A statistical similarity measure for non-rigid multi-modal image registration

2010 ◽  
Author(s):  
Jiangli Shi ◽  
Yunmei Chen ◽  
Murali Rao ◽  
Jinseop Lee
Keyword(s):  
Image Registration ◽  
Similarity Measure ◽  
Statistical Similarity

scholarly journals A Novel Outlier-Robust Kalman Filtering Framework based on Statistical Similarity Measure

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Yulong Huang ◽  
Yonggang Zhang ◽  
Yuxin Zhao ◽  
Peng Shi ◽  
Jonathon Chambers
Keyword(s):  
Similarity Measure ◽  
Kalman Filtering ◽  
Statistical Similarity

scholarly journals A statistical similarity measure for aggregate crowd dynamics

2012 ◽  
Vol 31 (6) ◽  
pp. 1-11 ◽  
Author(s):  
Stephen J. Guy ◽  
Jur van den Berg ◽  
Wenxi Liu ◽  
Rynson Lau ◽  
Ming C. Lin ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Crowd Dynamics ◽  
Statistical Similarity

scholarly journals Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Mumu Aktar ◽  
Md. Al Mamun ◽  
Md. Ali Hossain
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Similarity Measure ◽  
Sliding Window ◽  
Remote Sensing Images ◽  
Normalized Mutual Information ◽  
Statistical Similarity ◽  
Multitemporal Remote Sensing ◽  
Sparse Samples ◽  
Window Approach

Change detection (CD) of any surface using multitemporal remote sensing images is an important research topic since up-to-date information about earth surface is of great value. Abrupt changes are occurring in different earth surfaces due to natural disasters or man-made activities which cause damage to that place. Therefore, it is necessary to observe the changes for taking necessary steps to recover the subsequent damage. This paper is concerned with this issue and analyzes statistical similarity measure to perform CD using remote sensing images of the same scene taken at two different dates. A variation of normalized mutual information (NMI) as a similarity measure has been developed here using sliding window of different sizes. In sliding window approach, pixels’ local neighborhood plays a significant role in computing the similarity compared to the whole image. Thus the insignificant global characteristics containing noise and sparse samples can be avoided when evaluating the probability density function. Therefore, NMI with different window sizes is proposed here to identify changes using multitemporal data. Experiments have been carried out using two separate multitemporal remote sensing images captured one year apart and one month apart, respectively. Experimental analysis reveals that the proposed technique can detect up to 97.71% of changes which outperforms the traditional approaches.

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