Characterization Of Camera Research Articles

Characterization of x-ray framing cameras for the National Ignition Facility using single photon pulse height analysis.

Single hit pulse height analysis is applied to National Ignition Facility x-ray framing cameras to quantify gain and gain variation in a single micro-channel plate-based instrument. This method allows the separation of gain from detectability in these photon-detecting devices. While pulse heights measured by standard-DC calibration methods follow the expected exponential distribution at the limit of a compound-Poisson process, gain-gated pulse heights follow a more complex distribution that may be approximated as a weighted sum of a few exponentials. We can reproduce this behavior with a simple statistical-sampling model.

The Calibration Home Base for Imaging Spectrometers

The Calibration Home Base (CHB) is an optical laboratory designed for the calibration of imaging spectrometers for the VNIR/SWIR wavelength range. Radiometric, spectral and geometric calibration as well as the characterization of sensor signal dependency on polarization are realized in a precise and highly automated fashion. This allows to carry out a wide range of time consuming measurements in an ecient way. The implementation of ISO 9001 standards in all procedures ensures a traceable quality of results. Spectral measurements in the wavelength range 380–1000 nm are performed to a wavelength uncertainty of +- 0.1 nm, while an uncertainty of +-0.2 nm is reached in the wavelength range 1000 – 2500 nm. Geometric measurements are performed at increments of 1.7 µrad across track and 7.6 µrad along track. Radiometric measurements reach an absolute uncertainty of +-3% (k=1). Sensor artifacts, such as caused by stray light will be characterizable and correctable in the near future. For now, the CHB is suitable for the characterization of pushbroom sensors, spectrometers and cameras. However, it is planned to extend the CHBs capabilities in the near future such that snapshot hyperspectral imagers can be characterized as well. The calibration services of the CHB are open to third party customers from research institutes as well as industry.

Weighted Constrained Hue-Plane Preserving Camera Characterization.

Color correction relates device dependent sensor responses (RGB) to device independent color values (XYZ). Here we present a new approach to Hue-plane Preserving Color Correction (HPPCC) using weighted constrained 3 × 3 matrices. Hue-plane preservation was introduced in [1] in conjunction with an HPPCC method. That method maps using a finite number of local white point preserving 3 × 3 matrices, each of which operates in a hue-angle delimited subregion of device space defined by the white and two adjacent chromatic training set colors. However, that formulation does not leave room for optimization or continuity beyond C0 in the transitions between the subregions. To remedy that our new method uses hue-angle specific weighted matrixing: given a device RGB from which a device hue-angle is derived, a corresponding transformation matrix is found as the normalized weighted sum of all precalculated constrained white point and training color preserving matrices. Each weight is calculated as a power function of the minimum difference between the device and the training color hue-angle. The weighting function provides local influence to the matrices that are in close hue-angle proximity to the device color. The power of the function is optimized for global accuracy. We call this Hue-plane Preserving Color Correction by Weighted Constrained Matrixing HPPCC-WCM 1 1. Experiments performed using different input spectra show that our method consistently improves on both stability and accuracy compared to state of the art methods.

Spectral Characterization of a Prototype SFA Camera for Joint Visible and NIR Acquisition.

Multispectral acquisition improves machine vision since it permits capturing more information on object surface properties than color imaging. The concept of spectral filter arrays has been developed recently and allows multispectral single shot acquisition with a compact camera design. Due to filter manufacturing difficulties, there was, up to recently, no system available for a large span of spectrum, i.e., visible and Near Infra-Red acquisition. This article presents the achievement of a prototype of camera that captures seven visible and one near infra-red bands on the same sensor chip. A calibration is proposed to characterize the sensor, and images are captured. Data are provided as supplementary material for further analysis and simulations. This opens a new range of applications in security, robotics, automotive and medical fields.

