Robust Integration of Thermal and Visual Imagery for OutdoorScene Analysis
用于户外场景分析的热图像和视觉图像的稳健集成
基本信息
- 批准号:9109584
- 负责人:
- 金额:$ 7万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:1991
- 资助国家:美国
- 起止时间:1991-07-01 至 1995-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Past research by this investigator established a new method for analyzing outdoor scenes. Thermal and visual imagery was combined to extract internal object properties that serve as physically meaningful features for region labeling. This new research presents an improved formulation of the energy exchange model used for this purpose. The energy exchange model is formulated as a linear regression model. Statistically robust techniques will be used to extract reliable estimates of internal object properties such as thermal capacitance. These techniques allow reliable estimates in spite of almost 50% of the data being arbitrarily corrupted due to misregistration of images and segmentation errors. Statistically robust techniques have been shown to be beneficial in many important computer vision problems such as pose estimation, straight-line extraction, computation of image structure, and surface fitting. The proposed research will explore statistical robust approaches to another important problem, i.e., sensor fusion. The computational characteristics of the robust algorithms will be explored for the chosen sensor fusion task. The new formulation also allows iterative refinement of surface parameters (such as thermal emissivity) which were assumed known in the previous formulation of the model. The new approach thus improves the accuracy of the internal property value estimates and also provides new information regarding surface parameter values. The method thus provides a greater number of stable features for object classification.
本研究建立了一种分析室外场景的新方法。结合热图像和视觉图像提取内部物体属性,作为区域标记的物理有意义的特征。这项新研究提出了用于此目的的能量交换模型的改进公式。能量交换模型被表述为线性回归模型。统计稳健技术将用于提取物体内部属性(如热电容)的可靠估计。这些技术允许可靠的估计,尽管几乎50%的数据被任意损坏,由于图像配准错误和分割错误。统计鲁棒性技术已被证明在许多重要的计算机视觉问题中是有益的,如姿态估计、直线提取、图像结构计算和表面拟合。提出的研究将探索统计稳健的方法来解决另一个重要问题,即传感器融合。对于所选择的传感器融合任务,将探讨鲁棒算法的计算特性。新公式还允许迭代细化表面参数(如热发射率),这些参数在以前的模型公式中是已知的。因此,新方法提高了内部属性值估计的准确性,并提供了有关表面参数值的新信息。因此,该方法为目标分类提供了更多的稳定特征。
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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N. Nandhakumar其他文献
A vision-based framework for the discovery-driven manipulation of non-rigid objects
一种基于视觉的框架,用于非刚性物体的发现驱动操作
- DOI:
- 发表时间:
1996 - 期刊:
- 影响因子:0
- 作者:
Philip W. Smith;N. Nandhakumar;A. Ramadorai - 通讯作者:
A. Ramadorai
Unified 3D models for multisensor image synthesis
用于多传感器图像合成的统一 3D 模型
- DOI:
10.1109/cadvis.1994.284504 - 发表时间:
1995 - 期刊:
- 影响因子:0
- 作者:
Jonathan D. Michel;N. Nandhakumar - 通讯作者:
N. Nandhakumar
View-invariant regions and mobile robot self-localization
视图不变区域和移动机器人自定位
- DOI:
10.1109/70.538985 - 发表时间:
1996 - 期刊:
- 影响因子:0
- 作者:
K. Simsarian;T. Olson;N. Nandhakumar - 通讯作者:
N. Nandhakumar
Multisensor integration for underwater scene classification
- DOI:
10.1007/bf00872222 - 发表时间:
1995-07-01 - 期刊:
- 影响因子:3.500
- 作者:
N. Nandhakumar;S. Malik - 通讯作者:
S. Malik
Model-based interpretation of stereo imagery of textured surfaces
- DOI:
10.1007/s001380050072 - 发表时间:
1997-12-01 - 期刊:
- 影响因子:2.300
- 作者:
Wenyi Zhao;N. Nandhakumar;Philip W. Smith - 通讯作者:
Philip W. Smith
N. Nandhakumar的其他文献
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{{ truncateString('N. Nandhakumar', 18)}}的其他基金
SGER: Discovery Driven Manipulation of NonRigid Objects: Representation, Sensing and Planning
SGER:发现驱动的非刚性物体操纵:表示、传感和规划
- 批准号:
9616131 - 财政年份:1996
- 资助金额:
$ 7万 - 项目类别:
Standard Grant
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