IMAGE MATCHING USING DIFFERENT KEYPOINTS TYPES

Abstract

The work is devoted to experiments with various methods of selecting special points on images,
followed by their description with a binary descriptor and comparison by a full search method.
This paper actively uses the method of describing the neighborhood of singular points, based
on the construction of a binary string that characterizes changes in the brightness of pixels in the
described neighborhood. The resulting string is obtained by comparing the brightness of pixels
according to a specific template. Today, the use of special points when working with images allows
you to develop applied methods in various areas of computer vision with increased requirements
for working time and resistance to sudden changes in scenes. The paper presents the results
of experiments with special points of various classes, the classification is given in section 1. During
the experiments, methods implemented in the OpenCV library were used. The paper provides
brief descriptions of the methods used in experiments. Section 1 of the paper offers a classification
of modern types of singular points of images and provides a brief description of popular methods
for detecting the described types of singular points. In section 2, the authors give a General description
of methods for working with special image points. Section 3 describes the experiments
that are being carried out with the comparison of special points of different types described by a
single descriptor, and reveals their results. The experiments performed allow us to identify the
strengths and weaknesses of bundles of different types of singular points when comparing them.