3D Plant Modelling Using Spectral Data From Visible to Near Infrared Range

2018 ◽  
pp. 1904-1925
Author(s):  
Ali Zia ◽  
Jie Liang
Keyword(s):  
Hyperspectral Imaging ◽  
Material Properties ◽  
Near Infrared ◽  
Infrared Range ◽  
Plant Surface ◽  
Imaging Device ◽  
Plant Phenomics ◽  
Physical Traits ◽  
Plant Modelling ◽  
Near Infrared Range

Plant phenomics research requires different types of sensors employed to measure the physical traits of plant surface and to estimate the biomass. Of particular interests is the hyperspectral imaging device which captures wavelength indexed band images that characterize material properties of objects under study. This chapter introduces a proof of concept research that builds 3D plant model directly from hyperspectral images captured in a controlled lab environment. The method presented in this chapter allows fine structural-spectral information of an object be captured and integrated into the 3D model, which can be used to support further research and applications. The hyperspectral imaging has shown clear advantages in segmenting plant from its background and is very promising in generating comprehensive 3D plant models.

3D Plant Modelling Using Spectral Data from Visible to Near Infrared Range

2015 ◽  
pp. 273-294
Author(s):  
Ali Zia ◽  
Jie Liang
Keyword(s):  
Hyperspectral Imaging ◽  
Material Properties ◽  
Near Infrared ◽  
Infrared Range ◽  
Plant Surface ◽  
Imaging Device ◽  
Plant Phenomics ◽  
Physical Traits ◽  
Plant Modelling ◽  
Near Infrared Range

Plant phenomics research requires different types of sensors employed to measure the physical traits of plant surface and to estimate the biomass. Of particular interests is the hyperspectral imaging device which captures wavelength indexed band images that characterize material properties of objects under study. This chapter introduces a proof of concept research that builds 3D plant model directly from hyperspectral images captured in a controlled lab environment. The method presented in this chapter allows fine structural-spectral information of an object be captured and integrated into the 3D model, which can be used to support further research and applications. The hyperspectral imaging has shown clear advantages in segmenting plant from its background and is very promising in generating comprehensive 3D plant models.

scholarly journals Hyperspectral imaging of the degradation of meat and comparison with necrotic tissue in human wounds

2019 ◽  
Author(s):  
Amadeus Holmer ◽  
Christoph Homberger ◽  
Thomas Wild ◽  
Frank Siemers
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Objective Evaluation ◽  
Infrared Range ◽  
Spectral Features ◽  
Necrotic Tissue ◽  
Tissue Discrimination ◽  
Near Infrared Range ◽  
Imaging Setup

The objective evaluation of scattering tissue and the discrimination of tissue types is an issue that cannot be solved with colour cameras and image processing alone in many cases. Examples can be found in the determination of freshness and ageing of meat, and the discrimination of tissue types in food technology. In medical applications tissue discrimination is also an issue, e.g. in wound diagnostics. A novel hyperspectral imaging setup with powerful signal analysis algorithms is presented which is capable of addressing these topics. The spectral approach allows the chemical analysis of material and tissues and the measurement of their temporal change. We present a method of hyperspectral imaging in the visible-near infrared range which allows both the separation and spatial allocation of different tissue types in a sample, as well as the temporal changes of the tissue as an effect of ageing. To prove the capability of the method, the ageing of meat (slices of pork) was measured and, as a medical example, the application of the hyperspectral imaging setup for the recording of wound tissue is presented. The method shows the ability to discriminate the different tissue components of pork meat, and the ageing of the meat is observable as changes in spectral features. An additional result of our study is the fact that some spectral features, which seem to be typical for the ageing of the meat, are similar to those observed in the necrotic tissue from wound diagnostics in medicine.

scholarly journals Surface Investigation of Photo-Degraded Wood by Colour Monitoring, Infrared Spectroscopy, and Hyperspectral Imaging

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Giorgia Agresti ◽  
Giuseppe Bonifazi ◽  
Luca Calienno ◽  
Giuseppe Capobianco ◽  
Angela Lo Monaco ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Infrared Region ◽  
Short Wave ◽  
Infrared Range ◽  
Surface Chemical ◽  
Colour Changes ◽  
Short Wave Infrared ◽  
Near Infrared Range

