Name
Title | ||
|---|---|---|
Gaussian mixture modeling approach for stationary human identification in through-the-wall radar imagery |
Abstract | ||
|---|---|---|
We propose a Gaussian mixture model (GMM)-based approach to discriminate stationary humans from their ghosts and clutter in through-the-wall radar images. More specifically, we use a mixture of Gaussian distributions to model the image intensity histograms corresponding to target and ghost/clutter regions. The mixture parameters, namely the means, variances, and weights of the component distributions, are used as features and a K-nearest neighbor classifier is employed. The performance of the proposed method is evaluated using real-data measurements of multiple humans standing or sitting at different locations in a small room. Experimental results show that the nature of the targets and ghosts/clutter in the image allows successful application of the GMM feature-based classifier to distinguish between target and ghost/clutter regions. (C) 2015 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/1.JEI.24.1.013028 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
target classification,clutter,Gaussian mixture model,through-the-wall radar | Radar,Computer vision,Histogram,Radar imaging,Pattern recognition,Computer science,Clutter,Gaussian,Artificial intelligence,Constant false alarm rate,Classifier (linguistics),Mixture model | Journal |
Volume | Issue | ISSN |
24 | 1 | 1017-9909 |
Citations | PageRank | References |
2 | 0.48 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vamsi Kilaru | 1 | 2 | 0.48 |
| Moeness Amin | 2 | 2909 | 287.79 |
| Fauzia Ahmad | 3 | 651 | 64.26 |
| Pascale Sévigny | 4 | 2 | 0.48 |
| David DiFilippo | 5 | 3 | 1.10 |