insight - Matrox

IMAGING 

INSIGHT Case 

Solutions, products and news from Matrox Imaging 

The Smart Camera 

Renaissance 

Vol. 10 No. 1 

Anatomy of a smart camera: 

Matrox Iris GT 

The Vision Squad Files: 

Use MIL’s GMF masks 

Study : 

The separation of salt and earth

Contents 

IMAGING INSIGHT Vol. 10 No. 1 

VP’s Commentary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 03 

Vice President of Sales & Marketing François Bertrand focuses on the 

light at the end of the economic tunnel 

Featured Product: Matrox Iris GT . . . . . . . . . . . . . . . . . . . 04 

Experience the smart camera renaissance 

Product News: Matrox Supersight . . . . . . . . . . . . . . . . . . 06 

Meet the biggest brother in the Matrox 4Sight family 

Case Study: Salt Sorter by Winn Hardin . . . . . . . . . . . 07 

Linescan camera-based machine-vision system images 

falling salt and enables contaminant removal 

Case Study: Novartis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

Automated microscopy system uses MIL to detect chromosomal 

abnormalities in cells 

The Vision Squad Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 

How to effectively use MIL GMF masks 

Trade Show Calendar 

Visit Matrox Imaging on the road! 

For tickets or more info, contact Joanna Puccio: jpuccio@matrox .com . 

IFSEC 

Birmingham, UK 

May 11-14, 2009 

2009 International 

Robots, Vision & 

Motion Control Show 

and Conference 

Chicago, USA 

June 9-11, 2009 

04 

06 

07 

10

IMAGING 

INSIGHT 

Publisher: Matrox Imaging 

Editor: Sarah Sookman 

Art Director: Roland Joly 

Marketing Communications: 

Catherine Overbury, Manager 

Reproduction in whole or in part, without the 

prior written permission of Matrox Imaging, 

is prohibited . For more information on 

articles published in this issue, or to share your 

comments, please contact the editor: 

Tel: 1-514-685-7230 ext. 2753 

Fax: 1-514-822-6273 

Email: imagingmr@matrox.com 

Want to subs cribe? 

Go to: 

matrox .com/imaging/news_events/newsletter/ 

Corporate headquarters: 

Canada and U .S .A . 

Matrox Electronic Systems Ltd . 

1055 St . Regis Blvd . 

Dorval, QC H9P 2T4 

Canada 

Tel: +1 (514) 685-2630 

Fax: +1 (514) 822-6273 

Offices: 

Europe, Middle East & Africa 

Matrox VITE Limited 

Chaplin House 

Widewater Place 

Moorhall Road 

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Fax: +44 (0) 1895 827301 

Germany 

Matrox Electronic Systems GmbH 

Inselkammerstr . 8 

D-82008 Unterhaching 

Germany 

Tel: +49 (0) 89 / 62170 0 

Fax: +49 (0) 89 / 614 97 43 

From the 

VP’s Desk 

The winter’s tale 

During a recent visit, a customer commented to us: “in our market, this is a very long 

winter” . Unfortunately, for a certain segment of our customer base, this is a reality; the 

economy is in a state of hibernation, and it’s on the news every day . 

In a recession, there are tough choices to make . Matrox Imaging’s strategy is to protect 

its existing customer base and engage in targeted marketing and product initiatives to 

prepare for the upturn, when it does come . 

That brings us to our Matrox Iris GT smart camera and Design Assistant software . This 

product bundle helps us to expand our customer base, to reach out to customers beyond 

the traditional OEM developer . 

Matrox Iris GT is built for automation experts who need to integrate vision . On the 

outside, the Matrox Iris GT has a rugged, dust proof, washable casing with IP67 rating, 

and lockable and waterproof M12 connectors, and even a VGA output . We’ve made it 

easy for the camera to communicate with automation devices though Ethernet/IP and 

Modbus® over TCP/IP . On the inside, we’ve got the Intel® Atom . The intelligence lies in 

the Design Assistant interactive camera configuration environment . The flow chart-based 

interface is intuitive and makes vision accessible to anyone who wants to solve a machine 

vision problem without complex programming tools . Design Assistant’s functionality is 

based on our field-proven Matrox Imaging Library (MIL) . What’s more, we are offering the 

Matrox Iris GT and Design Assistant bundle at a very attractive price point . 