The aim of this investigation is to study the changes occurring on the surface of poplar wood exposed to artificial irradiation in a Solar Box. Colour changes were monitored with a reflectance spectrophotometer. Surface chemical modifications were evaluated by measuring the infrared spectra. Hyperspectral imaging was also applied to study the surface wood changes in the visible-near infrared and the short wave infrared wavelength ranges. The data obtained from the different techniques were compared to find the possible correlations in order to evaluate the applicability of the Hyperspectral imaging to investigate wood modifications in a non-invasive modality. The study of colour changes showed an important variation due to photo-irradiation which is the greatest change occurring within the first 24 hours. Infrared spectroscopy revealed that lignin degrades mainly in the first 48 hours. Concerning Hyperspectral imaging, the spectral features in the visible-near infrared range are mainly linked to the spectral shape, whereas in the short wave infrared cellulose and lignin affect shape and reflectance levels. The proposed approach showed that a correlation can be established between colour variation and wood degradation in the visible-near infrared range; furthermore in the short wave infrared region surface chemical changes can be assessed.

scholarly journals Hyperspectral imaging for textile sorting in the visible–near infrared range

2019 ◽  
Author(s):  
Carolina Blanch-Perez-del-Notario ◽  
Wouter Saeys ◽  
Andy Lambrechts
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Hierarchical Classification ◽  
Infrared Range ◽  
Chemical Recycling ◽  
Textile Waste ◽  
Textile Materials ◽  
Material Discrimination ◽  
Sample Set ◽  
Near Infrared Range

Recycling of textile materials is becoming important due to the increasing amount of textile waste and its large environmental impact. The Resyntex project aims at dealing with this textile waste by enabling its chemical recycling. To do so, pure textile materials and blends need to be sorted first. In this paper we evaluate the suitability of hyperspectral imaging for pure and blend textile sorting. We also test the discrimination capacity between denim and non-denim textile, since this is required prior to the de-colouration processes. For this purpose, we use a line-scan sensor in the 450–950 nm range, since its cost, compactness and speed characteristics make it suitable for industrial deployment. To deal with the strong colour interference of the textile a hierarchical classification approach is proposed. The results on the available sample set show promising discrimination potential for material discrimination as well as for denim versus non-denim detection.

scholarly journals Semi-automatic decision-making process in histopathological specimens from Barrett’s carcinoma patients using hyperspectral imaging (HSI)

2020 ◽  
Vol 6 (3) ◽  
pp. 261-263
Author(s):  
Marianne Maktabi ◽  
Hannes Köhler ◽  
Claire Chalopin ◽  
Thomas Neumuth ◽  
Yannis Wichmann ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Cancer Diagnostics ◽  
Machine Learning Algorithms ◽  
Infrared Range ◽  
Decision Making Process ◽  
Multi Layer Perceptron ◽  
Classification Methods ◽  
Near Infrared Range

AbstractDiscrimination of malignant and non-malignant cells of histopathologic specimens is a key step in cancer diagnostics. Hyperspectral Imaging (HSI) allows the acquisition of spectra in the visual and near-infrared range (500-1000nm). HSI can support the identification and classification of cancer cells using machine learning algorithms. In this work, we tested four classification methods on histopathological slides of esophageal adenocarcinoma. The best results were achieved with a Multi-Layer Perceptron. Sensitivity and F1-Score values of 90% were obtained.

scholarly journals Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4940
Author(s):  
Qinlin Xiao ◽  
Xiulin Bai ◽  
Pan Gao ◽  
Yong He
Keyword(s):  
Neural Network ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Geographical Origin ◽  
Short Wave ◽  
Infrared Range ◽  
Radix Astragali ◽  
Origin Identification ◽  
Level Fusion ◽  
Near Infrared Range

Radix Astragali is a prized traditional Chinese functional food that is used for both medicine and food purposes, with various benefits such as immunomodulation, anti-tumor, and anti-oxidation. The geographical origin of Radix Astragali has a significant impact on its quality attributes. Determining the geographical origins of Radix Astragali is essential for quality evaluation. Hyperspectral imaging covering the visible/short-wave near-infrared range (Vis-NIR, 380–1030 nm) and near-infrared range (NIR, 874–1734 nm) were applied to identify Radix Astragali from five different geographical origins. Principal component analysis (PCA) was utilized to form score images to achieve preliminary qualitative identification. PCA and convolutional neural network (CNN) were used for feature extraction. Measurement-level fusion and feature-level fusion were performed on the original spectra at different spectral ranges and the corresponding features. Support vector machine (SVM), logistic regression (LR), and CNN models based on full wavelengths, extracted features, and fusion datasets were established with excellent results; all the models obtained an accuracy of over 98% for different datasets. The results illustrate that hyperspectral imaging combined with CNN and fusion strategy could be an effective method for origin identification of Radix Astragali.

