RI: Medium: Collaborative Research: Object and Activity Recognition as the Maximum Weight Subgraph Problem with Mutual Exclusion Constraints
RI:中:协作研究:对象和活动识别作为具有互斥约束的最大权重子图问题
基本信息
- 批准号:1302164
- 负责人:
- 金额:$ 49.38万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-09-01 至 2019-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
It has been widely acknowledged that recognizing objects in images, and human activities in video - the basic problems in computer vision - can be significantly improved by accounting for object (activity) parts, context, and their spatiotemporal relationships. This is because these constraints facilitate resolving ambiguous hypotheses in the face of uncertainty. Since parts and contexts can be efficiently modeled by graphical models (e.g., Conditional Random Field), object and activity recognition are often formulated as probabilistic inference of graphical models. The project develops a new theoretical framework of graphical models that explicitly encodes high-order, spatiotemporal, hierarchical, and contextual interactions among objects (activities) as Quadratic Mutual-Exclusion Constraints (QMCs), for the purposes of object and activity recognition in images and video.The key contributions of the project work include: 1) Approaches to view-invariant object and activity recognition; 2) Formulations of learning and inference of graphical models representing objects and human activities, as finding a maximum weight subgraph (MWS) under the QMCs; 3) Polynomial-time algorithms for solving the MWS problem subject to QMCs; and 4) Explicit performance bounds and theoretical guarantees of tightness and convergence of the proposed learning and inference algorithms. The project framework encodes hard constraints from the domain of interest that have never been used in prior work, and uses principled, polynomial-time algorithms for learning and inference. The research of this project advances the state of the art in object and activity recognition, and enables new applications including video surveillance, retrieval from large datasets, and perception of mobile robots.
人们普遍认为,识别图像中的对象和视频中的人类活动-计算机视觉中的基本问题-可以通过考虑对象(活动)部分,上下文及其时空关系来显着改善。这是因为这些约束有助于在不确定性面前解决模糊的假设。由于部件和上下文可以通过图形模型(例如,条件随机场),对象和活动识别往往制定为概率推理的图形模型。该项目开发了一个新的图形模型的理论框架,明确编码对象之间的高阶,时空,层次和上下文交互本项目的主要工作包括:1)视角不变的目标和活动识别方法; 2)表示对象和人类活动的图形模型的学习和推理的公式化,如在QMC下找到最大权重子图(MWS); 3)用于解决服从QMC的MWS问题的多项式时间算法;以及4)所提出的学习和推理算法的紧密性和收敛性的显式性能界限和理论保证。该项目框架对来自感兴趣领域的硬约束进行编码,这些约束在以前的工作中从未使用过,并使用原则性的多项式时间算法进行学习和推理。该项目的研究推进了对象和活动识别的最新技术水平,并实现了新的应用,包括视频监控,从大型数据集检索,以及移动的机器人的感知。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Longin Jan Latecki其他文献
Graph and Subspace Learning for Domain Adaptation
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Longin Jan Latecki - 通讯作者:
Longin Jan Latecki
UITI2007-University Information Technical Interchange Review Meeting
UITI2007-高校信息技术交流评审会
- DOI:
- 发表时间:
2008 - 期刊:
- 影响因子:0
- 作者:
Franques;Roger Williams;S. Schubert;J. Bloch;A. Ostrogorsky;A. Burger;Zhong He;J. Derby;Kelvin G. Lynn;J. D. Pruneda;D. McGregor;P. Lucas;K. Richardson;S. Hauck;K. Webb;M. Richardson;S. Sharpe;L. Carin;G. Wolberg;J. Gunther;T. Moon;Longin Jan Latecki;S. Balkır;I. Paschalidis;A. Garrett;G. Tepper;Z. Pizlo;G. Williams;J. Ryan;A. Maccabe;Jun Qi;M. Hoffman - 通讯作者:
M. Hoffman
Polygonal approximation of laser range data based on perceptual grouping and EM
基于感知分组和EM的激光测距数据的多边形逼近
- DOI:
- 发表时间:
2006 - 期刊:
- 影响因子:0
- 作者:
Longin Jan Latecki;Rolf Lakämper - 通讯作者:
Rolf Lakämper
Semi-Supervised Learning on an Augmented Graph with Class Labels
带有类标签的增强图的半监督学习
- DOI:
10.3233/978-1-61499-672-9-1571 - 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Nan Li;Longin Jan Latecki - 通讯作者:
Longin Jan Latecki
Using spatiotemporal blocks to reduce the uncertainty in detecting and tracking moving objects in video
使用时空块减少检测和跟踪视频中移动对象的不确定性
- DOI:
10.1504/ijista.2006.009914 - 发表时间:
2006 - 期刊:
- 影响因子:0
- 作者:
Longin Jan Latecki;V. Megalooikonomou;Roland Miezianko;D. Pokrajac - 通讯作者:
D. Pokrajac
Longin Jan Latecki的其他文献
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{{ truncateString('Longin Jan Latecki', 18)}}的其他基金
RI:Small: Learning shape features with deep neural networks
RI:Small:使用深度神经网络学习形状特征
- 批准号:
1814745 - 财政年份:2018
- 资助金额:
$ 49.38万 - 项目类别:
Standard Grant
EAGER: Solving Markov Random Fields with Mutual Exclusion Constraints
EAGER:求解具有互斥约束的马尔可夫随机场
- 批准号:
1257024 - 财政年份:2012
- 资助金额:
$ 49.38万 - 项目类别:
Standard Grant
CDI-Type II: Collaborative Research: Perception of Scene Layout by Machines and Visually Impaired Users
CDI-Type II:协作研究:机器和视障用户对场景布局的感知
- 批准号:
1027897 - 财政年份:2010
- 资助金额:
$ 49.38万 - 项目类别:
Standard Grant
Collaborative Research: Recovery of 3D Shapes from Single Views
合作研究:从单一视图恢复 3D 形状
- 批准号:
0924164 - 财政年份:2009
- 资助金额:
$ 49.38万 - 项目类别:
Continuing Grant
Collaborative Research: Simultaneous Contour Grouping and Medial Axis Estimation
协作研究:同时轮廓分组和中轴估计
- 批准号:
0812118 - 财政年份:2008
- 资助金额:
$ 49.38万 - 项目类别:
Standard Grant
Collaborative Research: From Edge Pixels to Recognition of Parts of Object Contours
协作研究:从边缘像素到物体轮廓部分的识别
- 批准号:
0534929 - 财政年份:2005
- 资助金额:
$ 49.38万 - 项目类别:
Continuing Grant
US-Germany Cooperative Research: Robot Localization and Robot Mapping Based on Shape Matching
美德合作研究:基于形状匹配的机器人定位与机器人建图
- 批准号:
0331786 - 财政年份:2003
- 资助金额:
$ 49.38万 - 项目类别:
Standard Grant
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