Ironically, economic downtime often allows companies to redefine themselves and look 

at simpler and/or speedier ways to achieve their business goals . These times can become 

good opportunities to experiment; we believe the Matrox Iris GT makes experimentation 

a sound investment . 

Let’s get ready for spring! 

Commentary 

François Bertrand 

Vice President, Sales & Marketing 


03

04 

Featured Product: Matrox Iris GT 

Anatomy of a 

smart camera 

Matrox Iris GT 

Experience the smart camera renaissance with Matrox Iris GT . Our 2nd generation smart 

camera is more efficient, 20% smaller, and three times faster than its predecessor! 

Eye 

• Two sensor options (more in development) 

• 640 x 480 @ 100fps 1/3” monochrome CCD 

• 1280 x 960 @ 20fps 1/3” monochrome CCD 

• Direct intensity and strobe control of third-party 

LED light sources1 Language 

• Video output for image and operator interface 

• 10/100/1000 Ethernet port 

2,3 

• USB 2 .0 port 

• RS-232 serial port 

® 

• Support for Ethernet/IP and Modbus over TCP/IP 

communications to directly interact with automation 

devices (PLCs, robots, etc .) 

• Trigger input and conventional Strobe output 

2 

• Discrete I/Os including rotary encoder support 

IMAGING INSIGHT Vol. 10 No. 1

Featured Product: Matrox Iris GT 

Intelligence 

Matrox Design Assistant interactive development software 

• An interface where machine vision applications are created 

by constructing a flowchart instead of writing traditional 

program code 

• Image processing and analysis functions are based on the 

algorithms from the Matrox Imaging Library 

• Create flowchart steps to calibrate, enhance and transform 

images, locate objects, extract and measure features, read 

character strings, and decode and verify identification marks 

• Design a graphical operator interface for the application 

Power source 

® • Powerful embedded Intel Atom architecture processor 

• 

45 nm Ultra Low Power (ULP) CPU at 1 .6GHz 

Shell 

• Rugged, dust-proof, immersion-resistant casing with 

IP67 rating 

• Lockable and waterproof M12 connectors 

1 Support for select Advanced Illumination models . 

2 Support available through a future software update . 

3 USB port intended for keyboard/mouse/touch-screen only . 


05

06 

Product News: Matrox Supersight 

Set your sights on high-performance computing 

It’s big . It’s powerful . It can process billions of pixels per second . 

There’s a new high-performance computing (HPC) platform on the 

horizon . We’re so excited about it, we unveiled this new blue box at 

Vision 2008 in Stuttgart before its official release . 2009 promises to 

be the year of the Matrox Supersight, and Imaging Insight had a chat 

with Product Manager Dwayne Crawford about it . 

Imaging Insight: What exactly is the Matrox Supersight? 

Dwayne Crawford: Matrox Supersight is a high-performance computing 

platform for OEMs . We like to think of it as the big brother to the 4Sight 

family that we have been shipping for over 10 years now . What’s 

different about the Matrox Supersight is that it combines CPU, GPU 

and FPGA technologies into one or many computing clusters . 

II: So it’s an industrial computer . How does it differ from a highperformance 

PC? 

DC: The Matrox Supersight creates massive parallelism, enables 

large pipelines and removes I/O bottlenecks with PCIe x16 (Gen 2) 

interconnects between the processing nodes . By mapping the 

interconnects through an advanced PCIe switched fabric, nodes can 

be grouped into compute clusters that seamlessly share data without 

burdening other nodes within the system . The Matrox Supersight 

offers unprecedented processing performance in part because it’s so 

configurable . As CPU, GPU and FPGA technologies evolve, how the 

application is balanced between them will change . With the Matrox 

Supersight and MIL the developer can easily “re-cluster” the compute 

elements to ensure the application is optimally balanced between 

these technologies . 

