• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.193-201
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• 2006

This paper presents a method that allows for detection of all rapid and gradual scene changes. The method features a combination of the current color histogram and the local $X^{2}-test$. For the purpose of this paper, the $X^{2}-test$ scheme outperforming existing histogram-based algorithms was transformed, and a local $X^{2}-test$ in which weights were applied in accordance with the degree of brightness was used to increase detection efficiency in the segmentation of color values. This Method allows for analysis and segmentation of complex time-varying images in the most general and standardized manner possible Experiments were performed to compare the proposed local $X^{2}-test$ method with the current $X^{2}-test$ method.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.2994-3001
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• 2000

In this paper. a method for color image segmentation using region merging is proposed. A inhomogeneity which exists in image is reduced by smoothing with non-linear filtering. saturation enhancement and intensity averaging in previous step of image segmentation. and a similar regions are segmented by non-uniform quantization using zero-crossing information of color histogram. A edge strength of initial region is measured using high frequency energy of wavelet transform. A candidate region which is merged in next step is selected by doing this process. A similarity measure for region merging is processed using Euclidean distance of R. G. B color channels. A Proposed method can reduce an over-segmentation results by irregular light sources et. al, and we illustrated that the proposed method is reasonable by simulation.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.361-368
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• 2014

The LPR(License plate recognition) system has been developed to efficient control for complex traffic environment and currently be used in many places. However, because of light, noise, background changes, environmental changes, damaged plate, it only works limited environment, so it is difficult to use in real-time. This paper presents a heuristic segmentation algorithm for robust to noise and illumination changes and introduce a real-time license plate recognition system using it. In first step, We detect the plate utilized Haar-like feature and Adaboost. This method is possible to rapid detection used integral image and cascade structure. Second step, we determine the type of license plate with adaptive histogram equalization, bilateral filtering for denoise and segment accurate character based on adaptive threshold, pixel projection and associated with the prior knowledge. The last step is character recognition that used histogram of oriented gradients (HOG) and multi-layer perceptron(MLP) for number recognition and support vector machine(SVM) for number and Korean character classifier respectively. The experimental results show license plate detection rate of 94.29%, license plate false alarm rate of 2.94%. In character segmentation method, character hit rate is 97.23% and character false alarm rate is 1.37%. And in character recognition, the average character recognition rate is 98.38%. Total average running time in our proposed method is 140ms. It is possible to be real-time system with efficiency and robustness.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.74-86
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• 2006

This paper proposes a robust and computationally efficient algorithm for automatic video object segmentation. For implementing the spatio-temporal segmentation, which aims for efficient combination of the motion segmentation and the color segmentation, an SOM-based hierarchical clustering method in which the segmentation process is regarded as clustering of feature vectors is employed. As results, problems of high computational complexity which required for obtaining exact segmentation results in conventional video object segmentation methods, and the performance degradation due to noise are significantly reduced. A measure of motion vector reliability which employs MRF-based MAP estimation scheme has been introduced to minimize the influence from the motion estimation error. In addition, a noise elimination scheme based on the motion reliability histogram and a clustering validity index for automatically identifying the number of objects in the scene have been applied. A cross projection method for effective object tracking and a dynamic memory to maintain temporal coherency have been introduced as well. A set of experiments has been conducted over several video sequences to evaluate the proposed algorithm, and the efficiency in terms of computational complexity, robustness from noise, and higher segmentation accuracy of the proposed algorithm have been proved.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.137-146
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• 2015

In this paper, we propose a pothole detection method using various features of intensity and motion. Segmentation, decision steps of pothole detection are processed according to the values which are derived from feature characteristics. For segmentation using intensity, we use a binarization method using histogram to separate pothole region from background. For segmentation using motion, we filter using high pass filter and get standard deviation value. This value is divided by regression value according to camera environment such as photographing angle, height, velocity, etc. We get binary image by histogram based binarization. For decision, candidate regions are decided whether pothole or not using comparison of candidate and background's features. Experimental results show that our proposed pothole detection method has better results than existing methods and good performance in discrimination between pothole and similar patterns.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.645-655
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• 2013

As one of segmentation techniques for Synthetic Aperture Radar (SAR) image with oil spill, we applied a bimodal histogram method to discriminate oil pixels from non-oil pixels. The threshold of each moving window was objectively determined using the two peaks in the histogram distribution of backscattering coefficients from ENVISAT ASAR image. To reduce the effect of wind speed on oil spill detection, we selected ASAR image which satisfied a limit of wind speeds for successful detection. Overall, a commonly used adaptive threshold method has been applied with a subjectively-determined single threshold. In contrast, the bimodal histogram method utilized herein produces a variety of thresholds objectively for each moving window by considering the characteristics of statistical distribution of backscattering coefficients. Comparison between the two methods revealed that the bimodal histogram method exhibited no significant difference in terms of performance when compared to the adaptive threshold method, except for around the edges of dark oil spots. Thus, we anticipate that the objective method based on the bimodality of oil slicks may also be applicable to the detection of oil spills from other SAR imagery.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.1062-1067
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• 2003

A vision system for Intelligent vehicles here. The system exploits the characteristics of the gray level histogram of the road to detect lane markers. Each lane maker is then analyzed using a decision tree, and finally the relations between lane markers are analyzed to create structures defining the lane boundaries. The resulting system also generates images that can be used ae preprocessing stages in lane detection, lane tracking or obstacle detection algorithm. The system runs in realtime ay rates of about 30Hz.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.222-223
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• 2007

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.35-42
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• 2013

Shot boundary detection is an essential step for efficient browsing, sorting, and classification of video data. Robust shot detection method should overcome the disturbances caused by pixel brightness and object movement between frames. In this paper, two shot boundary detection methods are presented to address these problem by using segmentation, object movement, and pixel brightness. The first method is based on the histogram that reflects object movements and the morphological dilation operation that considers pixel brightness. The second method uses the pixel brightness information of segmented and whole blocks between frames. Experiments on digitized video data of National Archive of Korea show that the proposed methods outperforms the existing pixel-based and histogram-based methods.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.