The Representation of Visual Feature Variables in Connectionist Networks (Computer and Information Science)

联结网络中视觉特征变量的表示（计算机与信息科学）

• 批准号: 8705553
• 负责人: Deborah Walters
• 金额: $ 16.54万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-07-01 至 1989-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8705553&HistoricalAwards=false
• 关键词: Representation; Visual; Feature; Variables; Connectionist

This research provides a general theoretical framework for the description and analysis of representations for variables in connectionist networks. The space in which all representations lie is defined, and existing representation schemes are described in terms of this space. In addition, the space is used to suggest new representations. A mathematical analysis of the representations is used to compare different representations in terms of various properties. This analysis has lead to theories about visual processing, and has been used to determine which types of information are best encoded by a particular representation, and is thus useful for choosing an optimal encoding for a given computational task. This research shows that in the early stages of visual processing the choice of representation for a visual feature variable depends not only on the type of information about the variable that is desired, but also on the measurement process used to extract the feature from an image. A tuning-curve technique has been developed for analyzing the measurment process, and thus determining the constraints placed on the choice of representation that are a function of the measurement process. The focus of this project is to study a special representation techniques, call Rho-space representation. It has been developed along with simple parallel connectionist computations for edge analysis. Rho-space has many advantages over other edge representations including: no thresholding of local edge elements is required; a natural representation of connectivity is created which agrees with human perception; both coarse and fine representation of orientation information is possible; and, illusory contours, of the type produced by the human visual system occur.

这项研究为联结网络中变量的表示的描述和分析提供了一个通用的理论框架。 定义了所有表示所在的空间，并根据该空间来描述现有的表示方案。 此外，空间还被用来提出新的表达方式。 对表示的数学分析用于比较不同表示的各种属性。 这种分析产生了有关视觉处理的理论，并已用于确定哪些类型的信息最适合由特定表示进行编码，因此对于为给定计算任务选择最佳编码非常有用。 这项研究表明，在视觉处理的早期阶段，视觉特征变量表示的选择不仅取决于所需变量的信息类型，还取决于用于从图像中提取特征的测量过程。 调谐曲线技术已被开发用于分析测量过程，从而确定对作为测量过程的函数的表示选择的约束。 该项目的重点是研究一种特殊的表示技术，称为 Rho 空间表示。 它是与用于边缘分析的简单并行联结计算一起开发的。 与其他边缘表示相比，Rho 空间具有许多优点，包括：不需要对局部边缘元素进行阈值处理；创建了与人类感知一致的连接的自然表示；方向信息的粗略和精细表示都是可能的；并且，出现由人类视觉系统产生的类型的虚幻轮廓。