II: What were the reasons for the Matrox Supersight’s development? 

DC: We constantly see high-performance imaging applications that are 

subject to hardware limitations . I/O limitations such as bottlenecks 

within blade servers and external cable connects between 1U pizza 

boxes pose a major challenge to creating effective parallelism . 

Furthermore, in order to support acceleration hardware such as FPGAs 

Matrox Supersight 


and GPUs, physical slots with sufficient bandwidth, power and cooling 

are required – and they are not always available in blade and pizza 

box environments . It’s possible to accelerate image processing to its 

maximum with the judicious application of multiple GPUs, CPUs and 

FPGAs all working together . The Supersight hosts all these devices yet 

circumvents the I/O, space, power and cooling limitations of current 

processing platforms . 

II: What are the other benefits of the Supersight? 

DC: Consumer PCs are built and sold for the short term, typically 

around 12 to 14 months . OEMs make significant investments in 

development and need products that are available for the long term . 

Matrox provides 5 to 7 years of availability for its products; we help 

OEMs get the maximum return on their investment and avoid costly 

and constant re-validation of new platforms . Furthermore, companies 

in the medical and pharmaceutical industries face strict governmental 

regulation to ensure public safety . Unforeseen changes can freeze 

production, and in a worst case scenario, even result in product 

recalls . Because change is unavoidable, all Matrox Imaging hardware 

products, not just the Matrox Supersight, benefit from our managed 

life cycle programs . We can manage products for long-term fit, form 

and function . We can also offer the option of strict revision control 

that includes prior change notification, risk analysis and acceptance 

sign-off . 

II: Will the Matrox Imaging Library (MIL) support the Supersight 

platform? 

DC: Absolutely . Naturally our hardware and software are optimized 

for each other – that’s another benefit . OEMs who work with a single 

supplier spend far more time adding value to their product – they 

focus on developing their application without the struggles of software 

and hardware compatibility issues . Matrox validates both hardware 

and software changes to prevent unexpected incompatibility in the 

field .

Salt Sorter 

By Winn Hardin 

Dataschalt’s DataSort system can find and remove stones from a cascade of falling salt 

crystals in real time using a single 2000-pixel linescan camera. 

Case Study: dataschalt 

Linescan camera-based machine-vision system images 

falling salt and enables contaminant removal 


07

08 


RHEWUM has built screening machines and vibrating feeders since 

the 1950s . To keep its customers ahead of the productivity curve, 

RHEWUM decided it needed to incorporate an automated sorting 

system to its screening systems—in particular, using machine vision 

to sort rocks and other contaminants from salt as it falls from a chute 

to a collection bin . 

Hitting a stationary target is relatively easy; however, directing an 

air jet to hit a falling stone as it cascades along with a sheet of salt 

crystals from a chute to a bin is not an easy task . RHEWUM turned to 

industrial electronics and food sorting specialist dataschalt Group to 

build a system capable of detecting falling contaminants in real time 

and interfacing with RHEWUM’s OEM screening equipment to blow the 

contaminants out of the product stream . 

Dataschalt accomplished the task by integrating machine-vision 

components from Matrox Imaging and Basler Vision Technologies into 

a system capable of sorting salt crystals in real time at a rate of up to 

120 tons per hour . 

Speeding the sort 

Until recently, microprocessors and other computational elements 

were not powerful enough to sort contaminants from hundreds of salt 

crystals falling through space . To use air to blow falling contaminants 

from cascading salt crystals, the vision system needed to be able to 

classify each crystal based on size, shape, and location, and then 

provide a bank of air jets with the specific location of the contaminant 

as it is falling from feeder chute to collection chute . A PMC I/O card 

inside the PC host then triggers the appropriate air jets, which blow 

the contaminant into a defect bin . 

