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New Processors in Sherlock 7 Ben Dawson

New Processors in Sherlock 7 Ben Dawson. Sherlock’s Processors. Preprocessor = Image to Image (e.g. threshold) Algorithm = Image to “readings” (e.g. blob analysis) Formula = Reading to Reading (e.g. add to a number) Sherlock 7 inherited most Sherlock 6 Processors

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New Processors in Sherlock 7 Ben Dawson

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  1. New Processors in Sherlock 7 Ben Dawson

  2. Sherlock’s Processors • Preprocessor = Image to Image (e.g. threshold) • Algorithm = Image to “readings” (e.g. blob analysis) • Formula = Reading to Reading (e.g. add to a number) • Sherlock 7 inherited most Sherlock 6 Processors • Some have slight differences (e.g. dynamic threshold)

  3. New Processors • Some processors improved (e.g., edge detectors) • New processors for new image types (e.g., color) • New processors for specific tasks (e.g., a “bead tool”) • New “high level” processors (e.g., Hough transform) • New utility processors (e.g., test patterns) • Introduce some of these new goodies and application

  4. Rewrite of Edge Detection Algorithms • Our edge detection algorithms needed improvement: • Inconsistent implementations with varying accuracy • Limited options • Sometimes not intuitive to use • Rewrote most standard edge detectors • Improved and consistent implementation • Better accuracy (1/8 pixel nominal, 1/25 best) • Flexible and easy-to-use GUI

  5. GUI for New Edge Detectors

  6. Comparing Old and New Edge Detection • Old edge detectors listed at the bottom of the line algorithms and marked “(legacy)” • Will be deprecated • NOTE: 0,0 is the CENTER of the pixel

  7. Other Edge Detectors • Edge Count uses the old interface and algorithms • HVLine has poor sub-pixel accuracy (½ pixel at best) • New edge detectors for specific tasks: • Laser Caliper (also used on Bead Tool) • Corner Detector • Ramp Edges has been subsumed by Detect Edges, etc. • Chatter Edges is an edge enhancer not a edge detector!

  8. Color Processing • Not calibrated (referenced to some standard) • Need standard lighting and calibration targets • Newer DALSA cameras will have calibration • Usually not necessary in machine vision • Can compensate for lighting changes • Color Correction Coefs and Color Correction • Needs a “reference patch” in field-of-view • Even LEDs change color with temperature and age

  9. Some Color Preprocessors • Color Correction – Applies correction coefficients • Gamma – Applies gamma correction • Raises each pixel to a fixed exponent, pg • Makes the image look better on the display • Usually not good for MV. Turn it off at the camera too! • Threshold – AND or OR of R,G,B thresholds • Threshold Components – Threshold individual components • Simple “classifier” that divides color space into cubes • Normalize by Chroma – Divides out intensity

  10. Tray of Aerators

  11. Threshold Components

  12. Normalize by Chroma

  13. Color Algorithms – Statistics • Color Correction Coefs – Learns correction coefficients • Average [channel] – Average value per channel • Count [channel] – Per channel count of pixels with specified value • Count [color] – Count of pixels with specified color • MinMax – Minimum and maximum RGB and location • MinMax [channel] – Minimum and maximum value per channel and location • Statistics [channel] – Arrays of minimum, maximum, average, variance per channel, and histograms • Unique Colors – Number of unique colors in ROI

  14. Color Classifiers • “Recognizes” or “Identifies” learned colors • GUI for training makes our classifiers easy to use • Color Map – Labels learned colors (outputs image) • Color Presence – Lists learned colors found in ROI • Spot Meter – Detects average learned color in an ROI • Trained classifiers can be shared between Color Map and Color Presence • Training for Map and Presence can take some time

  15. Specific Task Processors – Bead Tool • Designed to follow a “bead” – a thin line of material such as glue • Example: Checking glue bead on automotive liners • Set “start box” and learn the path of the bead. • At run time, follows learned path and checks that bead is there and correctly dimensioned

  16. Specific Processors – Chatter Edges • Amplifies very wide (slow intensity change) edges • Designed to help detect bearing “chatter” • Can be used for other slowly changing “ramp” edges • Note the “phase shift” of edges – this is normal

