An optoelectronic sensor system for industrial multipoint and imaging spectrometry

Herrala, Esko; ̈rinen, Timo Hyva; Voutilainen, Osmo; Lammasniemi, Jorma

doi:10.1016/s0924-4247(97)80283-x

Cited by 19 publications

(14 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Raw material on-line characterization and discrimination processes are a key issue in several agricultural Xin, 2005) and industrial sectors (Reich, 2008;Herrala, 1997;GarciaAllende, 2008c). Technologies for quality evaluation detect physical properties which correlate well with certain quality factors of the products such as density, firmness, vibrational characteristics, X-ray and gamma ray transmission, optical reflectance and transmission, electrical properties and nuclear magnetic resonance.…”

Section: His Applied To Industry Processesmentioning

confidence: 99%

Hyperspectral Imaging for Raw Material Sorting and Processed Product Quality Control

García-Allende¹,

Conde²,

López‐Higuera³

2011

Applications and Experiences of Quality Control

View full text Add to dashboard Cite

Section: His Applied To Industry Processesmentioning

confidence: 99%

Hyperspectral Imaging for Raw Material Sorting and Processed Product Quality Control

García-Allende¹,

Conde²,

López‐Higuera³

2011

Applications and Experiences of Quality Control

View full text Add to dashboard Cite

“…When only two classes are present, (1) is reduced to (2) where and denote the number of spectra of the two classes and denotes the variance of class on the th component. After component variability calculation, the selected eigenvectors can be sorted according to the higher variability, since it enhances the class clustering.…”

Section: A Principal Component Analysismentioning

confidence: 99%

Data Processing Method Applying Principal Component Analysis and Spectral Angle Mapper for Imaging Spectroscopic Sensors

et al. 2008

View full text Add to dashboard Cite

A data processing method to classify hyperspectral images from an imaging spectroscopic sensor is evaluated. Each image contains the whole diffuse reflectance spectra of the analyzed material for all the spatial positions along a specific line of vision. The implemented linear algorithm comes to solve real time constrains typical of industrial systems. This processing method is composed of two blocks: data compression is performed by means of principal component analysis (PCA) and the spectral interpretation algorithm for classification is the spectral angle mapper (SAM). This strategy, applying PCA and SAM, has been successfully tested for online raw material sorting in the tobacco industry, where the desired raw material (tobacco leaves) should be discriminated from other unwanted spurious materials, such as plastic, cardboard, leather, feathers, candy paper, etc. Hyperspectral images are recorded by a sensor consisting of a monochromatic camera and a passive prism-grating-prism device. Performance results are compared with a spectral interpretation algorithm based on artificial neural networks (ANN).Index Terms-Imaging spectroscopic sensor, principal component analysis (PCA), raw material online characterization, spectral angle mapper (SAM).

show abstract

“…The referenced color values were also computed by using the reference white and black as follows where C' is a color component R. G, or B of a pixel, and CR' and CD are the reference white and black values for the corresponding pixel, respectively. For example, the red component can be computed as (3) Then, the means of the r, gb values were computed for each tile. The means in the r, g, b coordinate system are shown in Fig.…”

Section: Rgb Imagesmentioning

confidence: 99%

“…Especially, we are continuing the study of multispectral images, applying a spectral line camera described in Ref. 3.…”

Section: Introductionmentioning

confidence: 99%

<title>Visual inspection by spectral features in the ceramics industry</title>

1999

View full text Add to dashboard Cite

Visual quality control is an important application area of machine vision. In ceramics industry, it is essential that in each set of ceramic tiles every single tile looks similar, while considering e.g. color and texture. Our goal is to design a machine vision system that can estimate the sufficient similarity or same appearance to the human eye. Currently, the estimation is usually done by human vision. Our main approach is to use accurate spectral representation of color, and compare spectral features to the RGB color features.The authors have recently proposed preliminary methods and results for the classification of color features. In this paper the approach is developed further to cope with illumination effects and to take more advantage of spectral features more. Experiments with five classes of brown tiles are discussed. Besides the k-NN classifier, a neural network, called the Self-Organizing Map (SOM) is used for understanding spectral features. Every single spectrum in each tile is used as input to a 2-D SOM with 30 x 30 nodes or neurons. The SOM is analyzed in order to understand how spectra are clustered. As a result, the nodes are labeled according to the classes. Another interest is to know whether we can find the order of spectral colors. In our approach, all spectra are clustered by 32 nodes in a 1-D SOM, and each pixel (spectrum) is presented by pseudocolors according to the trained nodes. Thus, each node corresponds to one pseudocolor and every spectrum is mapped into one of these nodes. Finally, the results are compared to experiments with human vision.

show abstract

An optoelectronic sensor system for industrial multipoint and imaging spectrometry

Cited by 19 publications

References 2 publications

Hyperspectral Imaging for Raw Material Sorting and Processed Product Quality Control

Hyperspectral Imaging for Raw Material Sorting and Processed Product Quality Control

Data Processing Method Applying Principal Component Analysis and Spectral Angle Mapper for Imaging Spectroscopic Sensors

<title>Visual inspection by spectral features in the ceramics industry</title>

Contact Info

Product

Resources

About