IPD Technical ConferenceFebruary 19th 2008

New Processors in Sherlock 7Ben 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

Some have slight differences (e.g. dynamic threshold)

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

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

GUI for New Edge Detectors

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

“Legacy” New, with GUI

Detect Edges Detect Edges

First Edge Find Edge

Inside Caliper Inside Caliper

Max Edge Max Edge

Outside Caliper Outside Caliper

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!

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

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

Tray of Aerators

Threshold Components

Normalize by Chroma

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

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

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

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

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

Laser Tools Setup for Height 1

LaserLine Camera

Camera view across pill width

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

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

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

Roughness as a Texture Filter

Roughness used to Suppress Texture

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

Edge Crawler (sub-pixel)

Tracks edges and reports their sub-pixel position

Can select individual contours

Older Crawler algorithm is integer pixel position

Corner Finder

Finds corners using the Harris corner detector

Corners are more constrained and therefore have more information than edges.

Corner Finder Applications

Applied to finding and counting flexible circuit connector “pins”

Hough Lines

Finds lines in noisy images

Hough transforms are “evidence-based” voting methods

Hough Segments

Finds line segments with specific length ranges

Very useful and works well

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

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

Draw Gaussian Example

Many test generators have “blending” option

Checkerboard Example

Testing Connectivity Analysis

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

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

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…