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I am designing a robot which irons and folds clothes autonomously. For this purpose, I need the robot to detect a certain number of key points in a given cloth in order to execute a certain folding or ironing algorithm. The following images best describe what I actually mean by key points

For example a T-shirt;,

For example for a T-shirt, the above image shows the desired key points. enter image description here

For example for a trouser, the above image shows the desired key points.

My first thoughts:
My first plans were to use neural networks detect various key points after recognising if the cloth is a t-shirt, trouser etc. But I believe, the problem with this approach is that collecting a lot of data sets to train the neural network to detect these key points is a massive task. Furthermore, I am not even sure if neural networks can produce the results I am looking for because generally, neural networks work well differentiating well defined varying classes of items like for example cats and dogs.

So my question is, is there a better way to achieve what I am trying to do? Any help is appreciated thank you.

UPDATE
Ideally, what I want to do is identify or recognise each of these key points precisely; meaning, for example, the system should know where the collar region for a t-shirt and track its location at all times. I need to know what each key points are (is it a zip area or shirt's shoulder or a collar region) in order to execute a certain folding algorithm. So in this case, I believe, convexity defects do not work.

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Neural networks are not the solution to every problem in robotics.

Have a look at the convex hull of the object and convexity defects.

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  • $\begingroup$ I have changed my question requirements. Please read. What you suggested is not really useful to me. Thank you. $\endgroup$ – Vino Jul 8 '17 at 2:48

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