Evaluation and consistency calibration of hyperspectral imaging system based on liquid crystal tunable filter for fabric color measurement

Polarized hyperspectral images can reflect the rich physicochemical characteristics of targets. Meanwhile, the contained plentiful information also brings great challenges to signal processing. Although compressive sensing theory provides a good idea for image processing, the simplified compression imaging system has difficulty in reconstructing full polarization information. Focused on this problem, we propose a two-step reconstruction method to handle polarization characteristics of different scales progressively. This paper uses a quarter-wave plate and a liquid crystal tunable filter to achieve full polarization compression and hyperspectral imaging. According to their numerical features, the Stokes parameters and their modulation coefficients are simultaneously scaled. The first Stokes parameter is reconstructed in the first step based on compressive sensing. Then, the last three Stokes parameters with similar order of magnitude are reconstructed in the second step based on previous results. The simulation results show that the two-step reconstruction method improves the reconstruction accuracy by 7.6 dB for the parameters that failed to be reconstructed by the non-optimized method, and reduces the reconstruction time by 8.25 h without losing the high accuracy obtained by the current optimization method. This feature scaling method provides a reference for the fast and high-quality reconstruction of physical quantities with obvious numerical differences.

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Camouflage Target Identification Based on Hyperspectral Imaging System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.135-136.341 ◽

2011 ◽

Vol 135-136 ◽

pp. 341-346

Author(s):

Na Ding ◽

Jiao Bo Gao ◽

Jun Wang

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Imaging System ◽

Target Identification ◽

Spectral Characteristics ◽

Tunable Filter ◽

Fusion Algorithm ◽

Infrared Band ◽

Target Segmentation ◽

Pseudo Color

A novel system of implementing target identification with hyperspectral imaging system based on acousto-optic tunable filter (AOTF) was proposed. The system consists of lens, AOTF, AOTF driver, CCD and image collection installation. Owing to the high spatial and spectral resolution, the system can operate in the spectral range from visible light to near infrared band. An experiment of detecting and recognizing of two different kinds of camouflage armets from background was presented. When the characteristic spectral wave bands are 680nm and 750nm, the two camouflage armets exhibit different spectral characteristic. The target camouflage armets in the hyperspectral images are distinct from background and the contrast of armets and background is increased. The image fusion, target segmentation and pick-up of those images with especial spectral characteristics were realized by the Hyperspectral Imaging System. The 600nm, 680nm, and 750nm images were processed by the Pseudo color fusion algorithm, thus the camouflage armets are more easily observed by naked eyes. Experimental results confirm that AOTF hyperspectral imaging system can acquire image of high contrast, and has the ability of detecting and identification camouflage objects.

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Color segmentation and extraction of yarn-dyed fabric based on a hyperspectral imaging system

Textile Research Journal ◽

10.1177/0040517520957401 ◽

2020 ◽

pp. 004051752095740

Author(s):

Zhang Jianxin ◽

Zhang Kangping ◽

Wu Junkai ◽

Hu Xudong

Keyword(s):

Hyperspectral Imaging ◽

Clustering Algorithm ◽

Imaging System ◽

Color Difference ◽

Woven Fabrics ◽

Woven Fabric ◽

Color Segmentation ◽

Color Measurement ◽

Watershed Algorithm ◽

Density Peak

For multi-color yarn-dyed fabrics which are cross-woven by yarns with different colors, the different colors cannot be directly measured by a traditional spectrophotometer because it can only obtain the average color of solid-color sample in the limited aperture. In this paper, a novel method for color segmentation and extraction for multi-color yarn-woven fabrics based on a Hyperspectral Imaging System (HIS) was proposed. First, the multi-color yarn-woven fabric images were acquired with the HIS. Then a space transformation based on Fréchet distance was used to transform the pre-processed hyperspectral fabric images into gray images, and then an improved watershed algorithm was used to segment the transformed gray images into different color regions. Finally, to solve the problems of over-segmentation with the improved watershed algorithm, an improved k-means clustering algorithm was adopted to merge the over-segmented color regions. The experimental results on four multi-color yarn-woven fabrics showed that the color segmentation accuracy of the proposed method outperformed the ordinary k-means, Fuzzy C-means (FCM), and Density peak cluster (DPC) algorithms on evaluation indexes of compactness (CP) and separation (SP), and the execution efficiency was improved by at least 55%. Furthermore, the color difference between the proposed method and the spectrophotometric measurements ranged from 0.60 to 0.88 CMC (2:1) (Color Measurement Committee) units, which almost satisfied the accuracy of color measurement.

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Multi-color measurement of printed fabric using the hyperspectral imaging system

Textile Research Journal ◽

10.1177/0040517519883953 ◽

2019 ◽

Vol 90 (9-10) ◽

pp. 1024-1037

Author(s):

Jianxin Zhang ◽

Junkai Wu ◽

Xudong Hu ◽

Xinen Zhang

Keyword(s):

Hyperspectral Imaging ◽

Imaging System ◽

Optimal Number ◽

Color Segmentation ◽

Color Measurement ◽

Self Organizing Map ◽

Density Peaks ◽

Silhouette Index ◽

Som Algorithm ◽

Density Peaks Clustering

Printed fabrics usually have multiple colors and intricate patterns, which make it difficult to directly measure the colors of the printed fabrics with a traditional spectrophotometer. However, a hyperspectral imaging system (HIS) can measure multiple colors since it acquires the spectral reflectance of a continuous band at every point of the fabric. For multiple-color printed fabrics, color segmentation is also very important. In this paper, color measurement of printed fabrics using the HIS was implemented; an algorithm which combines the self-organizing map (SOM) algorithm and the density peaks clustering (DPC) algorithm was then proposed to automatically determine the number of colors on the printed fabric and accurately segment the color regions for measurement. Firstly, the SOM algorithm was used to identify the main clusters, the DPC algorithm with Silhouette Index was then used to identify the optimal number of colors and merge the clusters. Experimental results show that this algorithm not only automatically determines the optimal number of colors for printed fabric and achieves accurate color segmentation, but requires less time for execution.

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