  17. Specific Processors – Laser Tools • Set of tools for mostly doing height measurements using a line of light (like a laser) and triangulation • Laser Caliper – Measure width of a bright line • Similar to Outside caliper but only bright lines and some additional noise reduction • Laser Points – Find line of light points • Laser Line – Fits a Sherlock line to points in the line of light • Laser Height – Measures part heights by triangulation

  18. Laser Tools Setup for Height 1

  19. Laser Tools Setup for Height 2 • Can put Laser above or off to the side. Above is better. • Camera must be to the side or above, opposite of laser • DALSA IPD’s height algorithm needs only three height calibration points: baseline, medium, high • NO measurements of the camera and laser positions, angles, distances etc. are needed • Typical accuracy is 1 part in 300 • Some limiting factors: • Laser speckle • Lens distortion

  20. High Level Processors • Extract features and information with more constraints and knowledge than edge detectors, calipers, blob, etc. • Roughness – Local standard deviation preprocessor • Texture– Edge Angles – Our first texture analyzer • Edge Crawler Sub-pixel edge crawler (Crawler is pixel) • Corner Finder – Finds corners (duh!) • Hough Transforms – Finds lines, line segments, or circles in noisy images

  21. Roughness Preprocessor • Computes the standard deviation in each neighborhood • Can be used as an “amplifier” for edges • Can be used as a spatial frequency texture filter • Can be used to suppress “background” texture

  22. Roughness as a Texture Filter

  23. Roughness used to Suppress Texture

  24. Texture – Edge Angles • First texture Algorithm (analyzer) – there will be more • Measures edge angle distribution (histogram) and computes an entropy (disorder) measure • These can be used to discriminate different textures

  25. Edge Crawler (sub-pixel) • Tracks edges and reports their sub-pixel position • Can select individual contours • Older Crawler algorithm is integer pixel position

  26. Corner Finder • Finds corners using the Harris corner detector • Corners are more constrained and therefore have more information than edges.

  27. Corner Finder Applications • Applied to finding and counting flexible circuit connector “pins”

  28. Hough Lines • Finds lines in noisy images • Hough transforms are “evidence-based” voting methods

  29. Hough Segments • Finds line segments with specific length ranges • Very useful and works well

  30. Hough Circles • Finds circles with specified radius ranges • Can’t tolerate distortions • Currently difficult to use – often generates a huge number of unwanted circles • Suggest using the spoke tool and BestFitCircleToPts formula for now

  31. Utility Processors – Test Patterns • Test pattern generators • Constant – ROI set to constant color or intensity • Draw Bars – Sub-pixel bars for testing edge detectors • Draw Gaussian – Draws Gaussian intensity distribution • Draw Line – Draws a single line • Draw Ramp – Draws intensity ramps • Draw Grid – Draws a grid of lines of any thicknesses • Draw Checkerboard – Draws checkerboard

  32. Draw Gaussian Example • Many test generators have “blending” option

  33. Checkerboard Example

  34. Testing Connectivity Analysis

  35. Other Utility Processors • Apodize – Increases or decreases intensity towards the edges of the image. • Could be used to compensate for some vignetting • Better to use radial cos4(r), not separable functions • ROI to Array(s) – Copies ROI pixel values to array(s) • Border – Puts a border (frame) just inside the ROI • Field Extract – Extracts even or odd fields from monochrome or color images • Mainly used to remove motion “interlace fingers” from older RS-170 interlaced images. • Sometimes useful in surveillance applications

  36. What’s Cooking in the Lab • Adding 16-bit image processors • Only a Statistics algorithm is currently distributed • Averaging, Shading correction • “Smart” conversion to 8-bit images • 16-bit test pattern generators • Applications in biological and microscope images • More specific and higher-level processors • Spring Tool (not the season or a delivery time) • Additional Laser Tools (wave, topographic surface, etc.) • Image Morphology Tools (Top-hat, watershed, etc.) • Improvements to Hough and other tools

  37. Summary • Many new and improved processors in Sherlock 7 • Most new processors are documented in technical “white papers” found on the web site • Move towards “higher level” vision processors • Edge detection still fundamental, but we can do better in many cases • Ease-of-use is an important design consideration • We welcome your input and suggestions • Send us your hard problems. After we all have a laugh…

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