STTR Phase II: IntelliStitch AI: Intelligent Computerized Embroidery Design Automation for the Textile Industry

STTR 第二阶段：IntelliStitch AI：纺织行业智能电脑刺绣设计自动化

• 批准号: 0239356
• 负责人: David Goldman
• 金额: $ 49.93万
• 依托单位: Soft Sight, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-01-15 至 2005-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0239356&HistoricalAwards=false
• 关键词: STTR; Phase; II; IntelliStitch; AI

This Small Business Technology Transfer (STTR) Phase II project will develop an automated means for embroidery design specification for use in the textile industry. This technology will provide simplified mechanisms for converting scanned artwork into high quality embroidery design data. This data will then be utilized by commercial sewing equipment to produce embroidered artwork that has become quite common on all types of garments and woven goods. Embroidered artwork is often quite expensive to produce and in many cases may substantially exceed the costs of the actual garments being imprinted. These costs arise from a variety of factors including an embroidered design's size and complexity. Well-designed embroidered artwork permits efficient production with high yields (i.e. minimal defects produced). Automating design creation provides additional benefits by eliminating the time consuming manual process that must otherwise be undertaken by a human expert. The commercial and broader impacts of this technology facilitate lower manufacturing costs while allowing consistent production of high-quality goods. Additionally, this research may have broader applications within other fields such as document processing, image recognition, or other areas where image understanding and interpretation are important.

这个小企业技术转让（STTR）第二阶段项目将开发一种用于纺织行业的刺绣设计规范的自动化方法。该技术将提供简化的机制，用于将扫描的艺术品转换为高质量的刺绣设计数据。这些数据将被商业缝纫设备用于生产刺绣艺术品，这在所有类型的服装和纺织品上都很常见。刺绣艺术品的生产成本通常非常昂贵，在许多情况下，可能会大大超过实际服装的成本。这些成本来自各种因素，包括刺绣设计的尺寸和复杂性。精心设计的刺绣艺术品允许高产量的高效生产（即产生的缺陷最少）。自动化设计创建通过消除必须由人类专家进行的耗时的手动过程提供了额外的好处。这项技术的商业和更广泛的影响有助于降低制造成本，同时允许始终如一地生产高质量的产品。此外，这项研究可能在其他领域有更广泛的应用，如文件处理，图像识别，或其他领域的图像理解和解释是重要的。