Category: Tutorial

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Battle of the Image Sensors – Sony IMX174 Vs CMOSIS CMV2000

 

Sony Pregius imaging sensors with a newly advanced pixel design provide unsurpassed high dynamic range, quantum efficiency and excellent signal to noise (SNR) ratios.  With a Dynamic range of 73 dB and a quantum efficiency of 76% (@ 525nm), this sensor will become a major work horse in the industrial imaging market.  Comparing these data points however can be mind boggling and leave you asking, “What’s the end result?” 1st Vision put the CMOSIS CMV2000 and new Sony Pregius IMX174 into the ring to battle it out!   
 
The old phrase of “A picture is worth a thousand words” definitely stands true in comparing sensors and reviewing images.  In our battle, we compared the CMOSIS CMV2000 (2MP CMOS)  sensor to the new Sony Pregius IMX174 (2.3MP CMOS) sensor.  We reviewed several of the key sensor aspects and are as follows. 
Specifications

From the specification standpoint, the IMX174 provides better values across the board.  (Refer to the SonyPregius Sensor page for detailed information on the terms)

   Comparison Data:


Round 1:  Saturation Capacity & Dynamic Range

Saturation Capacity or “well depth” is analogous to a bucket of water, however in lieu of water, we have electrons.  Given a larger bucket, we can pour more water in the bucket without it overflowing.  The water overflowing is analogous to “saturation”.  In turn, a sensor that has a larger saturation capacity (bigger bucket) can hold more electrons (water) without saturating (overflowing).  This would relate to the sensors overall dynamic range in that it will not saturate quickly in a given image, allowing us to see darker and lighter areas. 

The example below shows images comparing the CMV2000 and IMX174.  These were captured with the same lens, camera manufacturer and set at the identical exposures.  

Looking at these images, which one is the better sensor?  We would want to quickly say, “The brighter one!”, however brighter does not always mean better!

On the left CMOSIS image, we see a noticeable difference as it is starting to saturate especially on the lower right corner.  The colors are reaching peak levels in which they are close to saturating (brighter image).  For this given light level, our small “buckets” are getting full!

On the right IMX174 image, we can see similar brightness levels in the upper left corners, but the bottom right part of the image is NOT saturating, as well as the markers are not reaching peak values.  In essence, we have a small bucket with the CMV2000 and will overflow much faster than the larger bucket with the IMX174.  Relating back to our values in the chart, saturation capacity and dynamic range are much greater on the IMX174 and can be visibly seen in the images!  Sony Pregius wins round 1.

(Knowing this.. the brighter image does not always mean its a better sensor! )


Round 2:  Quantum Efficiency (Sensitivity)
In comparing the Quantum Efficiency values, we see the IMX174 is 13% higher.  This relates to the conversion of photons to electrons and in turn providing a higher numeric value in our image.  The images below were taken in very low light with a 10uS exposure.  The IMX174 provides a brighter image due to this added efficiency.



Sony Pregius IMX174 wins round 2 with a Knock Out!

The Sony Pregius pixel architecture is being expanded into many resolutions and will implemented into many industrial cameras.  

For a complete list of cameras with Sony Pregius sensors click HERE.  Keep an eye on this link as it continues to expand.  Need more technical information on the Sony Pregius sensors?  Click HERE

Contact us for a quote on cameras with the Sony Pregius sensors and to discuss your application!  1st Vision can provide a complete solution including lenses, cables and lighting.  

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Imaging Basics – Calculating Lens Focal length

In any industrial imaging application, we have the task of selecting several main components to solve the problem at hand.  The first being an industrial camera and second,  a lens to acquire the given image.  In many cases, our working distance of our lens is constrained and may have to mount the camera closer or further from the object plane.  Once set, this defines our working distance (WD) for the lens.  In addition, we have a given field of view (basically the dimension across the image) of the desired object.  


In order to select the correct focal length lens which is denoted in millimeters (i.e 25mm focal length), we need additional information on the camera sensor.  Camera sensors come in various “Image formats”.  The chart below indicates some common formats which relate to the sensor size.  The sensor size can be found on the actual sensor datasheets if not available in a given chart.  


For this exercise, we want to image an object that is 400mm from the front of the lens to the object and desire a field of view of 90mm.  

We have selected a camera with the Sony Pregius CMOS IMX174 sensor.  This uses a 1/1.2″ format which measures 10.67mm x 8mm.  


We have the following known values at this point: 

Field of View (FOV)  =  90mm
Working Distance (WD) = 400mm   
Sensor Size = 10.67mm – We will calculate for a 90mm horizonal FOV, in turn use the horizontal sensor dimension

The basic formula on how to calculate the lens focal length is as follows:

FL = (Sensor size * WD) / FOV

Using the values from our application, 

FL = ( 10.67mm * 400mm ) / 90mm 
FL = 47.4mm

Lenses are only available off the shelf in various focal lengths (i.e 25mm, 35mm, 50mm), so this calculate is theoretical and may need an iteration to adjust working distance. Alternatively, if your application can have a slightly smaller or larger FOV, the closest focal length lens to your calculation may be suitable.


1st Vision has made calculating your lens focal length a bit easier!  As in engineering, its good to know the background formulas, but in practicality, like to simplify life with tools

You will find our lens calculator HERE.  Alternatively as select a camera, you will find an icon to the right which will automatically populate the calculator.  Below is a short video showing how to use this resource from the camera pages.  