相机参数可变的齐次多项式色度特征化模型

相机参数可变的齐次多项式色度特征化模型

Measurement of food colour in L*a*b* units from RGB digital image using least squares support vector machine regression

The aim of this work is to evaluate the potential of least squares support vector machine (LS-SVM) regression to develop an efficient method to measure the colour of food materials in L*a*b* units by means of a computer vision systems (CVS). A laboratory CVS, based on colour digital camera (CDC), was implemented and three LS-SVM models were trained and validated, one for each output variables (L*, a*, and b*) required by this problem, using the RGB signals generated by the CDC as input variables to these models. The colour target-based approach was used to camera characterization and a standard reference target of 242 colour samples was acquired using the CVS and a colorimeter. This data set was split in two sets of equal sizes, for training and validating the LS-SVM models. An effective two-stage grid search process on the parameters space was performed in MATLAB to tune the regularization parameters γ and the kernel parameters σ<sup>2</sup> of the three LS-SVM models. A 3-8-3 multilayer feed-forward neural network (MFNN), according to the research conducted by León <em>et al.</em> (2006), was also trained in order to compare its performance with those of LS-SVM models. The LS-SVM models developed in this research have been shown better generalization capability then the MFNN, allowed to obtain high correlations between L*a*b* data acquired using the colorimeter and the corresponding data obtained by transformation of the RGB data acquired by the CVS. In particular, for the validation set, R<sup>2</sup> values equal to 0.9989, 0.9987, and 0.9994 for L*, a* and b* parameters were obtained. The root mean square error values were 0.6443, 0.3226, and 0.2702 for L*, a*, and b* respectively, and the average of colour differences ΔE<sub>ab</sub> was 0.8232±0.5033 units. Thus, LS-SVM regression seems to be a useful tool to measurement of food colour using a low cost CVS.

Colorimetric characterization of digital cameras with unrestricted capture settings applicable for different illumination circumstances

With colorimetric characterization, digital cameras can be used as image-based tristimulus colorimeters for color communication. In order to overcome the restriction of fixed capture settings adopted in the conventional colorimetric characterization procedures, a novel method was proposed considering capture settings. The method calculating colorimetric value of the measured image contains five main steps, including conversion from RGB values to equivalent ones of training settings through factors based on imaging system model so as to build the bridge between different settings, scaling factors involved in preparation steps for transformation mapping to avoid errors resulted from nonlinearity of polynomial mapping for different ranges of illumination levels. The experiment results indicate that the prediction error of the proposed method, which was measured by CIELAB color difference formula, reaches less than 2 CIELAB units under different illumination levels and different correlated color temperatures. This prediction accuracy for different capture settings remains the same level as the conventional method for particular lighting condition.

Critical spectra in the color reproduction process of digital motion picture cameras

Most cameras resolve a color stimulus by three spectral sensitivity responses. Commonly, these do not satisfy the Luther-Ives condition. Hence, there is no linear mapping between the camera's RGB values and the CIE XYZ tristimulus values. Although more complex methods for camera characterization have been proposed, none can solve the issues caused by camera metamerism: While being different to the human eyes, certain stimuli will induce the same RGB response in the camera. To investigate color reproduction errors and the effects of device metamerism, metameric spectra must be found. Previously proposed methods generate metameric pairs artificially. We present a method for finding metameric pairs within a database of real-world measured spectra, which we believe has more practical utility than methods employing synthesized metameric pairs. By using measured camera sensitivities and reflectances from the SOCS database we can identify metamers with a real-world occurrence possibility. We characterize critical spectra by type of object and by hue, considering both CIE illuminants and real-world LED fixtures.

Perceptual color characterization of cameras.

Color camera characterization, mapping outputs from the camera sensors to an independent color space, such as XY Z, is an important step in the camera processing pipeline. Until now, this procedure has been primarily solved by using a 3 × 3 matrix obtained via a least-squares optimization. In this paper, we propose to use the spherical sampling method, recently published by Finlayson et al., to perform a perceptual color characterization. In particular, we search for the 3 × 3 matrix that minimizes three different perceptual errors, one pixel based and two spatially based. For the pixel-based case, we minimize the CIE ΔE error, while for the spatial-based case, we minimize both the S-CIELAB error and the CID error measure. Our results demonstrate an improvement of approximately 3% for the ΔE error, 7% for the S-CIELAB error and 13% for the CID error measures.

Characterization of a Commercial, Front-Illuminated Interline Transfer CCD Camera for use as a Guide Camera on a Balloon-Borne Telescope

We report results obtained during the characterization of a commercial front-illuminated progressive scan interline transfer CCD camera. We demonstrate that the unmodified camera operates successfully in temperature and pressure conditions (-40°C, 4 mBar) representative of a high altitude balloon mission. We further demonstrate that the centroid of a well-sampled star can be determined to better than 2% of a pixel, even though the CCD is equipped with a microlens array. This device has been selected for use in a closed-loop star-guiding and tip-tilt correction system in the BIT-STABLE balloon mission.

