Segmentation and Recognition of Complex Temporal Patterns

复杂时间模式的分割和识别

• 批准号: 9211419
• 负责人: DeLiang Wang
• 金额: $ 6万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-07-01 至 1995-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9211419&HistoricalAwards=false
• 关键词: Segmentation; Recognition; Complex; Temporal; Patterns

Temporal information processing underlies various kinds of intelligent behaviors, including hearing and vision. A neural network framework for segmenting and recognizing complex temporal patterns is proposed. Processing of temporal segmentation is based on the idea that segmentation is expressed by synchronization within each segment and desychronization among different segments. Each segment becomes an input to the recognition network that explicitly encodes neighborhood or topological relations of local features of the input, and recognition is based on the graph matching method. To cope with problems embedded in time, the network to constructed codes time explicitly. Multiple temporal patterns are segregated into different segments that are activated alternately in the time domain. The network is able to recognize complex temporal patterns, and recognition is invariant to distortions of time intervals (time warping) and to changes in the rate of presentation . The network will be tested for both neural plausibility and computational effectiveness. Results of this project will provide new computational principles that might be used by the brain to process temporal segmentation and recognition. Also, they will provide effective methods for solving technical problems indispensable in real time continuous auditor pattern recognition.

时间信息处理是各种智能行为的基础，包括听觉和视觉。提出了一种用于复杂时间模式分割和识别的神经网络框架。时间分割的处理是基于这样一种思想：分割是通过每个段内的同步和不同段之间的去同步来表达的。每个片段都成为识别网络的输入，该网络对输入的局部特征的邻域或拓扑关系进行显式编码，并基于图匹配方法进行识别。为了处理嵌入时间的问题，网络要明确地构造时间编码。多个时间模式被分离成不同的片段，在时域中交替激活。该网络能够识别复杂的时间模式，并且识别对时间间隔的扭曲（时间扭曲）和呈现率的变化是不变的。该网络将进行神经合理性和计算有效性的测试。该项目的结果将提供新的计算原理，可能用于大脑处理时间分割和识别。为解决实时连续审计模式识别中不可缺少的技术问题提供了有效的方法。