scholarly journals Hyperspectral camera system: acquisition and analysis

1995 ◽  
Author(s):  
Gavin J. Brelstaff ◽  
Alejandro Parraga ◽  
Tomasz Troscianko ◽  
Derek Carr
Keyword(s):  
Camera System ◽  
System Acquisition ◽  
Hyperspectral Camera

First results of a new hyperspectral camera system for chemical based wound analysis

2015 ◽  
Vol 10-11 ◽  
pp. 17-22 ◽  
Author(s):  
Jörg Marotz ◽  
Apostolos Siafliakis ◽  
Amadeus Holmer ◽  
Axel Kulcke ◽  
Frank Siemers
Keyword(s):  
Camera System ◽  
First Results ◽  
Hyperspectral Camera

A next generation VNIR-SWIR hyperspectral camera system: HySpex ODIN-1024

2014 ◽  
Author(s):  
Søren Blaaberg ◽  
Trond Løke ◽  
Ivar Baarstad ◽  
Andrei Fridman ◽  
Pesal Koirala
Keyword(s):  
Next Generation ◽  
Camera System ◽  
Hyperspectral Camera

Experimental Research on Construction-Waste-Classification Algorithms for a Hyperspectral-Camera System

2020 ◽  
Vol 46 (1) ◽  
pp. 5-14
Author(s):  
Yuedong Ku ◽  
Jianhong Yang ◽  
Huaiying Fang ◽  
Jiangteng Zhuang ◽  
Wen Xiao
Keyword(s):  
Adaptive Learning ◽  
Near Infrared ◽  
Comprehensive Evaluation ◽  
Spectral Characteristics ◽  
Recognition Rate ◽  
Classification Model ◽  
Camera System ◽  
Construction Waste ◽  
Learning Machine ◽  
Hyperspectral Camera

A large proportion of construction waste has a high recovery value, and some of the existing recycling-classification methods rely mainly on physical properties for vibration screening. In order to effectively recover construction waste, an industrial near-infrared hyperspectral camera is proposed in this paper to distinguish the spectral characteristics of the objects. The testing results were verified using an extreme learning machine, an adaptive-learning multilayer perceptron, and a one-dimensional convolutional neural network classification model. By establishing several different models to classify and identify the same kinds of experimental materials, the experimental results not only output the correct recognition rate, but also use the recognition efficiency and stability as indices for comprehensive evaluation. The results show that different classification models have different efficiencies and levels of correctness. Under different analytical conditions, such as when using data from different bands, it is very important to select the appropriate classification model to classify construction waste.

Oxygenation and perfusion monitoring with a hyperspectral camera system for chemical based tissue analysis of skin and organs

2016 ◽  
Vol 37 (11) ◽  
pp. 2064-2078 ◽  
Author(s):  
Amadeus Holmer ◽  
Florian Tetschke ◽  
Jörg Marotz ◽  
Hagen Malberg ◽  
Wenke Markgraf ◽  
...  
Keyword(s):  
Tissue Analysis ◽  
Camera System ◽  
Hyperspectral Camera

Precise on-line Measurement of Lattice Vectors

1990 ◽  
Vol 48 (1) ◽  
pp. 530-531
Author(s):  
W.J. de Ruijter ◽  
Sharma Renu
Keyword(s):  
Electron Microscope ◽  
Measurement Errors ◽  
Dynamic Range ◽  
Structural Information ◽  
Statistical Parameter ◽  
Systematic Errors ◽  
Geometric Distortion ◽  
Crystal Silicon ◽  
Camera System ◽  
On Line

