• Journal of Information Processing Systems
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• v.13 no.4
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.125-132
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• 2024

Optical satellite images are being used for national security and collection of information, and their utilization is increasing. However, it acquires low-quality images that are not suitable for the user's requirement due to weather conditions and time constraints. In this paper, a deep learning-based conversion of image and colorization model referring to high-resolution SAR images was created to simulate the occluded area with clouds of optical satellite images. The model was experimented according to the type of algorithm applied and input data, and each simulated images was compared and analyzed. In particular, the amount of pixel value information between the input black-and-white image and the SAR image was similarly constructed to overcome the problem caused by the relatively lack of color information. As a result of the experiment, the histogram distribution of the simulated image learned with the Gray-scale image and the high-resolution SAR image was relatively similar to the original image. In addition, the RMSE value was about 6.9827 and the PSNR value was about 31.3960 calculated for quantitative analysis.

• Journal of Advanced Marine Engineering and Technology
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• v.12 no.3
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• pp.43-56
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• 1988

The image processing system consisted of the microcomputer IBM PC-XT and the graphic board (16 gray level and $640{\times}400$ pixels resolution) has been proposed, and the image processing softwares programmed in the BASIC and in the assembler language have been developed. The programs are consisted of the main menu and the sub menu, that have contained the subroutine for the capture for image data, the determination of region, the histogram, the change of value, the montage, the skeleton, the mask, the moving, the zoom, the disk access and the print. For the application, the photoelastic fringe data have been captured and analyzed. It was seen that the programs are available for the image processing.

• Journal of the Korean Society of Clothing and Textiles
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• v.20 no.5
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• pp.841-851
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• 1996

The purpose of this study is to investigate the backstaining generated during cellulase treatment for indigo-dyed denim. The results were as follows. 1. High generating conditions of the backstaining were as same as best conditions for cellulose activity (temperature at 6$0^{\circ}C$, pH at about 4.5~5.5, treatment time at 40 min. and enzyme concentration at 2g/l). And also, liquor ratio, sample weight and repeated-use cycle of liquor had influenced on the backstaining. 2. The backstaining was decreased about 30~40% when various additives, that is, surfactant, anti-backstaining agent (C.M.C) and softners were added. 3. A application of image processing on the backstaining evaluation was more effective than method by reflectance, particullarly in out of level fabrics. In image analysis, the backstainings were measured by histogram between 256 gray levels.

• Journal of Biosystems Engineering
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• v.17 no.2
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• pp.132-142
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• 1992

The main objective of this paper is to evaluate and modify the existing algorithms for the automatic threshold selection. Four existing algorithms were evaluated quantitatively using test images of coffee droplets and an apple. The images had the different area ratio of the object to the image size, different average gray values between the object and the background, and different S/N ratio of the Gaussian noise. The result showed that Histogram Clustering Method and Maximum Entropy Method were better than Moment Preserving Method and Simple Image Statistic Method in automatic thresholding.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.942-952
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• 2006

We propose an automatic segmentation method for identifying pulmonary structures using gray-level information of chest CT images. Our method consists of following five steps. First, to segment pulmonary structures based on the difference of gray-level value, we select the threshold using optimal thresholding. Second, we separate the thorax from the background air and then the lungs and airways from the thorax by applying the inverse operation of 2D region growing in chest CT images. To eliminate non-pulmonary structures which has similar intensities with the lungs, we use 3D connected component labeling. Third, we segment the trachea and left and right mainstem bronchi using 3D branch-based region growing in chest CT images. Fourth, we can obtain accurate lung boundaries by subtracting the result of third step from the result of second step. Finally, we select the threshold in accordance with histogram analysis and then segment radio-dense pulmonary vessels by applying gray-level thresholding to the result of the second step. To evaluate the accuracy of proposed method, we make a visual inspection of segmentation result of lungs, airways and pulmonary vessels. We compare the result of the conventional region growing with the result of proposed 3D branch-based region growing. Experimental results show that our proposed method extracts lung boundaries, airways, and pulmonary vessels automatically and accurately.

