• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.287-287
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• 2000

As a method to control the bacteria number in adequate level, a real time control system based on microscope image processing measurement for the bacteria is adopted. For the experiment, Ammonia-oxidizing bacteria such as Acinetobacter sp. are used. This paper proposed hybrid method combined watershed algorithm with adaptive automatic thresholding method to enhance segmentation efficiency of overlapped image. Experiments was done to show the effectiveness of the proposed method compared to traditional Otsu's method, Otsu's method with adaptive automatic thresholding method and human visual method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.29-37
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• 2006

Thresholding is a fundamental approach to segmentation that utilizes a significant degree of pixel popularity or intensity. Otsu's thresholding employed the normalized histogram as a discrete probability density function. Also it utilized a criterion that minimizes the between-class variance of pixel intensity to choose a threshold value for segmentation. However, the Otsu's method has a disadvantage of repeatedly searching optimal thresholds for the entire range. In this paper, a simple but fast multi-level thresholding approach is proposed by means of extending the Otsu's method. Rather than invoke the Otsu's method for the entire gray range, we advocate that the gray-level range of an image be first divided into smaller sub-ranges, and that the multi-level thresholds be achieved by iteratively invoking this dividing process. Initially, in the proposed method, the gray range of the object image is divided into 2 classes with a threshold value. Here, the threshold value for segmentation is selected by invoking the Otsu's method for the entire range. Following this, the two classes are divided into 4 classes again by applying the Otsu's method to each of the divided sub-ranges. This process is repeatedly performed until the required number of thresholds is obtained. Our experimental results for three benchmark images and fifty faces show a possibility that the proposed method could be used efficiently for pattern matching and face recognition.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.18-25
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• 2011

Salient object attracts more and more attention from researchers due to its important role in many fields of multimedia processing like tracking, segmentation, adaptive compression, and content-base image retrieval. Usually, a saliency map is binarized into black and white map, which is considered as the binary mask of the salient object in the image. Still, the threshold is heuristically chosen or parametrically controlled. This paper suggests using the global optimal threshold to perform saliency map thresholding. This work also considers the usage of multi-level optimal thresholds and the local adaptive thresholds in the experiments. These experimental results show that using global optimal threshold method is better than parametric controlled or local adaptive threshold method.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.126-128
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• 2006

For pattern matching, an object image should be segmented and analyzed for the first time. Thresholding is a fundamental approach to segmentation that utilizes a significant degree of pixel popularity or intensity. Otsu's thresholding is one of the most veil-known methods proposed in the literature. However, the method has a disadvantage of repeatedly searching the optimal thresholds for the entire region. To overcome this problem, a number of methods have been proposed. In this paper, we propose a simple and fast thresholding method of finding multi-level threshold values by extending the Otsu's method. Our experimental results for the benchmak images show a possibility that the proposed method could be used efficiently for pattern matching.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1305-1318
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• 2012

Image segmentation is one of the difficult research problems in machine vision and pattern recognition field. A commonly used segmentation method is the Otsu method. It is simpler and easier to implement but it fails if the histogram is unimodal or similar to unimodal. And if some target area is smaller than background object, then its histogram has the distribution close to unimodal. In this paper, we proposed an improved image segmentation method based on 1D Otsu method for a small target detection. To overcome drawbacks by unimodal histogram effect, we depressed the background histogram using a logarithm function. And to improve a signal to noise ratio, we used a local average value by the neighbor window for thresholding using 1D Otsu method. The experimental results show that our proposed algorithm performs better segmentation result than a traditional 1D Otsu method, and needs much less computational time than that of the 2D Otsu method.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.3
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• pp.224-230
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• 2002

