detection of leather surface defects / Malathy Jawahar in JOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION (JALCA), Vol. CXIV, N° 1 (01/2019)  

[article]  inJOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION (JALCA) > Vol. CXIV, N° 1 (01/2019) . - p. 10-19 Titre : Machine vision inspection system for detection of leather surface defects Type de document : texte imprimé Auteurs : Malathy Jawahar, Auteur ; K. Vani, Auteur ; Chandra Babu Narasimhan Kannan, Auteur Année de publication : 2019 Article en page(s) : p. 10-19 Note générale : Bibliogr. Langues : Américain (ame) Catégories : Analyse d'image L'analyse d'image est la reconnaissance des éléments contenus dans l'image. Il ne faut pas confondre analyse (décomposition en éléments) et traitement (action sur les composantes) de l'image. Cuir Cuirs et peaux -- Défauts -- Classification Cuirs et peaux -- Texture Détection de défauts (Ingénierie) Imagerie (technique) Qualité -- Contrôle Réseaux neuronaux (informatique) Index. décimale : 675 Technologie du cuir et de la fourrure Résumé : Leather quality inspection is very important in assessing the effective cutting value that can be obtained from the leather. Current practice involves an expert to inspect each piece of leather individually and detect defects manually. However, such a manual inspection is highly subjective and varies quite considerably from one assessor to another. Often this subjectivity leads to dispute between the buyer and the seller of the leathers and hence attempts are made to automate this. Automatic leather defect classification is a challenging research problem due to the difficulties that arise when segmenting defects from the leather background and determining the characteristics that describe the defects objectively. The present study describes application of machine vision system to capture leather surface images and the novel multi-level thresholding algorithm to segment defective and non-defective regions of leather followed by texture feature extraction to objectively quantify the leather surface defects. A dataset consisting of 90 leather images comprising 20 good leather and 50 defective samples has been used in the study. Experimental results on the leather defect image library database achieved an accuracy of 90% using neural network as classifier, confirming potential of using the proposed system for automatic leather defect classification. Note de contenu : - Materials - Image acquisition system - Image analysis - Image segmentation - Feature extraction using texture analysis - Defect classification using artificial neural network En ligne : https://drive.google.com/file/d/19bCDm5My0DozDCYyRxrzlQuQkzRwxUhI/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=31549 [article]

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