A few additional considerations when selecting a lens:

  • Lenses have minimum working distances (MOD), so this should be considered when reviewing a lens setup.  MOD’s can be found on the lens page for the given lenses.
  • Lenses need to be paired with the appropriate sensor.  For example, if you have a 1/2″ sensor, you need to ensure you are using a 1/2″ format lens or larger.
  • In selecting a lens, you need to ensure the lens has enough resolution (in lp/mm) to resolve the pixels on your camera.  Be sure to review this data carefully once you ID the desired focal length.  Demystifying lens specifications provides further understanding. 


Related Blogs: 
Demystifying Lens Specifications
Understanding Lens MTF
Calculating Resolution for Machine vision


Contact us to discuss your application and help make a recommendation!  1st Vision can provide a complete solution including lenses, cables and lighting. 

www.1stvision.com  
Ph:  978-474-0044

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Not All Lenses are Created Equal! Lens MTF Comparisons

In our previous blog, Demystifying lens specs,  we discussed the Modulation Transfer Function, also known as MTF, which gives you the performance of light through a medium.  The MTF helps us understand lens characteristics, however it is extremely hard to compare using manufacturer’s data sheets.  In essence, looking at a 25mm / f1.4 lens from vendor A to vendor B may look similar with basic information, but they are not!  Not all lenses are created equal and in turn need extensive data for comparison. 

The problem with comparing lens MTFs.

The problem is that most lens manufacturers DO NOT supply MTF information, or do not supply complete MTFs. Lens manufacturers with high quality optics, such as Schneider Optics, are one of the few that provide a complete set of MTFs vs. transmission.  (See an example on Pg 2 on this datasheet.)  Many will provide basic information in terms of line pairs/mm (lp/mm) measured in the center of the lens, however this is still not enough for a true comparison.  MTF data will vary with aperture (f3), light intensity and distance from the center to edges.  In turn, if you are not comparing “apples to apples”, you cannot draw a conclusion on which is the better lens.

Can I just measure the MTF myself?

The short answer to this is: Not so easily! First off, the MTF of a computer monitor is probably around 30 lp/mm. All the lenses we are discussing in this blog are at least 2x this, if not 3 or 4x it. So the limiting factor is the monitor, and you will not be able to see any differences. If you have a resolution chart, and some software where you can get the actual pixel data and plot it vs. the test pattern, you can get a better idea. However, a fairly rigid test set up with constant lighting, constant exact FOV and other identical parameters is required. This is a very lengthy process and requires special equipment. True optical testing is the correct way to determine and compare MTF.

Bottom Line:  1st Vision has done extensive testing on many lenses and have true comparisons.  We can help you determine which lens is the best for your application!…. Unless you have some nice optical equipment and some time!
Contact us to discuss the application and we can help make a recommendation!  1st Vision has 100’s of lenses in stock for same day delivery!


 Our lens webpage also highlights the resolution and distortion BUT again does not tell the whole story!

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Imaging Basics: How to Calculate Resolution for Machine Vision

 

 


 

 
Camera image resolution is defined by the number of pixels in a given CCD or CMOS sensor array.  This will be identified in a camera data sheet and shown as the number of pixels in the X and Y axis (i.e 1600 x 1200 pixels). 
The application will determine how many pixels are required in order to identify a desired feature accurately.  This also assumes you have a perfect lens that is not limiting resolving the pixel (see Demystifying lens specifications).  In general more pixels is better and will provide better accuracy and repeatability.  
 
 If for example you have a dark hole on a white background filling your field of view (FOV) by 90%, you will have many pixels across the feature.  On the contrary, if we have a small pin hole that is within the same field of view, we may not have enough pixels across the hole to identify the feature.  In order to find an edge you need at a minimum of 2 pixels given excellent contrast.  In order to be robust you ideally will want 3-4 pixels across a edge or feature.  
This leads us to identifying the resolution required given the size of a feature.  We will do this with an example and provide the needed formulas on how to calculate the resolution.
Example:  The vision inspection is to locate a pin hole which is 0.25mm in diameter on a part which is 20mm square.  In order to compensate for any misplacement of the part, we will set our FOV to 40mm x 30mm.  We would also like to have a minimum of 4 pixels across the 0.25mm feature.  

We can calculate the resolution required as follows:

Where:

Rs is the spatial resolution (maybe either X or Y)
FOV is the field of view dimensions (mm)  in either X or Y
Ri is the image sensor resolution; number of pixels in a row (X dimension) or column (Y dimension)
Rf is the feature resolution (smallest feature that must be reliably resolved) in physical units (mm)
Fp is the number of desired pixels that will span a feature of minimum size.

For this case we know: 

FOV(x) =  40mm
Rf = 0.25mm
Fp = 4 pixels

Calculating the spatial resolution (Rs) needed:
Rs = Rf / Fp = 0.25mm / 4 pixels = 0.0625mm pixel

From the spatial resolution (Rs) and the field of view (FOV), we can determine the image resolution (Ri) required (we have only calculated for the x-axis) using this calculation:

Ri = FOV / Rs = 40mm / 0.0625 mm/pixel = 640 pixels

We have now determined that we need a minimum resolution of 640 pixels in the x-axis to provide 4 pixels across our feature that is 0.25mm in diameter. The camera resolution can now be selected!  In today’s world, we could select a VGA (640 x 480) camera for the application.  As a note, the number of pixels required depends on many aspects of lighting, optics and algorithms used for processing.  This calculation method assumes optimum conditions.     

If you do not like math, you can download our resolution calculator here and just enter the data.  This makes it easy to test various iterations.  Download the calculator HERE. 

If you visit our camera page, you can sort by resolution in X and Y resolutions to quickly ID cameras that meet your resolution needs. 

For all your imaging needs, you can visit www.1stvision or contact us! to discuss your application in further detail or receive a quote on a desired camera.  We can also help identify which sensor is best based on the imaging conditions.  
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