i optimized only this value considering others as constant.

my idea is that more number of non-corner points to lead to straight line and this results in error because the real world objects may be irregular in shapes deviating from square , rectangle..etc

Feel free to upload your graph to one of the free image hosting services (e.g. imgur.com) and link it here. Two things:

– Did you optimize this value based on the square shapes I use in this example here, or did you consider other shapes as well? I recommend you look through the Vossepoel and Smeulders paper to see how they did their optimization, and replicate that.

– Did you optimize only this new value, keeping the others constant? You should consider optimizing all 4 constants in your equation simultaneously.

One thing that just occurred to me is that count(noncorner) could be equal to total-count(corner), is it? — If so, this count cannot be adding any new information. I also notice that the new constant has zeros for the first three decimal places, which is the number of places at which the other constants have been rounded to. You might have recovered some of the lost precision from the other constants?

]]>perimeter =count(even)∗0.980+count(odd)∗1.406−count(corner)∗0.091+count(noncorner)∗0.000132

where noncorner is the number of times the non-corner points occur. i have the graph which shows that absolute error decreases with this modified equation. is there any way i can attach the graph here ? ]]>

That’s an interesting thought, I hadn’t considered that before.

If you have a sequence of one repeated code, you have a straight line along one of the axes or at 45 degrees from an axis (e.g. 0,0,0,0,0 or 1,1,1,1,1). Taking these into account could improve quantification for shapes with straight edges that are aligned with the sampling grid (such as rectangles, diamond and octagon shapes). But for lines at any other orientation you would get more complicated sequences. These are difficult to identify.

For generic shapes with long straight edges at arbitrary angles, an approach could be to record the outline as a polygon (i.e. keep the pixel’s coordinates rather than encoding as a chain code), and simplify this polygon using the Douglas–Peucker algorithm. The allowed error should then be half a pixel (so that sequences of chain codes like 0,1,0,1,0,1,0,1 or 3,4,3,4,3,4,3,4 become a single polygon edge). It is not clear to me that this would result in a correct object outline, but it *might* produce a more precise result. You’d have to test to make sure this is actually the case. Let us know if you do!

Thanks for information provided. i have one question can i modify the equation provided by Vossepoel and Smeulders in which i will consider the number of times the same number chain code occurs ? ]]>

That paper [Vossepoel and Smeulders, 1982] uses *n* as the total number of chain codes, and *m* as the number of odd codes. They write *a _{n}n* +

Compare to the Euclidean case, where even codes get 1 and odd codes get √2. An optimized method should have similar values to those. If you were to give odd codes only 0.426, you’d be undervaluing their contribution significantly! Why would a diagonal step be shorter than a horizontal step?

]]>Isn’t the coefficient for ‘odd’ orientations 0.426 instead of 1.406? Referencing the equation on page 362 of Vossepoel and Smeulders. Let me know your thoughts if you get a chance, I’m trying to analyze microparticle images in which the particle radius is approximately 6 pixels.

Cheers! Connor 🙂

]]>Of course the way that the continuous world is discretised influences the way measurements should be performed. In this post I assume point sampling. Most images you come across nowadays approach ideal point sampling. If you use thresholding to determine which pixels are part of your object, then you can use pixel counting as an unbiased estimate of area.

]]>I read some papers and Zenon Kulpa mentioned that the area or perimeter measurement are related to digitization scheme. I try to find the length of interfacial area in a polymermixing. I used thresholding method for object extraction . Is pixel counting method suitable for area measurement ?

Is there any conclusion of research works about relations between properties of the corresponding discrete objets and their nondiscrete originals ? Or are the Zenon Kulpas conclusions still valid ?

Best Regards

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