In this paper, a counting algorithm hybridized with an adaptive automatic thresholding method based on Otsu's method and the algorithm that elongates markers obtained by the well-known watershed algorithm is proposed to enhance the exactness of the microcell counting in microscopic images. The proposed counting algorithm can be stated as follows. The transformed full image captured by CCD camera set up at microscope is divided into cropped images of m$\times$n blocks with an appropriate size. The thresholding value of the cropped image is obtained by Otsu's method and the image is transformed into binary image. The microbial cell images below prespecified pixels are regarded as noise and are removed in tile binary image. The smoothing procedure is done by the area opening and the morphological filter. Watershed algorithm and the elongating marker algorithm are applied. By repeating the above stated procedure for m$\times$n blocks, the m$\times$n segmented images are obtained. A superposed image with the size of 640$\times$480 pixels as same as original image is obtained from the m$\times$n segmented block images. By labeling the superposed image, the counting result on the image of microbial cells is achieved. To prove the effectiveness of the proposed mettled in counting the microbial cell on the image, we used Acinetobacter sp., a kind of ammonia-oxidizing bacteria, and compared the proposed method with the global Otsu's method the traditional watershed algorithm based on global thresholding value and human visual method. The result counted by the proposed method shows more approximated result to the human visual counting method than the result counted by any other method.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.29-39
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• 2020

Forest fire is a critical disaster that causes massive destruction of forest ecosystem and economic loss. Hence, accurate estimation of the burned area is important for evaluation of the degree of damage and for preparing baseline data for recovery. Since most of the area size damaged by wildfires in Korea is less than 1 ha, it is necessary to use satellite or drone images with a resolution of less than 10m for detecting the damage area. This paper aims to detect wildfire-damaged area from a Kompsat-3 image using the indices such as NDVI (normalized difference vegetation index) and FBI (fire burn index) and to examine the classification characteristics according to the methods such as Otsu thresholding and ISODATA(iterative self-organizing data analysis technique). To mitigate the salt-and-pepper phenomenon of the pixel-based classification, a gaussian filter was applied to the images of NDVI and FBI. Otsu thresholding and ISODATA could distinguish the burned forest from normal forest appropriately, and the salt-and-pepper phenomenon at the boundaries of burned forest was reduced by the gaussian filter. The result from ISODATA with gaussian filter using NDVI was closest to the official record of damage area (56.9 ha) published by the Korea Forest Service. Unlike Otsu thresholding for binary classification,since the ISODATA categorizes the images into multiple classes such as(1)severely burned area, (2) moderately burned area, (3) mixture of burned and unburned areas, and (4) unburned area, the characteristics of the boundaries consisting of burned and normal forests can be better expressed. It is expected that our approach can be utilized for the high-resolution images obtained from other satellites and drones.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.466-471
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• 2007

Conventional thresholding methods including Otsu's thresholding method are based on the gray levels frequency histogram. But the gray levels frequency histogram is obtained by recomposing only frequency information from an input image, where frequency histogram dose not contain any other informations such as the distribution of gray levels and relation between gray levels. Therefore the methods using the gray levels frequency histogram occasionally present inappropriate threshold values because it cannot reflect informations of the given image sufficiently. In this paper, we define a correlation function of gray levels and propose a novel thresholding method using the gray levels frequency histogram and the spatial correlation information. The effectiveness of the proposed method will be shown through comparison with Otsu's thresholding method.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.163-169
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• 2017

There are various algorithms evaluating a threshold for image segmentation. Among them, Otsu's algorithm sets a threshold based on the histogram. It finds the between-class variance for all over gray levels and then sets the largest one as Otsu's optimal threshold, so we can see that Otsu's algorithm requires a lot of the computation. In this paper, we improved the amount of computational needs by using estimated Otsu's threshold rather than computing for all the threshold candidates. The proposed algorithm is compared with the original one in computation amount and accuracy. we confirm that the proposed algorithm is about 29 times faster than conventional method on single processor and about 4 times faster than on parallel processing architecture machine.

• Journal of Biosystems Engineering
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• v.38 no.2
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• pp.149-162
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• 2013

Purpose: In image segmentation via thresholding, Otsu and Kapur methods have been widely used because of their effectiveness and robustness. However, computational complexity of these methods grows exponentially as the number of thresholds increases due to the exhaustive search characteristics. Methods: Particle swarm optimization (PSO) and genetic algorithms (GAs) can accelerate the computation. Both methods, however, also have some drawbacks including slow convergence and ease of being trapped in a local optimum instead of a global optimum. To overcome these difficulties, we proposed two new multi-level thresholding methods based on Bacteria Foraging PSO (BFPSO) and real-coded GA algorithms for fast segmentation. Results: The results from BFPSO and real-coded GA methods were compared with each other and also compared with the results obtained from the Otsu and Kapur methods. Conclusions: The proposed methods were computationally efficient and showed the excellent accuracy and stability. Results of the proposed methods were demonstrated using four real images.