Sky Based Light Metering for High Dynamic Range Images

AbstractImage calibration requires both linearization of pixel values and scaling so that values in the image correspond to real‐world luminances. In this paper we focus on the latter and rather than rely on camera characterization, we calibrate images by analysing their content and metadata, obviating the need for expensive measuring devices or modeling of lens and camera combinations. Our analysis correlates sky pixel values to luminances that would be expected based on geographical metadata. Combined with high dynamic range (HDR) imaging, which gives us linear pixel data, our algorithm allows us to find absolute luminance values for each pixel—effectively turning digital cameras into absolute light meters. To validate our algorithm we have collected and annotated a calibrated set of HDR images and compared our estimation with several other approaches, showing that our approach is able to more accurately recover absolute luminance. We discuss various applications and demonstrate the utility of our method in the context of calibrated color appearance reproduction and lighting design.

Subpixel characterization of a PIV-CCD camera using a laser spot

We present a simple method for charge-coupled device (CCD; or CMOS) sensor characterization by using a subpixel laser spot. This method is used to measure the variations in sensitivity of the 2D sensor array systems equipped with a microlens array. The experimental results show that there is variation in the sensitivity for each position on the CCD of the camera, and the pixel optical center error with respect to the geometrical center is in the range of one-tenth that of a pixel. The disparity observed is attributed to the coherence of the laser light used that generates interference at the scale of the pixel. This may have significant consequences for coherent light imaging using CCD (or CMOS) such as particle image velocimetry.

Spatial characterization of cameras for low-uncertainty radiometric measurements

Radiometrically characterizing a camera means relating the response of every pixel of the camera to the radiance emitted by a light source. In order to accomplish this task, not only the responsivities of the pixels have to be characterized, but, in addition, their non-ideal response (dark response or non-linearity) or the radiant flux impinging on the pixel from outside its corresponding viewing solid angle. An extended uniform source developed at the PTB was used to spatially characterize a camera by determining the parameters involved in a response model. The uniformity of the source was optimized using the actual camera to be characterized, and two different procedures were used to estimate the non-uniformity of the source. These procedures are based on varying the distance from the source to the camera and, in consequence, the field-of-view area, allowing the non-uniformity of the source to be identified as the non-correlated spatial variations in the images.

Characterization of IR cameras in student labs

The content of a student lab course is described which deals with characterization of the most important parameters governing the performance of infrared cameras. In detail, the parameters describing the temperature resolution, spatial resolution and time resolution of commercial IR cameras are analysed using simple lab experiments. Understanding the respective performance of the system is the prerequisite for applying infrared thermal imaging to quantitative measurements in physics and technology.

Characterization of Digital Cameras for Reflected Ultraviolet Photography; Implications for Qualitative and Quantitative Image Analysis During Forensic Examination

Reflected ultraviolet imaging techniques allow for the visualization of evidence normally outside the human visible spectrum. Specialized digital cameras possessing extended sensitivity can be used for recording reflected ultraviolet radiation. Currently, there is a lack of standardized methods for ultraviolet image recording and processing using digital cameras, potentially limiting the implementation and interpretation. A methodology is presented for processing ultraviolet images based on linear responses and the sensitivity of the respective color channels. The methodology is applied to a FujiS3 UVIR camera, and a modified Nikon D70s camera, to reconstruct their respective spectral sensitivity curves between 320 and 400 nm. This method results in images with low noise and high contrast, suitable for qualitative and/or quantitative analysis. The application of this methodology is demonstrated in the recording of latent fingerprints.