• Korean Journal of Human Ecology
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• v.1 no.2
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• pp.113-118
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• 1998

Clothing shape is principally described in seven factors that are composed of clothing design, clothing material, clothing size, pattern design, sewing method and body motion etc.. The aims of this study was to measurement out-line space-shape variation of clothing fitness with somato type by using the image processing. The subjects for direct anthropometric measurements were 248 female college students aged from 19 to 22. The data were statistically analyzed by principal analysis and cluster analysis. The results were obtained three somato type. Also I made skirts in order to analyzed to the out-line space-shape variation of clothing fitness with body. The effect of somato type on the shape of flare skirts was determined by the out-line space-shape variation of clothing fitness with body. The out-line space-shape variation of clothing fitness with body was observed between the node number and amplitudes of clothing wave form and node number was determined at the maxim of space-shape amplitude, and the space-shape amplitudes have related with aspect ratio of cross-sectional shape. Results for flare skirts show changes in amplitude and mean with fabrics, somato type. therefore gray-level histogram are correlated with changes out-line space-shape, differences in drape spacing and related fabric properties and their somato type. (Korean J Human Ecology 1(2):113∼110 1998)

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.741-747
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• 2012

In this paper, we propose a fast and robust framework for detecting and tracking multiple targets. The proposed system includes two modules: object detection module and object tracking module. In the detection module, we preprocess the input images frame by frame, such as gray and binarization. Next after extracting the foreground object from the input images, morphology technology is used to reduce noises in foreground images. We also use a block-based histogram analysis method to distinguish human and other objects. In the tracking module, color-based tracking algorithm and Kalman filter are used. After converting the RGB images into HSV images, the color-based tracking algorithm to track the multiple targets is used. Also, Kalman filter is proposed to track the object and to judge the occlusion of different objects. Finally, we show the effectiveness and the applicability of the proposed method through experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.516-521
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• 2010

In this paper, we propose a video-based intelligent unmanned fire surveillance system using fuzzy color models. In general, to detect heat or smoke, a separate device is required for a fire surveillance system, this system, however, can be implemented by using widely used CCTV, which does not need separate devices and extra cost. The systems called video-based fire surveillance systems use mainly a method extracting smoke or flame from an input image only. The smoke is difficult to extract at night because of its gray-scale color, and the flame color depends on the temperature, the inflammable, the size of flame, etc, which makes it hard to extract the flame region from the input image. This paper deals with a intelligent fire surveillance system which is robust against the variation of the flame color, especially at night. The proposed system extracts the moving object from the input image, makes a decision whether the object is the flame or not by means of the color obtained by fuzzy color model and the shape obtained by histogram, and issues a fire alarm when the flame is spread. Finally, we verify the efficiency of the proposed system through the experiment of the controlled real fire.

• The Journal of Advanced Prosthodontics
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• v.14 no.3
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• pp.143-149
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• 2022

PURPOSE. Masticatory loading triggers active bone remodeling, altering alveolar bone mineral density (BMD). While dental implants are placed to bear masticatory loading, their influence on changing bone properties has not been fully investigated. Objective of this pilot study was to examine whether the dental implantation has an effect on BMD distribution of bone by comparing dentate, edentulous, and edentulous patients with implants. MATERIALS AND METHODS. Cone beam computed tomography (CBCT) images of 19 partially edentulous patients (Dent), 19 edentulous patients (Edent), and 16 edentulous patients who received implants in the mandible (Edent+Im), were obtained. CBCT images were also obtained from 5 patients within Edent+Im group, before implant placement and after implant loading. Basal cortical bone region of the mandible was digitally isolated. A histogram of gray levels proportional to BMD was obtained to assess mean, histogram standard deviation (HSD), fifth percentile of low and high values (Low₅ and High₅) of the BMD distribution. Multivariate analysis of variance and paired t-test were used to compare the BMD parameters among the 3 dental status groups and between pre- and post-implantation, respectively. RESULTS. Edentulous patients with implants had significantly greater HSD and High₅ values compared to edentulous patients (P < .013). All other comparisons were not significant (P > .097). Mean, HSD, and High₅ values significantly increased after receiving implants (P < .022). CONCLUSION. The current findings suggested that receiving dental implants promoted oral bone mineralization for edentulous patients. The longitudinal investigation could provide valuable information on understanding the effects of implantation on the behavior of oral bone quality.