Results 251 to 260 of about 209,460 (284)

An Improved Multi-sensor Image Fusion Algorithm

2014 International Conference on Identification, Information and Knowledge in the Internet of Things, 2014
Acquired digital images are often corrupted by the lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation ...
Zhuozheng Wang, John R. Deller Jr.
openaire   +1 more source

Possibilistic multi-sensor fusion for humanitarian demining

2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
We propose a method for combining humanitarian mine detection sensors based on possibility theory. Firstly, different features are extracted from the sensor data. Possibility distributions are then derived from the features based on prior information. After that, the combination of possibility degrees is performed in two steps, on separate sensor level
Nada Milisavljevic, Isabelle Bloch
openaire   +1 more source

Multi-Sensor Measurement and Data Fusion

IEEE Instrumentation & Measurement Magazine, 2022
Zheng Liu 0002, George Xiao 0001, Huan Liu 0002, Hanbing Wei   +3 more
openaire   +1 more source

Effective fusion of distorted multi-sensor data

Proceedings of the 2003 IEEE International Symposium on Intelligent Control ISIC-03, 2003
A framework for the detection of bandlimited signals by intelligently fusing the multi-nonlinear sensor data is developed. Though most sensors used are assumed to be linear, none of them individually or in series give the truly linear relationship and errors are inevitable as a result of the assumption of linearity.
Sugathevan Suranthiran, Suhada Jayasuriya   +1 more
openaire   +1 more source

A Review of Multi-sensor Data Fusion for Traffic

2018
Data fusion is the process of integrating multi-sources data to obtain more consistent, accurate, and beneficial information than that provided by any single data source. This paper sum the state of the data fusion field and describes the most relevant studies. We first explain data fusion and multiple sensors Then, data fusion in traffic are reviewed.
Xue Zhao, Dongbo Zhang 0001
openaire   +1 more source

A variational approach to multi-sensor fusion of images

Applied Intelligence, 1995
Past research into multi-modality sensor data fusion has given rise to approaches that are generally heuristic and ad hoc. In this paper we utilize the calculus of variations as the underlying framework for fusing registered images of different modalities when models relating these modalities are available.
Homer H. Pien, John M. Gauch
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A STPHD-Based Multi-sensor Fusion Method

2012
In order to extract the peaks of PHD, a novel method STPHD has been proposed recently. This method can provide more accurate target state estimates than the general clustering algorithm such as k-means clustering. This paper presents a version of STPHD for multi-sensor scene and makes two contributions.
Zhenwei Lu, Lingling Zhao, Xiaohong Su, Peijun Ma   +3 more
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Multi-sensor Information Fusion by Query Refinement

2002
To support the retrieval and fusion of multimedia information from multiple real-time sources and databases, a novel approach for sensor-based query processing is described. The sensor dependency tree is used to facilitate query optimization. Through query refinement one or more sensor may provide feedback information to the other sensors. The approach
CHANG S. K, COSTAGLIOLA, Gennaro, JUNGERT E.   +2 more
openaire   +2 more sources

Multi-Sensor Fusion for Navigation of Ground Vehicles

2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR), 2022
Jurek Z. Sasiadek, Arsalan Ahmed
openaire   +1 more source

Survey of Multi-sensor Image Fusion

2014
The paper presents multi-sensor image fusion and its relevant framework and technical characteristics. The image fusion is divided into three level fusions: pixel level, feature level and decision level. It mainly discusses the image fusion algorithm at all levels of fusion, and then makes the summary and comparison of these algorithms.
Dingbing Wu, Aolei Yang, Lingling Zhu, Chi Zhang   +3 more
openaire   +1 more source