Have we chosen a viable approach? Wood ring orientation recognition

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In wood industry, there is an requirement when you glue together wood boards into plys, that the rings should be facing in the opposite way (i'd imagine it's because there is more stability in this form), so we have to analyze the rings and determine in which they are facing. I'll look into your suggestion tomorrow, thank you very much.

Regards, Nejc

I have a sheet of plywood in front of me right now :) That requirement is about wood fibers. On good material rings should not be present at all. So far as I understand, fibers go along the trunk of the tree. On wood veneer they should go either horizontally or vertically. You can't distinguish up versus down, only up-down versus left-right. Your picture is wrong, not typical and fibers are not visible at all. Throw it out and find a better sample. Feature detection is the only approach possible for your task.

I think there must be some misunderstanding. This is not plywood, but solid structural timber (example: http://www.bischoff-schaefer.de/en/pr... ). Before this kind of product is pressed (you can see several layers of boards I've posted in the first link) there will be a camera which will analyze the position and orientation of wood rings, so the board can be turned around for maximum structural integrity. With the approach we've chosen, what I need to know is whether this is viable approach (opencv edge/contour detection, along with machine learning algorithm based on many boards where slope coefficients are calculated) or is there huge improvements on method we should use.

Regards, Nejc

For such tasks details may be crucial. Let's clarify the terms used. You have boards which were cut along the tree trunk (board plane goes along the trunk axis) and your first image presents the end face of the board. In this case you should have perfect, geometrically correct arcs on the image (I guess this company uses 1 sort wood :)). What do you mean by orientation? One board must have arc ends up, another - down?

That is correct.

Your approach is quite rational. I don't see major flaws, but some improvements are visible.

Ok, so the improvements would be what you stated in original answer? I've made this question because as I stated in the first post, we have very little experience in both computer vision and machine learning, so if we would've chosen a wrong approach it could mean months down the drain because of inexperience. If you have some other improvements in mind, please share them with me. Anyways, thank you for the input regardless.

Regards, Nejc

I think, this task has at least 2 different solutions with comparable quality. You can choose from them by circumstances(such as the quality of input image). If you want machine learning, feature detection is right for you. This pair will be "scientifically correct". Meanwhile, these images have good structure. Features are used for surfaces with a texture. Here, you have complete lines and even with analytical representation. Hough transform may be used to detect straight lines and also circles. I didn't try the latter yet, but plan to do it in the future.

Thank you for feedback. I will not mark this as answered yet, as I'd like some more input but if everyone else remains quiet, I'll mark your answer as correct because I've been directed in promising direction. Regards, Nejc