“Because we are sorting products when they are falling we need to 

reduce the time between object scan and object rejection,” explains 

Kai Schwarz, automation engineer at dataschalt . “This is necessary 

because the behavior of a single falling object within the product 

stream is actually nondeterministic, but if the time between scan 

and shot is small we can assume that the object has moved linearly 

and will be able to hit it with the air blast . The air blast will always hit 


‘good’ product as well but this is accepted . This would be easier with 

a conveyor belt but you wouldn’t be able to sort the same amount of 

product . So we were looking for a very fast real-time [image-processing] 

platform with the capability to process blobs .” 

Schwarz says that a sorting machine already existed that processes 

pixel by pixel completely in hardware with a processing time faster 

than 4 ms, but this platform was not able to process blobs and 

therefore had limitations . Dataschalt evaluated the Tsunami embedded 

computing board from SBS Technologies (now GE Fanuc), but the 

Odyssey Xpro+ platform from Matrox had the advantage that it comes 

with the Matrox Imaging Library (MIL), which solved nearly all of the 

image algorithm problems and enabled dataschalt to concentrate on 

the process itself . 

“The Odyssey provides the possibility to program the PowerPC on the 

card itself,” adds Schwarz . “So we had a really fast processor with 

real-time capabilities that gets the image information directly [from 

the camera] via the [board’s] internal bus .” The board had a limited 

number of I/O channels to control the valve battery and so Matrox 

developed a solution by using their carrier board and a third-party PCM 

I/O with 64 I/O channels . Schwarz says, “These channels were enough 

for our valve control hardware—our DataSort Sorting Application (DSA), 

which consist of a transmitter unit, a receiver unit, and an optical fiber 

connection . The valve triggers are now written from the Xpro+ via the 

PCI-X bus to the PMC-IO-Card and we have no bandwidth problem .” 

Crystal-clear sorts 

During the salt sorting application, crystals are poured into a feeder 

bin and a vibrating conveyor channel separates the salt into a thin 

layer of crystals 2 m wide . The crystals then accelerate down a chute 

and freefall across a short gap before landing on a collecting chute, 

which funnels the good product into a collection bin . 

To correct for changes in illumination across the 2-m chute, the system uses a 

calibration bar at the beginning of each shift. The data is stored and used by the MIL 

software to correct images prior to thresholding and blob analysis.

Mounted on either side of the salt crystals are banks of white LED 

lights to illuminate the crystals as they fall . On one side of the 

crystal cascade is a bank of air valves that can be turned on and off 

independently by the PC based on information from the MIL software 

running on the Xpro+ image-processing board . 

To begin the sort, the operator sets a calibration bar within the Basler 

Vision L103KM-2K Camera Link linescan camera . This allows the MIL 

software to correct for changes in illumination across the 2-m-wide 

chute . dataschalt chose the L103 because of its relatively high pixel 

resolution and fast acquisition speeds . The clock runs at 18 .7 kHz, 

compared to 9 kHz for the L101 camera, which is important when trying 

to image falling product in real time and with high spatial resolution 

from a linescan camera . The camera clock and frame data passed to the 

MIL software also act as the synchronization signal for triggering the air 

jets to blow out contaminants from the salt crystal stream . 

As salt crystals begin to fall in front of the camera, each line along 

with its time stamp is passed to the MIL software . The Xpro+ board 

features an ASIC to accelerate certain image-processing algorithm 

convolutions running on the nearby G4 Power-PC processor; a FPGA 

handles filtering, normalization, and similar repetitive tasks based on 

the specific inspection routine . 

The board collects line images in local buffer memory, checking 

each pixel for intensity variations that indicate the pixel is either a 

background pixel, salt pixel, or—if darker— indicative of a stone or 

contaminant . Once contaminant-level pixels are identified, the MIL 

software runs a blob analysis to determine whether the contaminant 

is large enough to warrant rejection by the air jets . 

“The stones are darker than the salt,” explains dataschalt’s Schwarz . 

“Often they are included within the salt pieces . So we examine a 

blob and count the amount of dark or ‘bad’ classified pixels . The user 

defines beforehand the percentage of bad pixels that are allowed per 

piece .” The salt pieces are sometimes large (approximately 10 × 10 

cm), so often it is not enough to shoot only the bad pixels [from an 


embedded stone] but it is essential to recognize the complete piece 

and turn on enough valves to reject it . Furthermore, it is possible to 

sort the pieces by size . 

