Abstract
This paper presents the results of the analysis of existing methods for clustering data obtained during endoscopy of a larynx. A modification of the Viola-Jones method for image recognition using the flexible exit criterion is proposed. The Viola-Jones method explores all areas in the image and decides whether the recognized area belongs to the desired one by passing through a classified cascade. Endoscopic images have a large number of features, such as flare, noise, etc., which degrade the quality of recognition. To improve the quality of recognition, clustering with a flexible exit criterion was proposed, which satisfies the scalability criteria: changing the decision of the solution, instead of moving to another recognition area. It has been established that the proposed modification of the Viola-Jones method shows higher recognition results for endoscopic images.
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