Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-I

Nibedita Rout, George Baciu, Priyabrata Pattanaik, K. Nakkeeran, Asimananda Khandual* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

Numerous imaging applications and analyses demand human perception, and color space transformation of device-dependent tri-band color interpretation (RGB) to device-independent CIE color space standards needs human intervention. The imaging acquisition environment, theoretical conversion errors, viewing geometry, well-defined illumination uniformity, and calibration protocols limit their precision and applicability. It is unfortunate that in most image processing applications, the spectral data are either unavailable or immeasurable. This study is based on developing a novel integrating sphere imaging system and experimentation with textiles’ controlled variation of texture and color. It proposes a simple calibration technique and describes how unique digital color signatures can be derived from calibrated RGB derivatives to extract the best features for color and texture. Additionally, an alter-ego of reflectance function, missing in the imaging domain, is suggested that could be helpful for visualization, identification, and application for qualitative and quantitative color-texture analysis. Our further investigation revealed promising colorimetric results while validating color characterization and different color combinations over three textures.
Original languageEnglish
Article number3887
Number of pages14
JournalElectronics (Switzerland)
Volume11
Issue number23
Early online date24 Nov 2022
DOIs
Publication statusPublished - 24 Nov 2022

Bibliographical note

Funding
This research received no external funding.
Acknowledgments
We are also grateful to Manas Sarkar, ITC, HKPU for providing cotton samples with varied textures and Dystar, Hong Kong, for generously providing us with dye samples. We are thankful to for the experimental support from new fiber science and IoT Lab, OUTR sponsored by TEQIP-3 seed money and MODROB (/9-34/RIFDMO DPOLICY-1/2018-19).

Data Availability Statement

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/electronics11233887/s1; The images of the sample are provided in the Supplementary File, captured by the said calibrated imaging system. They are titled Figure S1. Proximity textures: 20 kinds of fabric from the same cotton fibers(texture was only slightly varied by spinning the yarn and weaving the fabric); Figure S2: red, cyan, blue, yellow samples; Figure S3. System set-up and calibrationThe corresponding experimental readings in the spectral and imaging systems were provided as follows: Table S1. Calibrated RGB readings of 20 fabric samples; Table S2. %R reflectance and CIE XYZ, L*a*,b* readings of 20 fabric samples; Table S3. Calibrated RGB readings of red, cyan, blue, and yellow fabric samples; Table S4. %R reflectance readings of red, cyan, blue, and yellow samples.

Keywords

  • computer vision
  • CIE color space
  • color image processing
  • radiance
  • tri-stimulus value
  • d/80 geometry
  • integrating sphere imaging

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