Image Metric Solutions

Specifications

Image metrics software DEVELOPMENT KIT

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Image Metric Solutions "Image Metrics SDK" comes with the core analysis algorithms from "Image Metrics" plus addtional image conversion functions. These algorithms and functions come in DLL (Dynamic Link Library) files and NI-Labview (2107) VI files. Some of the algorithms and functions use C++ libraries and image functions. The IM-SDK is64-bit only.

ANALYSIS ALGORITHMS

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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 Pixels 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 detects defects or contamination, and 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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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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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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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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Sharpness 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 Template Designer.
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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.

Utility Functions

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LV_DECODEBAYER2RGB Decodes a RAW Bayer image to an unsigned 32 bit RGB image (8 bits per channel). You can select from the following decode methods; Bi-Linear (C++), Gradient, Red Clear, and Red Clear/Gradient (Red Clear and Red Clear/Gradient are designed for automotive camera sensors supporting a single Red color channel.).
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LV_ExtractBayer Extracts the four Bayer planes (unsigned 16 bit) from a RAW Bayer image.
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LV_extractBayerDBL Extracts the four Bayer planes (double precision representation) from a RAW Bayer image.
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LV_ExtractRGBU32 Extracts the three 8 bit color channels Red, Green, and Blue from a RGB image.
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LV_MergeRGBU32 Merges the three color channels Red, Green and Blue into a signle unsigned 32 bit image.
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LV_readImageDataFile Reads an image file and outputs a double precision image buffer. This function supports standard image file types; BMP, JPG, and PNG. It also supports BIN and RAW file types but requires a RAW File Settings file exported from the Image Metrics application to properly extract the image data from file. This function also supports Image Metrics specific file types IAS and IAT. The IAS file type is a binary type file; which can contain any type of image data, this file type is an export option from the Image Metrics application. This file type is very fast for reading and writing and contains descriptors of the image data. The IAT is file type is specific to Image Metrics Template files. These Template Files are created in the supplemental application "Template Designer" and contain the original template image plus pattern matching descriptors generated from the application. These template files are used the Sharpness, Color Chart, and Step Chart algorithms. This function outputs the template descriptors plus the image data. This function also can read a CSV file.
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LV_getRGBBayer Decodes a RAW Bayer image to RGB, returning three seperate channels (Red, Green, Blue) preserving the bit depth from 8 bit to 16 bit per channel.
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LV_MergeBayer Merges the four Bayer channels into a single 16 bit image.
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LV_RGB2HSL Calculates the Hue, Saturation and Ligthness channels from a RGB image. The Red, Green and Blue channels are passed to this function as seperate channels.
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LV_RGB2YUV Calculates the Y (Luma), U, and V channels from a RGB image. The Red, Green and Blue channels are passed to this function as seperate channels.
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LV_RGB2Lab Calculates the L*, a*, and b* channels from a RGB image. This function requires some conversion parameters to perform correctly, and these can be found via the "Lab Settings" palette in the "Image Metrics" application. The Red, Green and Blue channels are passed to this function as seperate channels.
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LV_RGB2YCbCr Calculates the Y1, Cb, and Cr channels from a RGB image. The Red, Green and Blue channels are passed to this function as seperate channels.
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LV_lensshadingcorrection Corrects the lens shading effect in an image by applying correction factors using Relative Illumination percentage. You can measure the Relative Illumination using the "Relative Illumination" algorithm function to get the RI of the image then use it in this function to correct for the lens shading effect.
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LV_WhiteBalanceBayer Measures the mean of a centered ROI across all four Bayer channels and calculates scalars for each channel and applies them to balance the image data for white balanced image.
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VerifyUSBLicense64 Reads the USB License Key and returns a valid key flag. This is executed in each Analysis Algorithm, but can be used independently to verify a USB Key is inserted into the test system before beginning a test sequence.

data types

data Types: Image Metrics SDK supports image data with bit depths from 8-bit to 64-bit, BAYER interpolation to RGB format from 8-bit (U32), floating point image data via CSV files, and all image data calculations are performed in double precision representation.

Computer requirements

Item Minimum Recommended
Processor Duo Core or equivalent i7 Quad Core or greater
RAM 8GB 16 GB or greater
Operating System Windows 8/7 Pro
(64-bit)
 Windows 10 Pro (64-bit)
Disk Space 100 MB 100 MB

USB License Key

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The Image Metrics Software Development Kit requires a USB License Key. The "Image Metrics SDK" USB License Key cost is $3600 USD (per machine) and can be purchased by contacting Image Metric Solutions for a quote.