Abstract
A new method for endoscopic image analysis in real time is presented. This method allows improving accuracy and helps avoiding subjectivity when performing endoscopic image classification. The method is based on use of Scale– invariant feature transform detector and computation of gastric mucosa pit–pattern skeletons. Subsequent use of the “Bag of visual words” method (“Bag of features”) and K–means method for key points clustering allows image classification with more than 85% accuracy.
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