• Title, Summary, Keyword: Infrared target segmentation

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Infrared and Visible Image Fusion Based on NSCT and Deep Learning

  • Feng, Xin
    • Journal of Information Processing Systems
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    • v.14 no.6
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    • pp.1405-1419
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    • 2018
  • An image fusion method is proposed on the basis of depth model segmentation to overcome the shortcomings of noise interference and artifacts caused by infrared and visible image fusion. Firstly, the deep Boltzmann machine is used to perform the priori learning of infrared and visible target and background contour, and the depth segmentation model of the contour is constructed. The Split Bregman iterative algorithm is employed to gain the optimal energy segmentation of infrared and visible image contours. Then, the nonsubsampled contourlet transform (NSCT) transform is taken to decompose the source image, and the corresponding rules are used to integrate the coefficients in the light of the segmented background contour. Finally, the NSCT inverse transform is used to reconstruct the fused image. The simulation results of MATLAB indicates that the proposed algorithm can obtain the fusion result of both target and background contours effectively, with a high contrast and noise suppression in subjective evaluation as well as great merits in objective quantitative indicators.

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

  • Kim, Yong Min;Park, Ki Tae;Moon, Young Shik
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.6
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    • pp.2302-2316
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    • 2015
  • 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.

Extraction of Infrared Target based on Gaussian Mixture Model

  • Shin, Do Kyung;Moon, Young Shik
    • IEIE Transactions on Smart Processing and Computing
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    • v.2 no.6
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    • pp.332-338
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    • 2013
  • We propose a method for target detection in Infrared images. In order to effectively detect a target region from an image with noises and clutters, spatial information of the target is first considered by analyzing pixel distributions of projections in horizontal and vertical directions. These distributions are represented as Gaussian distributions, and Gaussian Mixture Model is created from these distributions in order to find thresholding points of the target region. Through analyzing the calculated Gaussian Mixture Model, the target region is detected by eliminating various backgrounds such as noises and clutters. This is performed by using a novel thresholding method which can effectively detect the target region. As experimental results, the proposed method has achieved better performance than existing methods.

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Infrared Image Segmentation by Extracting and Merging Region of Interest (관심영역 추출과 통합에 의한 적외선 영상 분할)

  • Yeom, Seokwon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.26 no.6
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    • pp.493-497
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    • 2016
  • Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.

Target extraction in FLIR image using Bi-modality of local characteristic and Chamfer distance (국부적 특성의 Bi-modality와 Chamfer 거리를 이용한 FLIR 영상의 표적 추출)

  • Lee, Hee-Yul;Kim, Se-Yun;Kim, Jong-Hwan;Kwak, Dong-Min;Choi, Byung-Jae;Joo, Young-Bok;Park, Kil-Houm
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.3
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    • pp.304-310
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    • 2009
  • In this paper, target extraction method in FLIR(forward-looking infrared) images based on fuzzy thresholding which used bi-modality and adjacency to determine membership value is proposed. The bi-modality represents how a pixel is classified into a part of target using distribution of pixel values in a local region, and The adjacency is a measure to represent how each pixel is far from the target region. First, membership value is calculated using above two measures, and then fuzzy thresholding is performed to extract the target. To evaluate performance of proposed target extraction method, we compare other segmentation methods using various FLIR tank image. Experimental results show that the proposed algorithm is a good segmentation performance.