Vision‐based facial oil blotting paper counting

Abstract

In the production of facial oil blotting papers, a certain number of papers must be counted before packaging. Currently, the papers are counted by hand, and this is hard work. Also, there are risks of adhesion of dust and wrinkles. In order to solve these problems, we propose a vision-based approach. After the papers, which are arranged by shifting, are captured, the proposed image processing steps detect the boundaries of the papers. By counting the detected boundaries, the number of papers can be counted. Since the parameters for the proposed image processing are optimized by genetic algorithm, prior setting by a user is not necessary. For the experiments, an image dataset was constructed with six types of facial oil blotting papers. The proposed method achieved higher F-measure than in other related works.

An Automatic Counting Algorithm for Topographic Maps Based on Videos for Map Management

“…The whole image is obtained by photographing the paper horizontally, arranged at a certain distance; then, the edge features of the papers in the image are processed to achieve counting. Junya Sato et al [14] extracted the edges of aligned paper images by finding the optimal threshold of the Canny edge detector with the genetic algorithm and inhibited the false detection to count six different types of facial oil blotting papers. Chen et al [15] established template Gabor filters with different frequencies based on the edge features for pretreatment and then counted the number of papers using the grayscale value difference algorithm according to the grayscale pixel curves.…”

A Non-Contact Method for Real-Time Stacked Sheets Counting with X-ray Absorption Spectra and Long Short-Term Memory Network

“…They can be divided into two categories: based on machine vision technology and using radioactive rays. In the method of machine vision, Sato, J. et al detect the boundaries of stacked facial oil blotting papers for counting, 5) but this method can be confused with the paper boundary and crease. Han, X. et al propose a stacked paper counting approach based on image texture.…”