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Proceedings Paper

The research of image segmentation methods for interested area extraction in image matching guidance
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Paper Abstract

The extraction of Region of Interest (ROI) is an important information guarantee in the application of imaging matching guidance, which directly affects the acquisition probability and matching accuracy of the target. Image segmentation is an important method to extract the Region of Interest of the target. Based on image segmentation algorithm, histogram equalization and morphological filtering, this paper proposes an effective image processing method to extract the Region of Interest of the target. (1) A variety of image threshold segmentation methods are applied to the actual processing flow, and their segmentation performance is compared and analyzed. Some image segmentation methods are obtained, which are suitable for target region extraction in template image preparation and target potential region location in matching recognition. (2) Preliminary localization of visible remote sensing images is carried, using color information, to obtain local regions, then enhance the image using histogram equalization method, finally morphological filtering is used to remove the edge noise. (3) The Otsu method and Kittler minimum error method are processed in parallel, then the segmentation results are fused, and the evaluation indexes such as area constraint, similarity and contrast are filtered to obtain the target region .Tests have been done with visible image and infrared image in this paper. The result indicates that the effectiveness of the morphological filter is more obvious after histogram equalization for the original image. Besides, the Otsu method and Kittler minimum error method are processed in parallel, then the segmentation results are fused to get a more precise Region of Interest, thus ensuring the accuracy and timeliness of imaging matching guidance.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290R (14 February 2020); doi: 10.1117/12.2539129
Show Author Affiliations
Yaozong Zhang, Hubei Key Lab. of Optical Information and Pattern Recognition (China)
Hubei Engineering Research Ctr. of Video Image and High Definition Projection (China)
Wuhan Institute of Technology (China)
Pan Chen, Hubei Key Lab. of Optical Information and Pattern Recognition (China)
Hubei Engineering Research Ctr. of Video Image and High Definition Projection (China)
Wuhan Institute of Technology (China)
Hanyu Hong, Hubei Key Lab. of Optical Information and Pattern Recognition (China)
Hubei Engineering Research Ctr. of Video Image and High Definition Projection (China)
Wuhan Institute of Technology (China)
Zhenghua Huang, Hubei Key Lab. of Optical Information and Pattern Recognition (China)
Hubei Engineering Research Ctr. of Video Image and High Definition Projection (China)
Wuhan Institute of Technology (China)
Chen Zhou, Hubei Key Lab. of Optical Information and Pattern Recognition (China)
Hubei Engineering Research Ctr. of Video Image and High Definition Projection (China)
Wuhan Institute of Technology (China)


Published in SPIE Proceedings Vol. 11429:
MIPPR 2019: Automatic Target Recognition and Navigation
Jianguo Liu; Hanyu Hong; Xia Hua, Editor(s)

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