Spectral image database for observing the quality of Nordic sawn timbers

Hirvonen, Tapani; Orava, Joni; Penttinen, Niko; Luostarinen, Katri; Hauta-Kasari, Markku; Sorjonen, Mika; Peiponen, Kai–Erik

doi:10.1007/s00226-014-0655-y

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…The datasets in [ 35 , 36 ] consist of images of objects taken indoors. There are some datasets which consist of a specific category of objects, like paintings [ 42 ], leaves from apple tree [ 43 ], textiles [ 39 ], honey [ 41 ] and wood samples [ 44 ]. Most of the available datasets are sampled or re-sampled at 10 nm intervals, while some of them have different sampling intervals, e.g., [ 28 ] has

nm sampling step, while datasets in [ 41 , 43 ] have spectral sampling intervals of 6 nm and

nm, respectively.…”

Section: Comparison Of Hytexila With Existing Hyperspectral Datasementioning

confidence: 99%

HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

Khan

Mihoubi

Mathon

et al. 2018

Sensors

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We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

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nm sampling step, while datasets in [ 41 , 43 ] have spectral sampling intervals of 6 nm and

nm, respectively.…”

Section: Comparison Of Hytexila With Existing Hyperspectral Datasementioning

confidence: 99%

HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

Khan

Mihoubi

Mathon

et al. 2018

Sensors

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“…CIE XYZ tristimulus values are then derived from spectral radiances using CIE color matching functions. Hyperspectral databases of indoor, outdoor, natural, and man‐made scenes, with various types of illumination (daylight, artificial, mixed) exist . On request, the authors can provide images from their own database, mainly composed of large field‐of‐view scenes.…”

Section: Development Of the Color Graphicmentioning

confidence: 99%

“…Hyperspectral databases of indoor, outdoor, natural, and man-made scenes, with various types of illumination (daylight, artificial, mixed) exist. [29][30][31][32][33][34][35] On request, the authors can provide images from their own database, mainly composed of large field-of-view scenes. Images produced with a digital camera can also be used after colorimetric and radiometric calibration.…”

Section: Development Of the Color Graphicmentioning

confidence: 99%

A color graphic informing on the impact of electric lighting and coated glazing in complex architectural scenes

Cauwerts

Jost

2018

Color Research & Application

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The present work proposes a graphical indicator that provides descriptive information about the color content of architectural environments, and shows color shifts due to illuminant or glazing changes. This color graphic is in accord with representations recently developed in electric lighting to provide information on color rendering. Its originality lies in (1) including contextualization, (2) dividing the color space based on color naming, and (3) giving the user the opportunity to compare scenes with different correlated color temperatures. After describing the development steps of the color graphic, two case studies and three validation experiments are presented. While the application of the graphic highlights its interest in a building design process, the validation experiments identified potential improvements.

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“…), Norway spruce (Picea abies) and Scots pine (Pinus sylvestris). 24 In the study described here, an imaging spectrograph equipped with an NIR camera (900-1700 nm, ImSpector N17E, Spectral Imaging Ltd, Oulu, Finland, www.specim.com) was used to image the transverse face of discs (Pinus radiata) at a resolution of ca 1 mm 2 . The images were converted into two-dimensional (2D) maps to show the variance in chemical composition for tree cross sections using an NIR wood chemistry calibration.…”

Section: Introductionmentioning

confidence: 99%

Mapping Within-Stem Variation of Chemical Composition by near Infrared Hyperspectral Imaging

Thumm

Riddell

Nanayakkara

et al. 2016

Journal of Near Infrared Spectroscopy

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A near infrared (NIR) imaging spectrograph was used to generate maps of chemical composition distribution on the surface of transverse wood discs taken from tree stems. The measured chemical components were lignin, galactose, glucose and mannose as well as cellulose and hemicelluloses, which were calculated from monomeric sugars. These components were determined using NIR-based chemistry models, which had been developed specifically for the imaging spectrograph. Explained test-set variation for key constituents ranged between 60% (galactan) and 78% (lignin). Day-today variability was 1-2% (standard deviation/range) depending on the chemical property. Various operational parameters such as room temperature, sample temperature, sample surface preparation and sample thickness were found to have a non-negligible, but manageable, influence on predicted results. The influence of room and sample temperatures could be reduced by incorporating temperature changes into the chemistry model. Extractives, transported to, and concentrated at, the disc surface during drying, needed to be physically removed from the surface to avoid an unpredictable influence on chemical results. Wood fibre angles at the disc surface needed to be aligned in a consistent manner to the camera. NIR information was found to derive from a sample depth of up to 10 mm. This distance was consequently chosen as the minimal sample thickness.

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Spectral image database for observing the quality of Nordic sawn timbers

Cited by 9 publications

References 22 publications

HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

A color graphic informing on the impact of electric lighting and coated glazing in complex architectural scenes

Mapping Within-Stem Variation of Chemical Composition by near Infrared Hyperspectral Imaging

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