Spectral image processing in real-time

Carlsohn, Matthias F.

doi:10.1007/s11554-006-0004-y

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…More and more widespread use for food distribution in the food industry is the near infrared (NIR) technology [21][22][23]. Using this technology allows to capture the imperfection of the material or to obtain statistical correlations between a spectra obtained and a content of selected substances [23].…”

Section: Sorting Techniquesmentioning

confidence: 99%

Research on the Possibility of Sorting Application for Separation of Shale and/or Gangue from the Feed of Rudna Concentrator

Grotowski¹,

Witecki²

2017

E3S Web Conf.

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Abstract. Shale, which occurs in the copper ore deposits belonging to KGHM Polska Miedź S.A., is the reason for a number of difficulties, at the stage of not only processing but also smelting. Gangue, in turn, getting in a feed during mining is a useless load of a concentrator and also contributes to lowering concentrating indexes. Its content in a feed is being evaluated at 15-30%. The multiple attempts to solve those issues by the methods of conventional mineral processing or even selective mining failed. In the range of work, research on the lithological composition and Cu content in 300 individual particles (selected from Rudna feed) have been carried out. Using those results, the simulation of gangue separation with an application of sorting have been done. The positive results have been received: introduction of a sorting operation causes, theoretically, removing of approximately 20-30% sorting feed mass as final tailings with Cu losses not bigger than 5-10%. It means that the capacity of Rudna concentrator can be increased proportionally. To confirm those results, industrial sorting trials are necessary, when appropriate sorters will become available. Additionally, one should take also into account that the finest classes of feed (-12.5 mm) could not be concentrated in a sorter. In the range of work, the preliminary tests of the industrial sorter (PRO Secondary Color NIR) for separation of the shale concentrate from Rudna concentrator feed have been carried out. The shale concentrates were received both from 12.5-20 mm class and +20 mm class. The concentrates produced from the coarse classes, for both technological sides had shale content at the level of 48-49%, with recovery of 52.9-60%. In the case of the finer class, shale content in the concentrates for both technological sides amounts to 30.9-35%, at the slightly lower recoveries than for coarse classes. Cu and Corg behavior in the sorting process were checked also, however, the results turned out to be not very interesting. Because the results of shale concentrate production by sorting have a significant potential for improvement, the further researches in this direction have been recommended, however, making them start off from elaboration of a technology for shale concentrate processing and calculation of a total balance of a concentrating process involving flotation and separation.

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Section: Sorting Techniquesmentioning

confidence: 99%

Research on the Possibility of Sorting Application for Separation of Shale and/or Gangue from the Feed of Rudna Concentrator

Grotowski¹,

Witecki²

2017

E3S Web Conf.

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“…SI with mosaic spectral filter arrays on the image sensor (Themelis et al, 2008) leads to substantial light gathering losses. In "staring" or "pushbroom" SI systems (Carlsohn, 2006), removable sets of narrow bandpass filters (Long et al, 2005) or time-sequential dynamic spectral filters (López-Álvarez et al, 2008) slow the SI process and cannot apply it to dynamic, fast changing objects. Modern trends in digital imaging (Brady, 2009) resort to a generic combination of optics with digital processing and to compressed sensing (CS) (Donoho, 2006, Candès et al, 2006 for various purposes and applications.…”

Section: Introductionmentioning

confidence: 99%

Snapshot Spectral and Color Imaging Using a Regular Digital Camera With a Monochromatic Image Sensor

Hauser

Zheludev

Golub

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission III, WG III/4KEY WORDS: Multispectral and hyperspectral imaging, Imaging systems, Computational Imaging. ABSTRACT:"Spectral imaging" (SI) refers to the acquisition of the three-dimensional (3D) spectral cube of spatial and spectral data of a source object at a limited number of wavelengths in a given wavelength range. "Snapshot spectral imaging" (SSI) refers to the instantaneous acquisition (in a single "shot") of the spectral cube, a process suitable for fast changing objects. Known SSI devices exhibit large total track length (TTL), weight and production costs and relatively low optical throughput. We present a simple SSI camera based on a regular digital camera with (i) an added diffusing and dispersing "phase-only" static optical element at the entrance pupil ("diffuser") and (ii) tailored compressed sensing (CS) methods for digital processing of the diffused and dispersed (DD) image recorded on the image sensor. The diffuser is designed to mix the spectral cube data spectrally and spatially and thus to enable convergence in its reconstruction by CS-based algorithms. In addition to performing SSI, this SSI camera is capable to perform color imaging using a "monochromatic" or "gray-scale" image sensor without color filter arrays.

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“…The most straightforward way to perform SI is to use a removable set of narrow bandpass filters [3] or dynamic spectral filters [4]. High quality SI systems exploit "staring" or "pushbroom" imagers [5]. Neither provides a "snapshot" SI mode for fast changing objects with an unpredictable development along the time scale, where the entire spectral cube needs to be acquired simultaneously and instantaneously in one "shot."…”

Section: Introductionmentioning

confidence: 99%

Spectral multiplexing method for digital snapshot spectral imaging

et al. 2009

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We propose a spectral imaging method for piecewise "macropixel" objects, which allows a regular digital camera to be converted into a digital snapshot spectral imager by equipping the camera with only a disperser and a demultiplexing algorithm. The method exploits a "multiplexed spectrum" intensity pattern, i.e., the superposition of spectra from adjacent different image points, formed on the image sensor of the digital camera. The spatial image resolution is restricted to a macropixel level in order to acquire both spectral and spatial data (i.e., an entire spectral cube) in a single snapshot. Results of laboratory experiments with a special macropixel object image, composed of small, spatially uniform squares, provide to our knowledge a first verification of the proposed spectral imaging method.

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Spectral image processing in real-time

Cited by 14 publications

References 9 publications

Research on the Possibility of Sorting Application for Separation of Shale and/or Gangue from the Feed of Rudna Concentrator

Research on the Possibility of Sorting Application for Separation of Shale and/or Gangue from the Feed of Rudna Concentrator

Snapshot Spectral and Color Imaging Using a Regular Digital Camera With a Monochromatic Image Sensor

Spectral multiplexing method for digital snapshot spectral imaging

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