• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.77-80
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• 2000

In this paper, we present a new automatic thresholding algorithm based on maximum entropy of two-dimensional pixel histogram. While most of the previous algorithms select thresholds depending only on the histogram of gray level itself in the image, the presented algorithm considers 2D relational histogram of gray levels of two adjacent pixels in the image. Thus, the new algorithm tends to leave salient edge features on the image after thresholding. The experimental results show the good performance of the presented algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.649-654
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• 2003

Most of the conventional image thresholding methods are based on the histogram function of the gray values. In this paper, we present a simple but effective example showing that the histogram-based thresholding methods do not perform well. To overcome the difficulty, the authors propose a new gray level threshold selection method based on the distribution of gray levels in images. Finally, we provide simulation results showing the effectiveness of the proposed threshold selection method through several examples.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1235-1238
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• 2004

Thresholding has been used to reduce the number of gray values in images. Typically, a single threshold value has been used, resulting in two gray level images. Image reduction of one single threshold value, however, may lose too much of the high-frequency edge information. Thus, dynamic thresholding that uses a different threshold for each pixel is preferred instead of using a single threshold value. Dynamic thresholding can preserve high frequency details as well as reduce the size of images. Since it takes long time to perform existing software dynamic thresholding in an embedded system, this paper proposes and implements a circuit by using a FPGA in order to perform a real-time dynamic thresholding,. The proposed circuit consists of two counters, and threshold look-up table, and control unit. The values of two counters determine each pixel position, the threshold look-up table converts each pixel value into other value, and the control unit generates necessary control signals. On arriving from a camera to the proposed circuit, each pixel is compared with its threshold value and is converted into other gray value. An image processing system by using the proposed circuit will be implemented and some experiments will be performed.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.488-491
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• 1987

In this paper. a heuristic multi-thresholding algorithm is proposed to extract objects from background. Specifically the proposed algorithm finds out multi valleys from gray level histogram automatically and non-recursively. Some experimental result for various types of image. are presented, to show the effectiveness of the proposed algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.607-609
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• 2002

본 논문은 흉부 전산화단층촬영 영상에서 폐 영역을 자동으로 분할하는 알고리즘과 폐결절을 자동으로 검출하는 알고리즘에 관한 연구 내용을 담고 있다. 폐 분할 알고리즘은 gray-level thresholding과 morphologic 영상 처리기법을 이용하였고, 폐결절 자동 검출 알고리즘은 추출된 결절 후보의 size, compactness, mean of gray level 값을 분석하여 혈관과 결절을 구분하였다. 개발한 폐결절 자동 검출 시스템은 실험한 영상에 포함된 폐결절 117개 중 55%인 64개를 검출하였고, 3.4 False Positive/section이었다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.112-122
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• 1994

Number plates of vehicles operating in real world are sometimes difficult to recognize due to the number plate degradation (bent or dirty plates). To recognize the vehicle number from a number plate with severe degradation, good segmentation is necessary, which in turn requires good thresholding. This paper proposes a binarization method that combines the fast processing speed of global thresholding methods with the local thresholding methods' ability to adapt to lacal gray level characteristics. The proposed method overcomes the degradation of number plates quickly and maintains the widths of digit strokes uniform. The paper presents results of comparison with existing global and local thresholding methods.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1247-1254
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• 2003

Currently. the north american symptomatic carotid endarterectomy trial, european carotid surgery trial, and common carotid method are used to measure the carotid stenosis for determining candidate for carotid endarterectomy using the projection angiography from different modalities such as digital subtraction angiography. rotational angiography, computed tomography angiography and magnetic resonance angiography. A new computerized carotid stenosis measuring system was developed using MR angiography axial image to overcome the drawbacks of conventional carotid stenosis measuring methods, to reduce the variability of inter-observer and intra-observer. The gray-level thresholding is one of the most popular and efficient method for image segmentation. We segmented the carotid artery and lumen from three-dimensional time-of-flight MRA axial image using gray-level thresholding technique. Using the measured intima-media thickness value of common carotid artery for each cases, we separated carotid artery wall from the segmented carotid artery region. After that, the regions of segmented carotid without artery wall were divided into region of blood flow and plaque. The calculation of carotid stenosis degree was performed as the following; carotid stenosis grading is(area measure of plaque/area measure of blood flow region and plaque) * 100%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.298-301
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• 1992

A digital image processing approach has been adopted to measure the flank wear area, which is very difficult to measure using conventional techniques. Automatic thresholding of the gray-level values of an image is very useful in automated analysis of image. 1-D entropy thresholding technique is used for image processing and analysis of the flank wear area. This strategy provides more information about drill wear conditions and should therefore have a higher reliability than previous methods. This study calulated quantitatively the flank were area of drill by computer program.