Once the MIL software determines that a contaminant has exceeded 

the allowable size, the horizontal location information and time 

stamps for the defect are passed across the compact PCI bus inside 

the host PC, through the carrier board to the Acromag PMC464 I/O 

board . Using its 64 I/O channels, the PMC card then sends that 

location information to the dataschalt DSA electronic control system, 

which fires specific valves in the bank of 255 air jets that stretch across 

the sort stream . The jets blow the defective crystal or contaminant out 

of the stream and into a defect bin . 

Adding color to the mix 

While the salt-sorting application only required monochrome images 

to differentiate white salt crystals from darker stones, many foodprocessing 

applications require more data to pick good product from 

bad . For these applications, dataschalt uses the same processing 

hardware with a color camera . To accommodate the additional 

data while maintaining high-speed processing, Schwarz says he 

uses principle component analysis (PCA) techniques to remove one 

dimension in the RGB color space that holds the least amount of data, 

essentially compressing the 3-D color space into 2-D . 

The throughput of the DataSort depends on particle size, the material’s 

bulk density, and the width of the machine . Currently, the sorting 

capacity can be up to 120 t/h at a machine width of 1200 mm . 

“We are very happy with the fast-working, high-tech solution we have 

received from dataschalt,” says Sigurd Schuetz, managing director 

of RHEWUM . “It enables us to provide a reliable and cost-efficient 

sorting technology to our customers, who greatly benefit from this 

combination of dataschalt and RHEWUM technological knowledge .” 

This article originally appeared in the January 2009 issue of Vision 

Systems Design . Used with permission . 

The MIL software compiles a complete image of a falling stone from individual 

line images and then uses the location across the linear array, plus the time 

stamps from each line associated with the stone, to provide location information 

to the dataschalt DSA system, which controls the bank of 255 air jets. 


09

10 

Case Study: Novartis 

It’s in 

the genes 

Automated microscopy system uses MIL to detect 

chromosomal abnormalities in cells 

The comet assay quantifies the DNA damage in a cell. The DNA particles migrating in an 

electric field are stained with propidium-iodide. The comet assay locates suitable nuclei 

and analyzes these “comet tails” using parameters such as tail length, intensity, tail 

moment etc. The number and brightness of particles directly correlates to the level of DNA 

damage. 

IMAGING INSIGHT Vol. 10 No. 1

The drug discovery process not only develops new drugs that cure, 

control, or prevent disease . This process must also include rigorous 

testing for severe side effects . This is particularly true for drugs that 

are intended for long-term or even lifetime use, such as anti-rejection 

drugs for transplant patients . 

Long before human trials can proceed, drugs must be tested for their 

efficacy and safety . The Preclinical Safety unit at Novartis (Basel, 

Switzerland) has the responsibility of determining adverse side effects 

of all new molecular entities (drugs) in vitro (i .e ., in cultured cells) 

and in vivo (animal testing) . Besides other toxicological studies, the 

researchers perform genetic toxicology assays (tests) to assess the 

genotoxic potential of new drug candidates . Drug effects on mammalian 

cells are studied, using chromosomal abnormalities (besides other 

cellular and nuclear endpoints) to assess the compound’s potential 

hazards . When such abnormalities occur, it means that the drug in 

question has interacted with the cell’s genome . Dr . Wilfried Frieauff, 

Laboratory Manager of the Preclinical Safety unit says that when a 

statistically and biologically significant increase in these events are 

observed, there’s a potential for the drug candidate to induce cancer . 

Frieauff manages the labs, and he is responsible for the image analysis 

platform – its development, system maintenance, and support . The 

Preclinical Safety unit runs hundreds of tests annually on literally 

millions of cells . Given the numbers, it’s no surprise that the analysis 

process is one that benefits from automation . 

