• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2113-2128
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• 2019

Image thresholding techniques introducing spatial information are widely used image segmentation. Some methods are used to calculate the optimal threshold by building a specific histogram with different parameters, such as gray value of pixel, average gray value and gradient-magnitude, etc. However, these methods still have some limitations. In this paper, an entropic thresholding method based on Gabor histogram (a new 2D histogram constructed by using Gabor filter) is applied to image segmentation, which can distinguish foreground/background, edge and noise of image effectively. Comparing with some methods, including 2D-KSW, GLSC-KSW, 2D-D-KSW and GLGM-KSW, the proposed method, tested on 10 realistic images for segmentation, presents a higher effectiveness and robustness.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.429-430
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• 2011

Roughness set theory is a popular topic to use in color-image segmentation. A new popular color image segmentation algorithm is proposed by scientists with the point using traditional histogram and Histon construct roughness set histogram. But, there is still a problem about that is the correlativity of color vector in roughness set histogram, which take an inactive effect in the process of color-image segmentation. Therefore, this paper represents further research based on this and proposed an improved method proved through lot of experiments. The experimental result reduces the correlativity of color vector in roughness set histogram and calculation time remarkably.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.189-194
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• 2017

In this study, we propose a method which is based on "Image segmentation by histogram thresholding using hierarchical cluster analysis"/HCA/ and "A nonparametric approach for histogram segmentation"/NHS/. HCA method uses that all histogram bins are one cluster then it reduces cluster numbers by using distance metric. Because this method has too many clusters, it is more computation. In order to eliminate disadvantages of "HCA" method, we used "NHS" method. NHS method finds all local minima of histogram. To reduce cluster number, we use NHS method which is fast. In our approach, we combine those two methods to eliminate disadvantages of Arifin method. The proposed method is not only less computational than "HCA" method because combined method has few clusters but also it uses local minima of histogram which is computed by "NHS".

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.4
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• pp.46-54
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• 2003

In this paper, we propose segmentation algorithm for MR brain images using the histogram of T1-weighted, T2-weighted and PD images. Segmentation algorithm is composed of 3 steps. The first step involves the extraction of cerebrum images by ram a cerebrum mask over three input images. In the second step, peak ranges are determined from the histogram of the cerebrum image. In the final step, cerebrum images are segmented using coarse to fine clustering technique. We compare the segmentation result and processing time according to peak ranges. Also compare with the other segmentation methods. The proposed algorithm achieved better segmentation results than the other methods.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1771-1774
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• 2003

Image segmentation is very important technique as preprocessing. It is used for various applications such as object recognition, computer vision, object based image compression. In this paper, a method which segments the multidimensional image using a hierarchical histogram approach, is proposed. The hierarchical histogram approach is a method that decomposes the multi-dimensional situation into multi levels of 1 dimensional situations. It has the advantage of the rapid and easy calculation of the histogram, and at the same time because the histogram is applied at each level and not as a whole, it is possible to have more detailed partitioning of the situation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.6
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• pp.1020-1024
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• 1987

Image segmentation based on the curvature of bi-directiona distribution functions of histogram with no mode informations is proposed. The curvature is an oscillating function and can be approximated to a polynomial form with a least square method using the Chebyshev basis. Nonhomogeneous linea equations are solved by Gauss-elimination method. In the proposed algorithm, critical points of the curvature are obtained on each direction to compensate the segmentation parameters, which can be ignored in only one-directional histogram.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.231-233
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• 2009

The object recognition is one of the major computer vision fields. The object recognition using features(SIFT) is finding common features in input images and query images. But the object recognition using feature methods has suffered of difficulties due to heavy calculations when resizing input images and query images. In this paper, we focused on speed up finding features in the images. we proposed method using block segmentation and histogram. Block segmentation used diving input image and than histogram decided correlation between each 1]lock and query image. This paper has confirmed that tile matching time reduced for object recognition since reducing block.

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.547-555
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• 2014

This paper presents a method for hand segmentation using depth information, and adaptive threshold by means of histogram analysis and color clustering in HSV color model. We consider hand area as a nearer object to the camera than background on depth information. And the threshold of hand color is adaptively determined by clustering using the matching of color values on the input image with one of the regions of hue histogram. Experimental results demonstrate 95% accuracy rate. Thus, we confirmed that the proposed method is effective for hand segmentation in variations of hand color, scale, rotation, pose, different lightning conditions and any colored background.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.856-862
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• 1987

An local adaptive image segmentatin algorithm for local feature detection and effective clustering of unimodal histogram shape are proposed. Local adaptive difference image and its histogram are obtained from the input image. The parameters are derived from the histogram and used for the segmentation based on relaxatin process. The results showed effective region segmentation and good noise cleaning for the ocular fundus fluorescein angiogram which has low contrast and unimodal histogram.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.431-436
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• 2014

Cell segmentation which extracts cell objects from background is one of basic works in bio-imaging which analyze cell images acquired from live cells in cell culture. In the case of clear images, they have a bi-modal histogram distribution and segmentation of them can easily be performed by global threshold algorithm such as Otsu algorithm. But In the case of degraded images, it is difficult to get exact segmentation results. In this paper, we developed a cell segmentation system that it classify input images by the type of their histogram distribution and then apply a proper segmentation algorithm. If it has a bi-modal distribution, a global threshold algorithm is applied for segmentation. Otherwise it has a uni-modal distribution, our algorithm is performed. By experimentation, our system gave exact segmentation results for uni-modal cell images as well as bi-modal cell images.