传统艺术图像识别算法集成到儿童艺术作品信息展示平台的稳定性测试

Yuanyuan Lv
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引用次数: 0

摘要

采用激光扫描和红外特征分析技术采集假艺术品图像,并采用超像素融合方法对采集到的信息进行融合,提取图像的边界特征信息。文章详细阐述了教师如何组织和开展艺术区活动的有效策略,通过开展丰富多彩的艺术区活动,使艺术区活动有趣、可玩、好学,培养幼儿的创造性思维能力。分割准确率稳定。目标分割技术为进一步的轮廓提取提供准确的定位,去除无用的图像背景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stability Test of Traditional Art Image Recognition Algorithm Integrated into Children’s Art Works Information Display Platform
Laser scanning and infrared feature analysis technology are used to collect images of fake art works, and superpixel fusion method is used to fuse the collected information to extract the boundary feature information of images. The article describes in detail how teachers organize and carry out activities in art areas Effective strategies, through the development of colorful art area activities, make art area activities interesting, playable, studious, and develop children's creative thinking ability. The segmentation accuracy rate is stable. Object segmentation technology provides accurate positioning for further contour extraction and removes useless image background.
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