At the end of the 1980s, Frieauff’s lab began to use Leitz MIAS image 

analyzers for that purpose . But by the late 1990s, Dr . Frieauff realized 

that the systems were seriously outdated . “It was a stand-alone system 

that could not be part of a network . Replacement parts were no longer 

available,” he explains . These early image analysis systems had a 

maximum capacity of processing only 16 slides in a row . “Sometimes we 

would load the slides on Friday, and the system would finish and sit idling 

until Monday morning .” More importantly, Frieauff expected the future 

would bring more image analysis development projects and he “realized 

that it was time to totally re-engineer and modernize our image analysis .” 


After extensive research of the market, Frieauff selected several 

components that would comprise the new ROBIAS, the Robotic Image 

Analysis System . ROBIAS is built from an electronically-driven Leica DM 

RXA/2 microscope mounted with a SONY DXC-390 color CCD camera 

that is connected to host PC with a Matrox Meteor-II/Multi-Channel 

frame grabber . An optional robotic slide feeder can automatically 

place up to 130 slides on the microscope’s stage for processing, which 

is performed by using custom-developed image analysis algorithms 

based on the Matrox Imaging Library . 

Automated microscopy 

Due to the subjective nature of manual microscopy and the increasing 

need for accurate, standardized results, image processing techniques 

have been used in microscopy for some time . Indeed, medical 

diagnostic and pharmaceutical applications need precise results 

– lives are at stake – and automated microscopy offers complete 

objectivity (thanks to image processing algorithms) and relieves the 

human eye of difficult and fatiguing tasks . 

The ROBIAS system performs specific cytogenetics assays (cell-genetic 

tests) that examine both in vitro and in vivo samples: 

• micronucleus test (MNT) in vivo (regulatory test to be conducted for 

drug registration) 

• comet assay in vivo 

• metaphase finding for chromosome aberration analysis (regulatory 

test to be conducted for drug registration ) 

• MNT in vitro (for early genotoxicity screening) 

• comet assay in vitro (for early genotoxicity screening) 

• MNT in primary human lymphocytes (currently under development) 

By studying the cells’ DNA, the researchers can add evidence to a drug’s 

relative safety, or identify potential hazards . For the micronucleus 

test or the chromosome aberration analysis, the researchers look 

for chromosomal abnormalities which become visible when the cell 

passes through mitosis . 

The Robotic Image Analysis System (ROBIAS) is built from a electricpowered 

microscope and an optional robotic slide feeder . 


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Metaphase is the stage of mitosis where the chromosomes migrate to the center of the cell 

before it divides. This metaphase test is the basis for chromosome aberration analysis. This 

image shows the “collection” of chromosomes of a dividing cell. 

As an example, a micronucleus reflects a genotoxic insult because 

it originates from a chromosome strand break due to the compound 

interaction with the nuclear material . Direct DNA lesions are assessed 

using the comet assay where DNA particles migrating in an electric field, 

are stained with a fluorescent dye and can be measured to assess the 

overall DNA damage following the compound administration . 

Summarizing genetic toxicology’s mission - if the drug does not damage 

a cell’s DNA, an important hurdle is passed for being a good candidate 

for later Phase 1 clinical trials . 

M is for morphology 

MIL markedly supports the researchers to extract the vital information 

they seek . After “discovering” MIL in 1999, Frieauff found that it was 

“the most flexible and ready-to-use tool library which fully supported 

mathematical morphology functions, as well as advanced blob analysis, 

both very important for biological image analysis .” These functions 

comprise the “core” of Frieauff’s software . With more than 20 years of 

experience in image analysis development, Frieauff had already built 

some powerful software tools based on low-level imaging functions . 

MIL’s functional interface simplified the overall software development . 

“For my applications,” he explains, “automated segmentation of 

biological structures, that is, cells containing nuclei or other cellular 

patterns, is crucial . Thus, I developed automated segmentation 

functions for various specific applications in industrial toxicology .” 

The “various specific applications” of which Frieauff speaks is one of 

the unique features of ROBIAS . “You may find diverse systems covering 

individual solutions,” he says, “but not one system covering all these 

tox[ological] assays on one platform . That was the main idea behind 

ROBIAS .” 

