ADAPTIVE AND INTELLIGENT SYSTEMS: Models of Photometric, Geometric and Dynamic Characteristics of Video Imagery for Segmentation, Classification and Synthesis, Including Layers

智力优势：本研究的目的是开发视频图像的随机动态模型，用于视频中时空事件的综合和分类。该方法基于利用动力系统理论、统计信号处理、微分几何和功能分析等工具，以推断视频片段的时空统计数据，并学习（识别）代表其“签名”的动态模型。这允许生成视频片段的新部分，或者在以前未见过的视频中识别它。研究人员将为这些模型开发识别算法、分割方案，将它们的时空域划分为统计上连贯的区域，并赋予它们一个度量结构，以实现分类和识别。更广泛的影响：所开发的模型将允许生成视频的合成部分，并操纵其时空统计数据，这与压缩和传输以及后期制作编辑和互动游戏的开发有关。此外，这些模型支持分类任务，包括检测视频中感兴趣的事件和分割成时空段。这对于安全、监视、视频编码和环境监测（远程探测火灾、烟雾、蒸汽）中的基于视频的识别非常重要。研究的一类特殊时空过程包括人体运动。调查人员将开发分析和计算工具，以便从视频数据中检测和识别个人及其步态。训练学生使用如此多样化的分析和计算工具是一项挑战，但这必须在现代工程学术环境中解决。

Intellectual Merit:The objective of this research is to develop stochastic dynamical models of video imagery for the purpose of synthesis and classification of spatio-temporal events in video.The approach is based on exploiting tools from dynamical systems theory, statistical signal processing, differential geometry and functional analysis in order to infer the spatio-temporal statistics of a video segment and learn (identify) a dynamical model that represents its "signature". This allows generating novel portions of a video segment, or recognizing it in previously unseen video. The investigators will develop identification algorithms for such models, segmentation schemes to partition their spatio-temporal domain into statistically coherent regions, and endow them with a metric structure to enable classification and recognition.Broader Impacts:The models developed will allow the generation of synthetic portions of video, and the manipulation of their spatio-temporal statistics, which is relevant for compression and transmission, and post-production editing and development of interactive games. Furthermore, these models support classification tasks, including detection of events of interest in video and segmentation into spatio-temporal segments. This is important for video-based recognition in security, surveillance, video coding, and environmental monitoring (remote detection of fire, smoke, steam). A particular class of spatio-temporal processes studied includes human motion. The investigators will develop analytical and computational tools to enable the detection and recognition of individuals and their gait from video data. Training students in such a diverse set of analytical and computational tools is a challenge, but one that must be tackled in a modern engineering academic environment.