Automation of waste recycling using hyperspectral image analysis

Picón, Artzai; Ghita, Ovidiu; Iriondo, Pedro; Bereciartua, Arantza; Whelan, Paul F.

doi:10.1109/etfa.2010.5641201

Cited by 9 publications

(9 citation statements)

References 4 publications

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“…Not only the classification of the imaged material is possible, see, e. g., [1], but also the extraction of more detailed information such as water content [2,3] and quality of food [4][5][6]. Additionally, hyperspectral images offer the possibility to extract criteria for the industrial sorting of various goods such as waste [7] and agricultural products like potatoes [8] and blueberries [9]. The downside of hyperspectral imaging, however, are low acquisition speed, big data volume and long computation time needed to process the acquired data.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral image unmixing involving spatial information by extending the alternating least-squares algorithm

Bauer¹,

Stefan²,

León³

2015

Tm - Technisches Messen

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Hyperspectral images, in contrast to common RGB images, offer the possibility to not only determine the pure materials present in a scene, but also material abundances in mixtures. The calculation of the material fractions with the so-called linear mixing model is not unique, an infinite number of solutions exists. Therefore, additional constraints should be incorporated. Some algorithms involve spatial constraints explicitly, e. g., they assume that the abundances mostly do not change considerably from one pixel to another. Recently, we presented such algorithms. The calculation time with spatial constraints included, however, is rather long, so it was checked if there is a faster way to include the spatial information. In this paper, we extend the well-known alternating least-squares algorithm to implicitly include the previously used spatial information in a slightly different way, namely by adding an extra image denoising step to the calculation. The extended algorithm is called ALSmooth. We compare the computing time and the results of the ALSmooth and the previously presented algorithms. For this purpose, laboratory data of mixtures with known ground truth had been acquired. Both the previously investigated algorithms and the ALSmooth algorithm are quite sensitive towards parameter value changes; the ALSmooth algorithm is even more sensitive. For certain applications with defined environment and endmembers, however, it can be a faster alternative.Zusammenfassung: Im Gegensatz zu herkömmlichen Farbbildern bieten hyperspektrale Bilder die Möglichkeit, nicht nur die in einer Szene enthaltenen Reinstoffe, sondern auch deren Anteile in Materialmischungen festzustellen. Da die Berechnung der Materialanteile mittels des sogenannten Linearen Mischmodells nicht eindeutig ist, existieren für dieses Problem unendlich viele Lösungen. Aus diesem Grund sollten zusätzliche Nebenbedingungen mit eingebunden werden. Einige Algorithmen binden räumliche Nebenbedingungen auf explizite Weise ein, indem sie die Annahme ausnutzen, dass die Materialanteile sich von einem Pixel zu seinen Nachbarn nicht wesentlich ändern. Wir haben solche Algorithmen kürzlich untersucht. Da diese Algorithmen, die räumliche Nebenbedingungen einbeziehen, vergleichsweise viel Rechenzeit beanspruchen, wurde nach recheneffizienteren Wegen gesucht, um die räumliche Information einzubeziehen. In diesem Beitrag wird die Methode der alternierenden kleinsten Quadrate (Alternating Least-Squares, ALS) um einen zusätzlichen Bildglättungsschritt erweitert, um die räumliche Information auf diese Weise einzubeziehen. Wir nennen diesen erweiterten Algorithmus ALSmooth und vergleichen ihn mit den bereits vorgestellten Algorithmen hinsichtlich Rechenzeit und Güte der Ergebnisse. Zu diesem Zweck wurden hyperspektrale Bilder von Pulvermischungen mit bekanntem Mischungsverhältnis aufgenommen. Sowohl ALSmooth als auch die anderen Algorithmen reagieren sehr sensibel auf Änderungen von Parameterwerten, wobei ALSmooth noch sensibler ist. Für manche Anwendungen stellt er sich ...

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Section: Introductionmentioning

confidence: 99%

Hyperspectral image unmixing involving spatial information by extending the alternating least-squares algorithm

Bauer¹,

Stefan²,

León³

2015

Tm - Technisches Messen

View full text Add to dashboard Cite

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“…Details about all mechanical components of the developed machine vision solution can be found in Ref. 24.…”

Section: System Overviewmentioning

confidence: 99%

“…As indicated in our earlier paper, where the mechanical design of the WEEE recycling system is outlined, 24 the WEEE delivery component (vibratory feeder) was carefully built to ensure a uniform placement of the nonferrous particles onto the belt of the conveyor without any particle overlaps.…”

Section: Background Segmentation and Particle Labelingmentioning

confidence: 99%

“…A robust solution resides in the combination of the spectral and spatial features with the aim of generating a descriptor that is able to factor in not only the spectral properties of the imaged materials, but also their textural characteristics as well. 4,9,15,[22][23][24] One approach toward spectralspatial integration was proposed by Mercier and Lennon,25 where the authors attempted to model the texture in the hyperspectral domain by computing the marginal distribution of the wavelet coefficients using generalized Gaussian density (GDD). Other approaches implemented texture decomposition using Gabor filters, 26 directional filter banks, 27 Markov random fields, 28 or analyzed the texture using statistical schemes based on the calculation of co-occurrence matrices.…”

Section: Introductionmentioning

confidence: 99%

“…These approaches proved inefficient when applied to the separation of several fractions of nonferrous materials (such as stainless steel, aluminum, and zinc), 8 and as a result new methodologies that perform the classification in the hyperspectral domain were actively explored. 9,24 While hyperspectral classification techniques proved accurate when applied to nonferrous sorting, they proved challenging when included in the development of industrial systems due to their high computational demand. To provide an insight into this issue, the specifications for WEEE recycling outlined in the SORMEN European project documentation 30 indicate that a sorting speed of one ton/h is required to justify the automatic recycling process from an economical perspective.…”

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confidence: 99%

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Real-time hyperspectral processing for automatic nonferrous material sorting

Picón

Ghita

Bereciartua

et al. 2012

J. Electron. Imaging

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Abstract. The application of hyperspectral sensors in the development of machine vision solutions has become increasingly popular as the spectral characteristics of the imaged materials are better modeled in the hyperspectral domain than in the standard trichromatic red, green, blue data. While there is no doubt that the availability of detailed spectral information is opportune as it opens the possibility to construct robust image descriptors, it also raises a substantial challenge when this high-dimensional data is used in the development of real-time machine vision systems. To alleviate the computational demand, often decorrelation techniques are commonly applied prior to feature extraction. While this approach has reduced to some extent the size of the spectral descriptor, data decorrelation alone proved insufficient in attaining real-time classification. This fact is particularly apparent when pixel-wise image descriptors are not sufficiently robust to model the spectral characteristics of the imaged materials, a case when the spatial information (or textural properties) also has to be included in the classification process. The integration of spectral and spatial information entails a substantial computational cost, and as a result the prospects of real-time operation for the developed machine vision system are compromised. To answer this requirement, in this paper we have reengineered the approach behind the integration of the spectral and spatial information in the material classification process to allow the real-time sorting of the nonferrous fractions that are contained in the waste of electric and electronic equipment scrap.

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