Image processing for feature detection and extraction

The present paper aims to conduct an experiment that compares different methods of detecting objects in images. Programs were developed to evaluate the efficiency of SURF, BRISK, MSER, and ORB object detection methods. Four static gray images with sufficiently different histograms were used. The experiment also highlighted the need for image preprocessing to improve feature extraction and detection. Thus, a programmed method for adjusting pixel groups was developed. This method proved useful when one of the listed algorithms failed to detect the object in the original image, but succeeded after adjustment. The effectiveness of detection methods and the evaluation of their performance depend on the application, image preparation, algorithms used, and their implementation. Results of the detection methods were presented numerically (similarities, gradients, distances, etc.) and graphically.