[논문]Multi-Level Segmentation of Infrared Images with Region of Interest Extraction

Yeom, Seokwon

doi:10.5391/ijfis.2016.16.4.246

Multi-Level Segmentation of Infrared Images with Region of Interest Extraction 원문보기

International journal of fuzzy logic and intelligent systems : IJFIS, v.16 no.4, 2016년, pp.246 - 253

Yeom, Seokwon (School of Computer and Communication Engineering, Daegu University)

Abstract ▼ AI-Helper

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. 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 initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

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문제 정의

In this paper, the multi-level segmentation is addressed for the ROI and the object of interest (OOI) extraction of the IR scene. The histogram of the image is assumed to follow the Gaussian mixture distribution.

이론/모형

They are shown to be corrupted by noise and clutter. The proposed method shows a lower average error probability than that of other methods such as Gonzalez, Otsu, k-means, and EM methods.

성능/효과

It is shown that the average probability error of the multi-level method is more than 50% lower on average than that of other methods.

참고문헌 (20)

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상세보기
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Multi-Level Segmentation of Infrared Images with Region of Interest Extraction 원문보기

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Multi-Level Segmentation of Infrared Images with Region of Interest Extraction 원문보기

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