Application of sequential processing of computer vision methods for solving the problem of detecting the edges of a honeycomb block

TL;DR

This paper explores sequential computer vision techniques, including the Hough transform and edge detection methods, to accurately identify honeycomb edges for improved cutting precision.

Contribution

It introduces an optimized image processing sequence combining various transformations to enhance honeycomb edge detection accuracy.

Findings

Optimal image processing sequence identified for maximum face detection

Enhanced accuracy in honeycomb edge detection for cutting applications

Improved shape quality through better face coordinate determination

Abstract

The article describes the application of the Hough transform to a honeycomb block image. The problem of cutting a mold from a honeycomb block is described. A number of image transformations are considered to increase the efficiency of the Hough algorithm. A method for obtaining a binary image using a simple threshold, a method for obtaining a binary image using Otsu binarization, and the Canny Edge Detection algorithm are considered. The method of binary skeleton (skeletonization) is considered, in which the skeleton is obtained using 2 main morphological operations: Dilation and Erosion. As a result of a number of experiments, the optimal sequence of processing the original image was revealed, which allows obtaining the coordinates of the maximum number of faces. This result allows one to choose the optimal places for cutting a honeycomb block, which will improve the quality of the resulting shapes.