I have seen very educational applications of pattern recognition on complete images. I was wondering if there are methods that could handle cases with the following specifications:
- instead of full images we have a set of sub-images which are made by tearing the original image apart
- sub-images have arbitrary sizes (within a given range)
- some parts of the original image might be missing after tearing it apart
I am a newbie in this field but are there (could there possibly be) convolutional networks that can handle such cases? From what I understand they work by breaking the image down.
The answer for both these cases are helpful: - Full size images for training and applying the classifier to torn-apart images - Torn-apart images for both training and testing
The answer for the both type of tasks to label each image (set of pieces) as a whole or to label each pixel in the image (~object recognition) would help me.
If you are not interested in bioinformatics don't mind this paragraph, but if you are interested, this question is the image-processing translation of my original question which is about phenotype-genotype mapping where I look at not fully assembled DNA sequences: we have contigs of reads rather than full sequences.
If you know some references that investigate this problem please advise me. Thank you very much for your input!