Mobile Image Parametric Systems

Specifications

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IMAGE ANALYSIS SUITE

analysis ALGORITHMS

Algorithm
Description
Type
Alignment
Measures XY Tilt, Rotation and other alignment measurements.
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Color Chart
Determines whether the Color from the 24 color patches are within an acceptable range, by comparing their L*a*b* values to a reference file.
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Color Ratio
Determine whether certain Color Ratios fall outside acceptable limits at predefined locations.
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Color Uniformity Radial
Determines color shading irregularities across an image using ROIs in a ring orientation (multiple rings are used across image).
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CPIQ-Color Shading
Determines shading irregularities in intensity and color across an image using a grid of ROIs.
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Dark Noise
Determine whether the image sensors temporal noise in a Dark Field is within acceptable range. Dark Noise is calculated by using 2 RAW images that were consecutively captured from the image sensor.
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Data Line Integrity
Determine whether the image sensor has data lines that are either stuck low, high, or stuck to adjacent data lines. This is evaluated by subtracting the self test pattern image from a Reference image with the same test pattern and the percentage of bad pixels is calculated.
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Defective Pixel Pairs
Scans image for defective pixels. A defective pixel is characterized as a pixel stuck low, a pixel stuck high, or a pixel covered by staining or foreign material at any level of the module. Single Pixel (SP) defects are detected and four Defect Pair types are determined within a 5x5 pixel grid of the Single Pixel defect, ARPD, SPD, MPD, LPD.
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Focus
Determine whether the slanted edges specified by the template file are within acceptable range. The focus scores can be specified as SFR, MTF, CTF, or CTF with W/B reference patches. Delta values can be specified in any measurement group, by using the Focus Template editor.
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Geometric Distortion
Determines whether the imaging optics has excessive barrel or pincushion distortion. A target chart with a matrix of dots is required for this algorithm.
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Line Noise
Determine whether the image sensor is creating temporal noise or fixed pattern noise. Line Noise is calculated by using 2 RAW images that were consecutively captured from the image sensor.
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Low Contrast Defect
Scans image for low contrast defects. A low contrast defect is characterized as a colored or gray area in the image, which may represent a water spot, residue on sensor surface, or particles on the IRCF, Lens or CFA abnormalities.
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Optical Center
Determines whether the imaging optics is centered with respect to the image sensor. The measured results are relative pixels values from image center. An uniform light field image should be used.
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Particles by Size
Scans image for contamination particles. A particle is characterized as a pixel stuck low, a pixel stuck high, or a pixel covered by staining or foreign material at any level of the module. This algorithm is rudimentary and cursory in detecting defects or contamination, but uses an inner and border region mask to bin defects. Inner region defects can have more strict pass fail criteria and a more lenient criteria for the border region. This algorithm reports three types of particle sizes; Single pixel, Couplets (2 adjacent pixels), and Clusters (3 or more adjacent pixels).
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Particles
Scans image for contamination particles. A particle is characterized as a pixel stuck low, a pixel stuck high, or a pixel covered by staining or foreign material at any level of the module. This algorithm is rudimentary and cursory in detecting defects or contamination, but uses an inner and border region mask to bin defects. Inner region defects can have more strict pass fail criteria and a more lenient criteria for the border region.
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Relative Illumination
Determines whether the imaging optics has excessive lens shading roll off. Relative illumination is calculated by dividing the average of each ROI in each corner of the image (upper left UL, upper right UR, lower left LL, lower right LR) by the average value of the ROI at the image center. Relative Illumination delta is the difference between the brightest and the dimmest corner.
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Relative Uniformity
Determines determines whether the imaging optics has excessive lens shading roll off, contamination, or irregular luminance values across the image. Relative Uniformity is calculated by dividing the image into a matrix of ROIs and calculating their means and then the maximum deviation from its neighbors and reporting the maximum deviation for the corner region set by “Corner Blocks” and the maximum for edges, and center regions.
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SMIA-Blemish
Scans image for blemishes. A blemish is characterized as a colored or gray area in the image, which may represent a water spot, residue on sensor surface, or particles on the IRCF, Lens or CFA abnormalities. Blemishes are defined into two classifications, Minor and Major. A Minor defect is defined as a single blemish defect with no adjacent defects, and a Major defect is defined as more than one adjacent blemish defect.
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SMIA-Defective Pixels
Scans image for defective pixels. A defective pixel is characterized as a pixel stuck low, a pixel stuck high, or a pixel covered by staining or foreign material at any level of the module. Multiple thresholds are used to determine weak or dead pixels and further classify them into minor and major couplets in a inner and border region. Clusters are classified as 3 or more defect pixels of any kind.
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SMIA-Dynamic Range
The dynamic range of a camera module is a measure of the range of light levels that may be present within one scene and reproduced faithfully. The upper useable limit of the light response of the camera is termed the full-scale deflection (FSD) of the camera. The minimum discernable response is taken to be at one standard deviation of the noise, including dark noise, above the noise floor.
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SMIA-Fixed pattern Noise
Fixed Pattern Noise is calculated by finding the row and column averages and calculating the variation between column averages (VFPN) and row averages (HFPN). If Multiple Images are selected then images are averaged before analysis.
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SMIA-Row Column Noise
The Row and Column Noise for an image camera is a measure of the temporal noise present in row averages and column averages, which manifests itself as flickering rows or columns when imaging in low light conditions.
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SMIA-Signal Noise Ratio
The signal to noise ratio (SNR light) for a camera module is a measure of the amount of speckle in an image of a lit scene. The SNR light can be defined as a noise power level for a standard uniform illumination, which, along with an exposure, results in an average output of 50 ± 5% of the FSD. As with the dark temporal noise, the SNR light is measured by taking a number of frames and finding the mean and standard deviation of the pixel level over these frames for each pixel. The pixel standard deviations represents the noise in an individual pixel, which are then root mean squared and divided into the means to give the SNR value for the camera.
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SMIA-Temporal Noise
The temporal noise is a measure of the “speckle” component of an image. The temporal noise is seen as a pixel level that varies randomly from frame to frame. The temporal noise is measured by taking a number of frames and finding the standard deviation of the pixel level over these frames for each pixel. The pixel standard deviations represents the noise in an individual pixel, which are then root mean squared to give a temporal noise value for the camera.
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Step Chart
Determines whether the Color from the step chart patches are within an acceptable range, by comparing their L*a*b* values to a reference file. The color values are measured by calculating the average pixel value of R, G, and B within each color patch, and then the L*a*b* values are calculated. The delta between the measured L*a*b* values and the Reference File L*a*b* values are calculated and then reported.
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Veiling Glare
Determines whether the Veiling Glare Index is within acceptable range. Veiling glare is the reduction in contrast, or misting, in an optical system due to random scattering of light onto the image plane. The veiling glare index is defined as the ratio of the irradiance at the center of an image of a small perfectly black area superimposed on an extended field of uniform radiance, to the irradiance at the same point of the image plane when the black area is removed.
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