Infrared image segmentation based on region of interest extraction with Gaussian mixture modeling

Seokwon Yeom

doi:10.1117/12.2263673

1 May 2017 Infrared image segmentation based on region of interest extraction with Gaussian mixture modeling

Seokwon Yeom

Proceedings Volume 10202, Automatic Target Recognition XXVII; 102020C (2017) https://doi.org/10.1117/12.2263673
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

Infrared (IR) imaging has the capability to detect thermal characteristics of objects under low-light conditions. This paper addresses IR image segmentation with Gaussian mixture modeling. An IR image is segmented with Expectation Maximization (EM) method assuming the image histogram follows the Gaussian mixture distribution. Multi-level segmentation is applied to extract the region of interest (ROI). Each level of the multi-level segmentation is composed of the k-means clustering, the EM algorithm, and a decision process. The foreground objects are individually segmented from the ROI windows. In the experiments, various methods are applied to the IR image capturing several humans at night.

Citation Download Citation

Seokwon Yeom "Infrared image segmentation based on region of interest extraction with Gaussian mixture modeling", Proc. SPIE 10202, Automatic Target Recognition XXVII, 102020C (1 May 2017); https://doi.org/10.1117/12.2263673

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