Currently, five ROBIAS units are deployed in Novartis locations, plus 

an additional two systems without the robotic slide feeder . These days 


In this micronucleus test, two cells show micronuclei, evidence that the drug compound 

interacted with the cell’s genetic material. 

Frieauff manages the laboratory’s toxicology assays and oversees all 

aspects of ROBIAS . Always in the midst of new application development, 

he is currently investigating sophisticated details of the MIL’s Edge 

Finder module . He is also working through the process of patenting 

ROBIAS . His determination has certainly paid off . In fact, in 2007 

Frieauff received the Novartis VIVA Leading Scientist award . “I was 

extremely proud,” recalls Frieauff, “the award reflects the importance 

of the ROBIAS system and the development of fully automatic image 

analysis applications for various units within Novartis Pharma research 

and development .” 

References: 

Sookman, Sarah . “In Living Color Microscopy tool uses MIL to automate 

color-based cell analysis” Imaging Insight 5 .3 (2003): 4-5 .

The The The 

VISION VISION 

SQUAD SQUAD 

Using MIL 

The Vision Squad Files: MIL GMF masks 

Geometric Model 

Finder masks 

Pattern recognition is used to locate an object in an image. For that reason, it is one of the most widely used 

operations in the machine vision industry. While the normalized grayscale correlation (NGC) technique uses a pixelby-pixel 

comparison, the MIL Geometric Model Finder (GMF) uses a technique based on the geometric features of an 

object. GMF is not only more flexible, but also more tolerant of lighting variations, model occlusion and variations 

in scale and angle. One way to get even better results with GMF is to apply masks to the search model. This Vision 

Squad article describes how to use the various masking capabilities of GMF so that the tool can be used to its full 

potential. 


13

14 


Determining model and target coverage 

The model coverage quantifies the model’s edges found in the 

occurrence or target. Model coverage of 100% means a perfect 

match, where every edge element in the model image has found 

a corresponding edge element in the target. Missing or occluded 

edges in the target decrease the model coverage. In Figure 1, 80% of 

the model’s edges are found in the first occurrence and 50% in the 

second. 

Model edge map 


Edge map + Mask 

Model coverage 

el weight Positive Negative 

et contribution 

Target coverage 


Flat region mask 



The flat region mask (M_FLAT_REGIONS) Edge map + Mask is used to Model Final mask edge coverage the mapmodel 

edges only. Note Target 100% that A the corresponding Target target 100% B edges that fall in the masked 


100% 

80% 

area still contribute to the target coverage when using a flat region mask. 

Model coverage 100% 

Model Target A 

Target B 

In Target A, both the model and target coverage score 100% since all edges of the model are found in the target and vice-versa. In Target B the 

< 100% b 

model coverage is 100% because the remaining edges in the final model are found in the target; however, the target coverage decreases to 80% 

because of the extra edges (Figure 4). 


Enhanced 100% model 100% 

Target A 


100% 

100% 


Target B 

Ta 

Enhanced model 


Target edge map 

Missing edges Occluded edges 


80% 50% 

Figure 1. Figure 2. 


Model coverage 80% Edge 50% map + Mask 

Missing edges Extra edges 

Model 

Final 

edge 

edge 

map 

map 

100% 60% 



Model coverage Target coverage 



100% 

100% 

Target edge map Target coverage 

Missing edges 

Target A 

Extra edges 

Target B 


100% 


100% 

100% 100% 

60% 

100% 

100% 

The target coverage quantifies the percentage of the target occurrence’s 



edges present in the model. This means that edges that are found in 

the occurrence but not in the model are considered extra edges. 

Therefore, they reduce the target coverage. In Figure 2 (below) the 

target coverage is 100% in the first occurrence because all of the 

target edges correspond to edge elements in the model. In the second 

occurrence, the Model target coverage 

80% is lower because only 60% 50% of the 

target edge elements correspond to the model’s edge elements. 


