Detecting the Most Unusual Part of Two and Three-dimensional Digital Images

TL;DR

This paper presents an algorithm for detecting the most unusual part of 2D and 3D digital images based on a probabilistic approach, useful for large image database analysis without relying on predefined models.

Contribution

Introduces a novel probabilistic algorithm for identifying the most unusual image regions in 2D and 3D images, independent of contrast and without predefined models.

Findings

Effective detection of unusual image parts in 2D and 3D images

No need for predefined models or contrast adjustment

Potential applications in large image database scanning

Abstract

The purpose of this paper is to introduce an algorithm that can detect the most unusual part of a digital image in probabilistic setting. The most unusual part of a given shape is defined as a part of the image that has the maximal distance to all non intersecting shapes with the same form. The method is tested on two and three-dimensional images and has shown very good results without any predefined model. A version of the method independent of the contrast of the image is considered and is found to be useful for finding the most unusual part (and the most similar part) of the image conditioned on given image. The results can be used to scan large image databases, as for example medical databases.