• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.571-574
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• 2005

In this paper, we propose an efficient image segmentation technique for large-sized monochrome images using a hybrid approach which combines threshold and region-based techniques. First, an image is partitioned into fixed-size blocks and for each block the representative intensity is determined by averaging pixel intensities within the block. Next, the neighborhood blocks that have similar characteristics with respect to a specific threshold are merged in order to form candidate regions. Finally, those candidate regions are refined to get final target object regions by merging regions considering the spatial locality and certain criteria. We have performed experiments on images selected from various domains and showed that our technique was able to extract target object regions appropriately from most images.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1183-1189
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• 2011

In this study, we propose the unsupervised image segmentation algorithm to estimate dental plaque accumulations on digital imaging with methylene blue disclosed plaque. With this aim, RGB color plane is mapped into HSI coordinates and the circular histogram of Hue is reconstructed by applying Otsu's threshold level. The histogram distribution on Saturation features is also analyzed by maximizing the variance between a plaque candidate and non-plaque one. The dental plaque regions are resolved by applying the composite decision logics based on the threshold level of Hue and Saturation.

• 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.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.2
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• pp.146-159
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• 2023

This paper proposes a hybrid algorithm combining K-means clustering and watershed algorithms for flower segmentation and counting. We use the K-means clustering algorithm to obtain the main colors in a complex background according to the cluster centers and then take a color space transformation to extract pixel values for the hue, saturation, and value of flower color. Next, we apply the threshold segmentation technique to segment flowers precisely and obtain the binary image of flowers. Based on this, we take the Euclidean distance transformation to obtain the distance map and apply it to find the local maxima of the connected components. Afterward, the proposed algorithm adaptively determines a minimum distance between each peak and apply it to label connected components using the watershed segmentation with eight-connectivity. On a dataset of 30 images, the test results reveal that the proposed method is more efficient and precise for the counting of overlapped flowers ignoring the degree of overlap, number of overlap, and relatively irregular shape.

• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1644-1649
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• 2018

Aiming at infrared thermal images with different buried depth defects, we study a variety of image segmentation algorithms based on the threshold to develop global search ability and the ability to find the defect area accurately. Firstly, the iterative thresholding method, the maximum entropy method, the minimum error method, the Ostu method and the minimum skewness method are applied to image segmentation of the same infrared thermal image. The study shows that the maximum entropy method and the minimum error method have strong global search capability and can simultaneously extract defects at different depths. However none of these five methods can accurately calculate the defect area at different depths. In order to solve this problem, we put forward a strategy of "divide and conquer". The infrared thermal image is divided into several local thermal maps, with each map containing only one defect, and the defect area is calculated after local image processing of the different buried defects one by one. The results show that, under the "divide and conquer" strategy, the iterative threshold method and the Ostu method have the advantage of high precision and can accurately extract the area of different defects at different depths, with an error of less than 5%.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.9-16
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• 2015

This paper proposes the parallel design of a shot change detection algorithm using frame segmentation and moving blocks. In the proposed approach, the high parallel processing components, such as frame histogram calculation, block histogram calculation, Otsu threshold setting function, frame moving operation, and block histogram comparison, are designed in parallel for NVIDIA GPU. In order to minimize memory access delay time and guarantee fast computation, the output of a GPU kernel becomes the input data of another kernel in a pipeline way using the shared memory of GPU. In addition, the optimal sizes of CUDA processing blocks and threads are estimated through the prior experiments. In the experimental test of the proposed shot change detection algorithm, the detection rate of the GPU based parallel algorithm is the same as that of the CPU based algorithm, but the average of processing time speeds up about 6~8 times.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.239-246
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• 2005

Biologist must have to do 2DGE biological experiment for Protein Search and Analysis. This experiment coming into being 2 dimensional image. 2DGE (2D Gel Electrophoresis : two dimensional gel electrophoresis) image is the most widely used method for isolating of the objective protein by comparative analysis of the protein spot pattern in the gel plane. The process of protein spot analysis, firstly segment protein spots that are spread in 2D gel plane by image processing and can find important protein spots through comparative analysis with protein pattern of contrast group. In the algorithm which detect protein spots, previous 2DGE image analysis is applies gaussian fitting, however recently Watersheds region based segmentation algorithm, which is based on morphological segmentation is applied. Watersheds has the benefit that segment rapidly needed field in big sized image, however has under-segmentation and over-segmentation of spot area when gray level is continuous. The drawback was somewhat solved by marker point institution, but needs the split and merge process. This paper introduces a novel marker search of protein spots by watersheds-based hierarchical threshold, which can resolve the problem of marker-driven watersheds.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.55-68
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• 2012

In this paper, we propose a novel method to solve the problem of excessive segmentation out of the method of segmenting regions from an image using Homogeneity Threshold($H_T$). The algorithm of the previous image segmentation based on $H_T$ was carried out region growth by using only the center pixel of selected window. Therefore it was caused resulting in excessive segmented regions. However, before carrying region growth, the proposed method first of all finds out whether the selected window is homogeneity or not. Subsequently, if the selected window is homogeneity it carries out region growth using the total pixels of selected window. But if the selected window is not homogeneity, it carries out region growth using only the center pixel of selected window. So, the method can reduce remarkably the number of excessive segmented regions of image segmentation based on $H_T$. In order to show the validity of the proposed method, we carried out multiple experiments to compare the proposed method with previous method in same environment and conditions. As the results, the proposed method can reduce the number of segmented regions above 40% and doesn't make any difference in the quality of visual image when we compare with previous method. Especially, when we compare the image united with regions of descending order by size of segmented regions in experimentation with the previous method, even though the united image has regions more than 1,000, we can't recognize what the image means. However, in the proposed method, even though image is united by segmented regions less than 10, we can recognize what the image is. For these reason, we expect that the proposed method will be utilized in various fields, such as the extraction of objects, the retrieval of informations from the image, research for anatomy, biology, image visualization, and animation and so on.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.1062-1075
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• 2010

In the medical image application the difference of intensity is widely used for the image segmentation and feature extraction, and a well known method is the threshold technique that determines a threshold value and generates a binary image based on the threshold. A frequently-used threshold technique is the Otsu algorithm that provides efficient processing and effective selection criterion for choosing the threshold value. However, we cannot get good segmentation results by applying the Otsu algorithm to chest X-ray images. It is because there are various organic structures around lung regions such as ribs and blood vessels, causing unclear distribution of intensity levels. To overcome the ambiguity, we propose in this paper an effective algorithm to extract pulmonary regions that utilizes the Otsu algorithm after removing the background of an X-ray image, constructs intensity-level maps, and uses them for segmenting the X-ray image. To verify the effectiveness of our method, we compared it with the existing 1-dimensional and 2-dimensional Otsu algorithms, and also the results by expert's naked eyes. The experimental result showed that our method achieved the more accurate extraction of pulmonary regions compared to the Otsu methods and showed the similar result as the naked eye's one.

• Annual Conference of KIPS
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• 2016.10a
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• pp.733-734
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• 2016

This study proposes a novel approach for ground segmentation of 3D point cloud. We combine two techniques: gradient threshold segmentation, and mean height evaluation. Acquired 3D point cloud is represented as a graph data structures by exploiting the structure of 2D reference image. The ground parts nearing the position of the sensor are segmented based on gradient threshold technique. For sparse regions, we separate the ground and nonground by using a technique called mean height evaluation. The main contribution of this study is a new ground segmentation algorithm which works well with 3D point clouds from various environments. The processing time is acceptable and it allows the algorithm running in real time.