Convergent Floating Thresholding for Image Extraction

Yamaguchi, Junichi; Kunishi, Kazuhiro

doi:10.1541/ieejeiss.129.1010

Cited by 3 publications

(2 citation statements)

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“…5 and 6, respectively. Figure 5 shows, from left to right, the input image (1), the result of discriminant analysis [1] (3), the result of the convergent floating threshold method [12] (3), the result of dynamic thresholding [11] (4), and the result of proposed method (5). Panels Table 1 gives the number of thresholding errors for every method compared to the reference images.…”

Section: Methodsmentioning

confidence: 99%

“…We now propose an image thresholding method with improved convergent floating threshold setting using extended control values. In the convergent floating threshold method , a tentative threshold is repeatedly updated using the neighboring grayscale distribution, and the local threshold at a target position is chosen by performing several iterations. Compared to other methods, this method produces relatively stable good results even in the case of brightness change or decline of contrast, and is effective in extracting objects from outdoor scene images.…”

Section: Introductionmentioning

confidence: 99%

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Image Thresholding by Improved Convergent Floating Thresholding Method Using Extended Control Value

Ueno

Yamaguchi

2013

Elect Comm in Japan

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SUMMARY In this paper, the authors propose a threshold setting method for binary images. This method consists of two steps which are different threshold calculating algorithms. The first step is an improved model of the Convergent Floating Thresholding Method, with switching between two control values to update a tentative threshold. By this step, thresholding of a boundary area of gray level change is performed. The second step is composed of the discriminant analysis method and is applied to flat areas of gray level. A feature of the proposed method is comparison of the gray level with two final tentative thresholds which are obtained using two coefficients as control values. With this method, it is possible to decrease thresholding error which occurs in the case of low contrast and brightness change. Pattern in similar gray‐level areas can also be clarified. The proposed method and the conventional methods were tested, using general outdoor scene images. The results showed that the proposed method can output good‐quality binary images and is useful for thresholding of scene images. © 2013 Wiley Periodicals, Inc. Electron Comm Jpn, 97(1): 82–88, 2014; Published online in Wiley Online Library (http://wileyonlinelibrary.com). DOI 10.1002/ecj.11495

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%