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

自适应和智能系统：用于分割、分类和合成（包括图层）的视频图像的光度、几何和动态特征模型

• 批准号: 0622245
• 负责人: Stefano Soatto
• 金额: $ 24万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0622245&HistoricalAwards=false
• 关键词: ADAPTIVE; INTELLIGENT; SYSTEMS; Models; Photometric

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.

智力优势：本研究的目标是开发视频图像的随机动态模型，用于视频中时空事件的合成和分类，该方法基于利用动态系统理论，统计信号处理，微分几何和泛函分析的工具，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，或者在先前未看到的视频中识别它。研究人员将为这些模型开发识别算法，分割方案，将其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，使分类和recognition.Broader影响：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，这是相关的压缩和传输，以及后期制作编辑和开发的互动游戏。此外，这些模型支持分类任务，包括检测视频中的感兴趣事件和分割成时空片段。这对于安全、监控、视频编码和环境监控（火灾、烟雾、蒸汽的远程检测）中的视频识别非常重要。研究的一类特殊的时空过程包括人体运动。研究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。培训学生在这样一个多样化的分析和计算工具是一个挑战，但必须在现代工程学术环境中解决。