Image Processing using Color Space Models for Forensic Fiber Detection

Wetzer, Elisabeth; Lohninger, Hans

doi:10.1016/j.ifacol.2018.03.076

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Wetzer & Lohninger [ 26 ] described a computer algorithm to separate fibre structures from the background noise using grayscale images. Furthermore, the different colour channels in RGB images are used to segment colour images that enable automated search.…”

Section: Instrumental Methodsmentioning

confidence: 99%

Interpol review of fibres and textiles 2016–2019

Lepot

Lunstroot

Wael

2020

Forensic Science International: Synergy

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Section: Instrumental Methodsmentioning

confidence: 99%

Interpol review of fibres and textiles 2016–2019

Lepot

Lunstroot

Wael

2020

Forensic Science International: Synergy

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“…Based on the CIE Lab colorimetric system, DE was used to determinate the color difference between the standard sample and the experimental samples. [31][32][33] The formula is as in equation (5):…”

Section: Characterization Of the Natural Indigo Dyed Cotton Fibersmentioning

confidence: 99%

“…It can be observed that the weight of the centrifuged impurity quickly increased with the increase of the speed and time in the beginning, then it slightly decreased. [32][33][34][35][36] The weight reaches the maximum when the speed is 7500 r/min and the time is 4 min, at which point the mass of centrifuged solid accounts for 88.2% of the weight of indigo dye. Therefore, appropriate centrifugal treatment can effectively remove this part of the impurities.…”

Section: Effect Of Centrifugation On Impurities Removalmentioning

confidence: 99%

The improvement of spinnability of natural indigo dyed cotton fiber using centrifugation pretreatment

Sun

Han

Liu³

et al. 2022

Textile Research Journal

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Natural indigo is a traditional dye for textiles. However, a large number of impurities on the surface of dyed fibers have been reported, which leads to poor friction and electrical performance, and exerts a negative impact on the spinning of the fibers. In this paper, a centrifugation pretreatment of the leuco indigo dye liquid was reported to remove the impurities, and the centrifuged dye was then applied to cotton fiber. The results revealed that centrifugation could effectively remove 88.2% of solid impurities, and the fiber after being dyed showed a clean and smooth surface. There was almost no chromatic aberration compared with the original fibers that were dyed with the noncentrifuged dye solution, and the optimized value of the color difference was less than 0.5. The friction and electrical properties of the fibers were improved. There was significantly reduced dust observed during the spinning process of the dyed fibers, and the spinnability of the fibers was improved. The coefficient of variation of single yarn decreased from 28.35% to 21.19%.

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“…Various image processing techniques are employed at different stages of conversion of a raw image ( viz image acquisition, image preprocessing, clustering, and classification), into making it more useful for extracting information [ 15 ]. Techniques such as color space, principal component analysis (PCA), and pattern recognition have been used for image processing in various medical fields [ 16 – 18 ].…”

Section: Introductionmentioning

confidence: 99%

Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study

Silva

Chellappan

Karunaweera

2021

Computational and Mathematical Methods in Medicine

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Skin lesions are a feature of many diseases including cutaneous leishmaniasis (CL). Ulcerative lesions are a common manifestation of CL. Response to treatment in such lesions is judged through the assessment of the healing process by regular clinical observations, which remains a challenge for the clinician, health system, and the patient in leishmaniasis endemic countries. In this study, image processing was initially done using 40 CL lesion color images that were captured using a mobile phone camera, to establish a technique to extract features from the image which could be related to the clinical status of the lesion. The identified techniques were further developed, and ten ulcer images were analyzed to detect the extent of inflammatory response and/or signs of healing using pattern recognition of inflammatory tissue captured in the image. The images were preprocessed at the outset, and the quality was improved using the CIE L ∗ a ∗ b color space technique. Furthermore, features were extracted using the principal component analysis and profiled using the signal spectrogram technique. This study has established an adaptive thresholding technique ranging between 35 and 200 to profile the skin lesion images using signal spectrogram plotted using Signal Analyzer in MATLAB. The outcome indicates its potential utility in visualizing and assessing inflammatory tissue response in a CL ulcer. This approach is expected to be developed further to a mHealth-based prediction algorithm to enable remote monitoring of treatment response of cutaneous leishmaniasis.

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Image Processing using Color Space Models for Forensic Fiber Detection

Cited by 7 publications

References 4 publications

Interpol review of fibres and textiles 2016–2019

Interpol review of fibres and textiles 2016–2019

The improvement of spinnability of natural indigo dyed cotton fiber using centrifugation pretreatment

Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study

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