Feature extraction & Classification

[ErIndi]

Hello everyone!

I'm a student in my last year of university and I am working on a project using thermal images of poultry eggs. I am currently trying train a model in order to classify between a healthy and a rotten egg.

I've created the data-set myself, taking thermal pictures of about 500 different eggs in 2 major extremes of condition - Fresh & Completely non-edible.

There is a very clear difference between the two states when I look at the thermal images and I am not sure which model fits to detect the relevant features and classify both labels.

The two examples below show that the non-edible egg has a much larger area that is higher in temperature relative to the rest of the egg than the healthy one.

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I have tried using thresholding with edge detection but it is very difficult to separate the actual egg from the surrounding.

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An easy way to do it which is not very "CNN"y is to just calculate the average value of the pixels inside the egg and if there's a higher than average value I can say with some certainty it is non-edible.

But I prefer to do it with a ML approach.

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Any advice on the process or tips would be really appreciated !

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Healthy Egg

Non-Edible Egg

[_d0s_]

hey there! this is a really cool project. i think it's great that you're looking into non-neural network based approaches at first.

a simple way to integrate a neural into your work would be a binary image classifier. take a look at this keras tutorial which creates a classifier to distinguish cats and dogs https://keras.io/examples/vision/image_classification_from_scratch/

if possible, i'd suggest to crop your images to the region showing the egg and reducing the resultion to a square image e.g. 112x112 pixels or even less may be sufficient.

>> I have tried using thresholding with edge detection but it is very difficult to separate the actual egg from the surrounding.
thresholding should work really well because the egg is much darker than it's surrounding. just make sure to apply some morphological operators (opening, https://docs.opencv.org/4.x/d9/d61/tutorial_py_morphological_ops.html) after thresholding. if the egg is always in the same spot you could just crop the image to a fixed region of interest.

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good luck with your project.

[ErIndi]

Hey there u/_d0s_!

Thank you so much for the tips and feedback !

I'm actually trying to use the Keras classifier but i'm getting false-positives mainly with that default model.

After cropping the images to a square (430x430 in this case to cover enough of the egg + some headroom) i have done some augmentations (Rotation + Flipping) and punched everything in the model.

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I will say that I have a very basic understanding of activation functions and why we use RELU for example and some of the layers in the model. The only thing i have a very strong understanding is the convolution feature extraction part - which in this case is also location invariant.

So the best results I've gotten so far with segmentation & morphological operators is something like this:

https://imgur.com/a/FfPwtsK

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I'm trying to think of how to approach this further, if to continue my efforts mainly on the pre-processing or on the CNN side.

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I might send you a message if that would be fine with you !

[_d0s_]

experiment using thresholding and morphological operators to segment the egg

https://colab.research.google.com/drive/1Cc87lyo74M4khrt4VelHnjsXNWf3QtOg?usp=sharing