Geometry-Independent Target-Based Camera Colorimetric Characterization

An accurate colorimetric characterization of digital still cameras (DSCs) is vital to any high-quality color-reproduction system. However, achieving a perfect relationship between DSC responses and input spectral radiance is not practically easy, even when they have a reasonable linear relationship. In this research, we investigated differences in capturing geometries as a source of nonlinearity in camera characterization workflows. This nonlinearity can be corrected using a physical model describing the spectrophotometric changes according to illumination/capturing geometries. We introduced a model based on the Saunderson equation as an approach to predict surface properties suitable for paint layers in different geometries. According to the results, the Saunderson surface correction success- fully compensated for the dissimilarities among spectrophotometric and spectroradiometric measurements, regardless of the capturing and lighting geometries. The model was also used for characterizing digital still cameras using matte, semi-glossy and glossy color targets as training datasets. The Saunderson-based models improved the transformation matrix for different geometries compared to conven- tional methods. Also, the results confirmed the validity of a simpler derivation of the Saunderson surface correction based on linear matrix operations. d

High-speed camera characterization of voluntary eye blinking kinematics

Blinking is vital to maintain the integrity of the ocular surface and its characteristics such as blink duration and speed can vary significantly, depending on the health of the eyes. The blink is so rapid that special techniques are required to characterize it. In this study, a high-speed camera was used to record and characterize voluntary blinking. The blinking motion of 25 healthy volunteers was recorded at 600 frames per second. Master curves for the palpebral aperture and blinking speed were constructed using palpebral aperture versus time data taken from the high-speed camera recordings, which show that one blink can be divided into four phases; closing, closed, early opening and late opening. Analysis of data from the high-speed camera images was used to calculate the palpebral aperture, peak blinking speed, average blinking speed and duration of voluntary blinking and compare it with data generated by other methods previously used to evaluate voluntary blinking. The advantages of the high-speed camera method over the others are discussed, thereby supporting the high potential usefulness of the method in clinical research.

SU-E-T-51: Characterization of a Novel CCD Camera Based Imaging System for Radiochromic Film Dosimetry

Purpose: In this paper we present work to further develop and characterize a novel method of reading radiochromic film. The technique is benchmarked against imaging on a clinically used Epson 10000XL scanner, using well characterized standard dose deliveries and patient IMRT QA results. Methods: A purpose‐built CCD camera‐based film digitizer was designed and prototyped with Modus Medical (London, Ontario). The system consists of a camera mounted above a LED lightbox, interfaced with computer image acquisition software. The system was based on components of the Vista optical CT scanner (Modus Medical Devices Inc.) and looks to improve on the flat‐bed scanner based dosimetry system, which suffer from film orientation dependence and non‐uniform scanner sensitivity. Various characteristics imaging system performance were measured including: dependence on camera aperture size, spatial resolution, light scatter contamination, lightbox temperature variation effects, and signal‐to‐noise ratio (SNR). Dose readout accuracy has also been determined using films that have been imaged using white‐light and colour lightbox illumination system. Results are compared with readout using the Epson scanner. Results: The new system provides fast, orientation independent readout of radiochromic film. High resolution (0.25 mm x 0.25 mm pixel size) and high signal‐to‐noise ratio (SNR = 280 for 230 cGy, 10x10 cm square field) images are obtained in 6.7 seconds, averaging 100 frames. A warm‐up time of at least one hour stabilizes light output and eliminates temperature effects. The readout of all test films agree well (to within 1% dose difference over most of the film) to the dose determined by other approaches. Conclusion: This new imaging technique shows good promise to simplify and improve on existing film dosimetry readout. Further development towards a commercial system is currently underway with Modus Medical. Research funding support provided by the Canadian Institutes of Health Research (CIHR)

Methods for designing characterisation targets for digital cameras

Characterisation targets usually include a set of physical coloured samples. A characterisation model can be derived between the colorimetric values (tristimulus values) and camera responses (RGB values) taken from an imaging device such as a digital camera capturing the colours in the target. The performance of such a model is highly dependent upon the number of colours and the colour region in the characterisation target. An ideal characterisation target should provide accurate model prediction without requiring too many samples. In this paper, a computational method is presented for colour selections to train a camera characterisation model based on a fourth‐order polynomial model including 35 terms. Compared with other available methods, the newly developed method performed better. It is proposed that this method be applied to generate generic targets in terms of colorimetric values. These targets should work reasonably well for a wide range of materials.

Simple X-ray cameras for beam-line instrumentation

The design and performance characterization of simple X-ray cameras are shortly presented. These cameras are installed on each high-energy beam-lines of SOLEIL. Main topics, such as the choice of the scintillator, the effect of the thickness of the scintillator on the resolution, the relation between the X-ray flux and the signal, are addressed in this article.