title: ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÐºÑÐ¸ÑÑÐ°Ð»Ð»Ð¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Machine vision algorithms for automatic crystal classification
Creators: name: ÐÐ»ÑÐ¸Ð½, ÐÐ¼Ð¸ÑÑÐ¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð´ÐµÑÐµÐºÑÐ¾Ñ ÐºÑÐ½Ð½Ð¸
Ð·Ð°Ð´Ð°ÑÐ° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð°Ð»Ð¼Ð°Ð·Ð¾Ð²
ÐºÐ¾Ð»Ð»ÐµÐºÑÐ¸Ð² Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
Ð¼ÐµÑÐ¾Ð´ Ð³Ð»Ð°Ð²Ð½ÑÑ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½Ñ
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¿Ð¾Ð¸ÑÐºÐ° ÑÐ°Ð±Ð»Ð¾Ð½Ð° ÐºÑÐ¸ÑÑÐ°Ð»Ð»Ð°
ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
machine vision
canney detector
classification of diamonds
collective of neural networks
method of principal components
crystal tub search algorithm
testing

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð°Ð»Ð¼Ð°Ð·Ð° Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÑÑ ÑÐ°Ð±Ð»Ð¾Ð½ ÐºÑÐ¸ÑÑÐ°Ð»Ð»Ð° Ð² Ð²Ð¸Ð´Ðµ Ð±Ð¸Ð½Ð°ÑÐ½Ð¾Ð³Ð¾ ÑÐµÑÐ½Ð¾-Ð±ÐµÐ»Ð¾Ð³Ð¾ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ. ÐÐ»Ñ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð¸Ñ ÑÐ°Ð±Ð»Ð¾Ð½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼. ÐÐ¾ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð¼Ñ ÑÐ°Ð±Ð»Ð¾Ð½Ñ Ð½Ð°Ð¹Ð´ÐµÐ½Ñ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ðµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. ÐÐ¸ÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð°, ÐºÐ¾ÑÐ¾ÑÐ°Ñ ÑÐ¾-Ð´ÐµÑÐ¶Ð¸Ñ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ Ð¾ ÑÑÐºÐ¾ÑÑÐ¸ Ð¸ Ð¿ÑÐ¾Ð·ÑÐ°ÑÐ½Ð¾ÑÑÐ¸. ÐÐµÐºÑÐ¾Ñ ÑÐ¾ÑÐ¼Ñ, Ð¾Ð¿Ð¸ÑÑÐ²Ð°ÑÑÐ¸Ð¹ ÑÐ¸Ð³ÑÑÑ ÑÐ°Ð±Ð»Ð¾Ð½Ð°.Ð¡ ÑÐµÐ»ÑÑ ÑÐ½Ð¸Ð·Ð¸ÑÑ ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÑ Ð½Ð°Ð¹Ð´ÐµÐ½Ð½ÑÑ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½ Ð¾ÑÐ±Ð¾Ñ Ð³Ð»Ð°Ð²Ð½ÑÑ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½Ñ. ÐÐ»Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÐºÑÐ¸ÑÑÐ°Ð»Ð»Ð¾Ð² ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ñ Ð´Ð²Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÐºÐ¾Ð»Ð»ÐµÐºÑÐ¸Ð²Ð¾Ð² Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð°ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¿Ð°ÐºÐµÑÐ° Ð¿ÑÐ¸ÐºÐ»Ð°Ð´Ð½ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼ Â«PythonÂ». Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ð¾ÑÐµÐ½ÐºÐ° Ð¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ. ÐÑÐ¾ÑÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð±ÑÐ»Ð° Ð±Ð¾Ð»ÐµÐµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾Ð¹ Ð¸ Ð´Ð¾ÑÑÐ¸Ð³Ð»Ð° ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð² 0.95%.
description: In this paper, to determine the signs of diamond, it is proposed to use a crystal template in the form of a binary black and white image. To obtain a template, an automated algorithm is developed. Based on the resulting template, the following classification features were found. A histogram that contains information about brightness and transparency. Shape vector describing the shape of the template. To reduce the dimension of the found features, the main components were selected. For the task of automatic classification of crystals, two models of neural network ensembles have been developed. The development was carried out using the Python application package. As a result of the work, the app was evaluated and tested. The second model was more efficient and achieved a classification accuracy of 0.95%.