100% 

100% 

100% 

80% 




< 100% but still very high 


100% 60% 

Final edge map 

Model Target coverage B 




Masks 

Masks are applied to models for two reasons: either to remove any irrelevant, inconsistent or featureless areas of the model, or to modify the 


Positive Negative None 

relative contribution of model Model edges edge mapto 

the model coverage. Missing edgesThe GMF module Extra provides edges three types of masks that have different effects on model 


100% 

coverage: don’t care, flat region and weighted. 


100% 

Don’t care mask Model coverage 


Missing edges 

Target 

Occluded 

coverage Edge edges map + Mask 


Target A 

The don’t care mask (M_DONT_CARES) masks any irrelevant regions in the model and the corresponding region in the target. Masked edges in the model 


Final edge mapTarget 

coverage 100% Target A 

60% Target B 

do not contribute to the model coverage, and any edges in the target’s corresponding region will not contribute to the target coverage (Figure 3). 

Figure 3. 





Final edge 80% map 

50% Target A 



In Target A, both the model and target Model coverage coveragescore 

100% since all edges of the model are Target found coverage in the target and vice-versa. In Target B, the 

model coverage is 100% because Edge map + all Mask edges of the model Final edge are map found in the target; the target Target Acoverage 

is also 100% Target because B the two circles fall within 

the corresponding region masked by the don’t care mask. 

Figure 4. 







Target A 





100% 

100% 


100% 

Target A Target B 


80% 

100%

ative 

ative None 

ative 

ative None 


Additional tips: 






80% 50% 

Missing edges 


Target edge map Extra edges 


Use the following guidelines to maximize the effect of don’t care and flat region masks on both model and target coverage: 

• Use don’t care masks to remove edges that lie “outside” the model geometry (sometimes associated with background noise). This will 

prevent these model edges and any irrelevant edges found in the corresponding target region from lowering the model and/or target 

Model coverage 100% 60% 

scores . 

Model coverage 80% 50% 

• Use flat region masks to remove edges that lie “inside” the model geometry. Such edges may be associated with extra geometry, for 



example, that could indicate an error in the manufacturing process; therefore these edges must be reflected by the target coverage . 



Target A 

Target B 

• If masked regions overlap, don’t care regions Target edge map take precedence over flat regions. 



• Masks usually speed up the matching process because they reduce the number of edges to process during a matching operation. 








100% 

100% 

Target A 


Weighted mask Edge map + Mask 


Target A 

Target B 

One very useful application of the weighted mask (M_WEIGHT_REGIONS) is when a model contains a major repetitive pattern . Models based on 

repetitive patterns can generate unstable results in real images . For example, false positives are possible simply because the occurrence might 


100% 

100% 


100% 

80% 

have a “better” coverage than the desired match due to some missing or extra or occluded edges, or have coverage above the certainty threshold 


100% 

100% 

set by the user . A target can get ‘better Target model coverage coverage’ because 100% the omission of 100% a few elements of the repetitive pattern does not significantly 

lower the model coverage (Figure 5) . 





Figure 5. 




Target B 

The model coverage for Enhanced Target B model in Figure 5 is still high Target enough A to be considered a match Target B because the extra geometries offset the desired match 

without significantly reducing the model edge elements found in the target . 

A weighted mask adjusts an edge element’s importance when the model coverage is calculated . Target edges that match model edges with 

negative weights will be ignored when the target coverage is calculated . To reduce ambiguity in repetitive patterns, first add “virtual” copies of the 

pattern . Then, mask the copies with negative weights (Figure 6) . 



Figure 6. 




100% 60% 




Target A 

100% 

100% 



The enhanced model has eight “pins” that have a negative mask (red lines) . When the model assesses Target B in Figure 6, Edge the map negative 

+ Mask 

weights significantly lower the model coverage of the misaligned/offset candidate, enough to reject this occurrence as a match (depending on 

the acceptance/certainty thresholds set by the user) . Therefore, even though the model is offset in Target B, the negative weights ensure the 

occurrence does not count as a match; only the desired match (Target A) will receive a high model score . 

100% 

100% 

Target B 

Target B 

100% 

80% 

Target B 

100%

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