Established methods for measurement of lattice spacings and angles of crystalline materials include x-ray diffraction, microdiffraction and HREM imaging. Structural information from HREM images is normally obtained off-line with the traveling table microscope or by the optical diffractogram technique. We present a new method for precise measurement of lattice vectors from HREM images using an on-line computer connected to the electron microscope. It has already been established that an image of crystalline material can be represented by a finite number of sinusoids. The amplitude and the phase of these sinusoids are affected by the microscope transfer characteristics, which are strongly influenced by the settings of defocus, astigmatism and beam alignment. However, the frequency of each sinusoid is solely a function of overall magnification and periodicities present in the specimen. After proper calibration of the overall magnification, lattice vectors can be measured unambiguously from HREM images.Measurement of lattice vectors is a statistical parameter estimation problem which is similar to amplitude, phase and frequency estimation of sinusoids in 1-dimensional signals as encountered, for example, in radar, sonar and telecommunications. It is important to properly model the observations, the systematic errors and the non-systematic errors. The observations are modelled as a sum of (2-dimensional) sinusoids. In the present study the components of the frequency vector of the sinusoids are the only parameters of interest. Non-systematic errors in recorded electron images are described as white Gaussian noise. The most important systematic error is geometric distortion. Lattice vectors are measured using a two step procedure. First a coarse search is obtained using a Fast Fourier Transform on an image section of interest. Prior to Fourier transformation the image section is multiplied with a window, which gradually falls off to zero at the edges. The user indicates interactively the periodicities of interest by selecting spots in the digital diffractogram. A fine search for each selected frequency is implemented using a bilinear interpolation, which is dependent on the window function. It is possible to refine the estimation even further using a non-linear least squares estimation. The first two steps provide the proper starting values for the numerical minimization (e.g. Gauss-Newton). This third step increases the precision with 30% to the highest theoretically attainable (Cramer and Rao Lower Bound). In the present studies we use a Gatan 622 TV camera attached to the JEM 4000EX electron microscope. Image analysis is implemented on a Micro VAX II computer equipped with a powerful array processor and real time image processing hardware. The typical precision, as defined by the standard deviation of the distribution of measurement errors, is found to be <0.003Å measured on single crystal silicon and <0.02Å measured on small (10-30Å) specimen areas. These values are ×10 times larger than predicted by theory. Furthermore, the measured precision is observed to be independent on signal-to-noise ratio (determined by the number of averaged TV frames). Obviously, the precision is restricted by geometric distortion mainly caused by the TV camera. For this reason, we are replacing the Gatan 622 TV camera with a modern high-grade CCD-based camera system. Such a system not only has negligible geometric distortion, but also high dynamic range (>10,000) and high resolution (1024x1024 pixels). The geometric distortion of the projector lenses can be measured, and corrected through re-sampling of the digitized image.

Human Factors Evaluation of 360 Panoramic View Camera System for Ground Vehicle

2007 ◽  
Author(s):  
Cheng Li Wei ◽  
Ang Cher Wee ◽  
Chan Wai Herng ◽  
Ying Meng Fai
Keyword(s):  
Human Factors ◽  
Panoramic View ◽  
Camera System ◽  
Ground Vehicle

An Infrared Microscope for Use on a Focused Ion Beam for Circuit Edit and Backside Edit Applications

2015 ◽  
Author(s):  
Alexander Richards ◽  
Matthew Weschler ◽  
Michael Durller
Keyword(s):  
Focused Ion Beam ◽  
Near Infrared ◽  
Ion Beam ◽  
Visible Spectrum ◽  
Camera System ◽  
Buried Structures ◽  
Ir Camera ◽  
Area Of Interest ◽  
Near Ultraviolet ◽  
Infrared Microscope

Abstract To help solve the navigational problem, i.e., being able to successfully locate a circuit for probing or editing without destroying chip functionality, a near-infrared (NIR), near-ultraviolet (NUV), and visible spectrum camera system was developed that attaches to most focused ion beam (FIB) or scanning electron microscope vacuum chambers. This paper reviews the details of the design and implementation of the NIR/NUV camera system, as instantiated upon the FEI FIB 200, with a particular focus on its use for the visualization of buried structures, and also for non-destructive real time area of interest location and end point detection. It specifically considers the use of the micro-optical camera system for its benefit in assisting with frontside and backside circuit edit, as well as other typical FIB milling activities. The quality of the image obtained by the IR camera rivals or exceeds traditional optical based imaging microscopy techniques.

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