scholarly journals Application of hyperspectral imaging and chemometrics for classifying plastics with brominated flame retardants

2019 ◽  
Author(s):  
Daniel Caballero ◽  
Marta Bevilacqua ◽  
José Amigo
Keyword(s):  
Hyperspectral Imaging ◽  
Flame Retardants ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Infrared Range ◽  
Safety Standards ◽  
Analysis Decision Tree ◽  
High Degree ◽  
Near Infrared Range

Most plastics need to incorporate flame retardants to meet fire safety standards requirements. The amount and the type of flame retardants can differ, so that in waste plastics a large variety of polymers and flame retardants can be found. The recycling of plastics containing flame retardants is increasing. However, only plastics of the same polymer type and the same additive content can be recycled together. Three models based on different chemometrics techniques applied to hyperspectral imaging in the near infrared range were developed [partial least square-discriminant analysis, decision tree (DT) and hierarchical model (HM)]. Optimal results were obtained for all classification techniques. HM shows the highest error at all levels due to the noisy spectra of the black plastics. However, DT classification gave outstanding results, considering that the sensitivity was higher than 0.9 in all cases. Thus, the application of DT with hyperspectral imaging could be used to sort plastic samples with respect to the type of polymer and the flame retardant used with a high degree of accuracy in an automated way. These findings are highly valuable for the plastic and waste management industries.

Coupling of Er light emissions to plasmon modes on In2O3: Sn nanoparticle sheets in the near-infrared range

2014 ◽  
Vol 105 (4) ◽  
pp. 041903 ◽  
Author(s):  
Hiroaki Matsui ◽  
Wasanthamala Badalawa ◽  
Takayuki Hasebe ◽  
Shinya Furuta ◽  
Wataru Nomura ◽  
...  
Keyword(s):  
Near Infrared ◽  
Infrared Range ◽  
Near Infrared Range ◽  
Sn Nanoparticle ◽  
Plasmon Modes

scholarly journals Fabrication of Dy2O3 Transparent Ceramics by Vacuum Sintering Using Precipitated Powders

2020 ◽  
Vol 7 (1) ◽  
pp. 6
Author(s):  
Dianjun Hu ◽  
Xin Liu ◽  
Ziyu Liu ◽  
Xiaoying Li ◽  
Feng Tian ◽  
...  
Keyword(s):  
Near Infrared ◽  
Optical Quality ◽  
Precipitation Method ◽  
Infrared Range ◽  
Transparent Ceramics ◽  
Vacuum Sintering ◽  
Hydrogen Carbonate ◽  
Ammonium Hydrogen ◽  
Liquid Precipitation ◽  
Near Infrared Range

As a kind of promising material for a Faraday isolator used in the visible and near infrared range, Dy2O3 transparent ceramics were prepared by vacuum sintering from the nano-powders synthesized by the liquid precipitation method using ammonium hydrogen carbonate as precipitant with no sintering aids. The synthesized precursor was calcinated at 950 °C–1150 °C for 4 h in air. The influences of the calcination temperature on the morphologies and phase composition of Dy2O3 powders were characterized. It is found that the Dy2O3 powder calcinated at 1000 °C for 4 h is superior for the fabrication of Dy2O3 ceramics. The Dy2O3 transparent ceramic sample prepared by vacuum sintering at 1850 °C for 10 h, and subsequently with air annealing at 1400 °C for 10 h, from the 1000 °C-calcined Dy2O3 powders, presents the best optical quality. The values of in-line transmittance of the optimal ceramic specimen with the thickness of 1.0 mm are 75.3% at 2000 nm and 67.9% at 633 nm. The Verdet constant of Dy2O3 ceramics was measured to be −325.3 ± 1.9 rad/(T·m) at 633 nm, about 2.4 times larger than that of TGG (Tb3Ga5O12) single crystals.

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