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Basically you could do this very simple.

• Collect a set of images of what your texture looks like
• Collect a set of negatives
• Train a cascade classifier using the train_cascade approach
• Classify each window as texture or not

However, there must be reasons why people haven't used cascade classifiers for texture recognition. I guess the downside is the amount of time it takes to actually train and create the classifier. Template matching performs more than well enough on textures and a lot of research acknowledges that fact.