Adaptive Man-Machine Interfaces

自适应人机界面

• 批准号: 9800032
• 负责人: Tomaso Poggio
• 金额: $ 45万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-15 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9800032&HistoricalAwards=false
• 关键词: Adaptive; Man; Machine; Interfaces

This work will exploit learning techniques recently developed to work towards computers that learn to detect and recognize people, estimate user's gestures and communicate visually with them via a photorealistic computer-generated human face. In particular, the plan is to use tow main theoretical and algorithmic approaches to learning: Support Vector Machines and Hidden Markov Models. With these tools, two key aspects of a trainable man-machine interface will be developed: An analysis module that can be trained to estimate in real time facial expressions of the user and associated physical parameters and a synthesis module that can be trained to generate image sequences of a real human face synchronized to a text-to-speech system. The significance of the work is three-fold: (1) The project will contribute to the development of a new generation of computer interfaces more user-friendly and human-centered than today's interfaces. Such interfaces will be of direct use in education and as components of prostheses for the disadvantaged; (2) The project will integrate recently developed learning techniques to real time vision and graphics applications; and (3) The project will explore the boundaries of what is possible to achieve using 2D representations of faces rather than the more common, physically-based, 3D-based models.

这项工作将利用最近开发的学习技术，使计算机能够学习检测和识别人、估计用户的手势并通过逼真的计算机生成的人脸与他们进行视觉交流。 特别是，该计划是使用两种主要的理论和算法方法来学习：支持向量机和隐马尔可夫模型。 通过这些工具，可训练的人机界面的两个关键方面将被开发：一个分析模块，可以被训练来估计用户的实时面部表情和相关的物理参数；以及一个合成模块，可以被训练来生成与文本到语音系统同步的真实人脸的图像序列。 这项工作的意义有三方面：（1）该项目将有助于开发新一代计算机界面，比当今的界面更加用户友好和以人为本。 此类接口将直接用于教育，并作为弱势群体的假肢组件； (2) 该项目将把最近开发的学习技术集成到实时视觉和图形应用中； (3) 该项目将探索使用面部 2D 表示而不是更常见的基于物理的 3D 模型所能实现的边界。