用计算机视觉比对素描与原图，自动评估绘画水平
Evaluation of Image Matching for Art Skills Assessment
- 通过SIFT关键点匹配和孪生网络比较手绘图与模板图
- SIFT方法在检测绘画技能上表现更优，准确识别差异
- 适合艺术教育评估、自动化作品打分场景
尽管有些人天生具备绘画天赋，但掌握绘画技巧仍需长期训练与实践。准确评估绘画能力需要全面的综合评价体系。本文提出一种通过比对手绘图像与原始模板来衡量绘画技能的方法。现有技术通常流程复杂，而计算机视觉的发展使得机器能够以类人水平完成图像比对，从而简化传统繁琐耗时的评估过程。利用计算机视觉技术，图像相似性评估旨在识别目标图像与参考图像之间的相似程度。我们实现了并分析了SIFT特征与孪生网络两种方法来测量图像相似性。实验结果表明，该方法可有效评估绘画技能水平。通过特征分析发现，基于SIFT的关键点匹配在检测绘画技能方面更为高效。
原文摘要 · Abstract (English)
While some individuals possess a natural talent for drawing, mastering this skill requires dedicated training and practice. Determining one's skill in the art of drawing requires proper comprehensive assessment. In this paper, we propose a method to measure drawing skill by by matching the hand-drawn image with the original template. Existing techniques often involve complex processes. However, advancements in computer vision allow us to train computers to perform these comparisons at a human-like level, thereby resolving the tedious and overwhelming traditional process. Using computer vision applications, determining image similarity involves identifying the level of similarities in an image with a reference image. We have implemented and analyzed the SIFT feature and Siamese network to measure image similarity. Our results indicate that it is feasible to assess art skill levels. Through feature analysis, we found that SIFT-based key point matching provides a more effective means of detecting drawing skills.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。