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Target segmentation in non-homogeneous infrared images using a PCA plane and an adaptive Gaussian kernel

Title
Target segmentation in non-homogeneous infrared images using a PCA plane and an adaptive Gaussian kernel
Author
문영식
Keywords
infrared image; segmentation; total least square; principal component analysis; sum of the square error; gaussian weight; error minimization
Issue Date
2015-06
Publisher
KSII-KOR SOC INTERNET INFORMATION
Citation
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, v. 9, No. 6, Page. 2302-2316
Abstract
We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.
URI
http://www.itiis.org/digital-library/manuscript/1049https://repository.hanyang.ac.kr/handle/20.500.11754/100858
ISSN
1976-7277
DOI
10.3837/tiis.2015.06.019
Appears in Collections:
ETC[S] > 연구정보
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