• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.49-59
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• 2009

In this paper a watermarking algorithm is proposed to optimize the capacity of the digital watermark insertion in an experimental threshold using the characteristics of human visual system(HVS), adaptive scale factors, and weight functions based on discrete wavelet transform. After the original image is decomposed by a 3-level discrete wavelet transform, the watermarks for capacity optimization are inserted into all subbands except the baseband, by applying the important coefficients from the experimental threshold in the wavelet region. The adaptive scale factors and weight functions based on HVS are considered for the capacity optimization of the digital watermark insertion in order to enhance the robustness and invisibility. The watermarks are consisted of gaussian random sequences and detected by correlation. The experimental results showed that this algorithm can preserve a fine image quality against various attacks such as the JPEG lossy compression, noise addition, cropping, blurring, sharpening, linear and non-linear filtering, etc.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.787-795
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• 2014

This paper presents an image registration using Triangulation-based Local Transformation (TLT) applied to the remaining matched points after elimination of the matched points with gross error. The corners extracted using geometric mean-based corner detector are matched using Pearson's correlation coefficient and then accepted as initial matched points only when they satisfy the Left-Right Consistency (LRC) check. We finally accept the remaining matched points whose RANdom SAmple Consensus (RANSAC)-based global transformation (RGT) errors are smaller than a predefined outlier threshold. After Delaunay triangulated irregular networks (TINs) are created using the final matched points on reference and sensed images, respectively, affine transformation is applied to every corresponding triangle and then all the inner pixels of the triangles on the sensed image are transformed to the reference image coordinate. The proposed algorithm was tested using KOMPSAT-2 images and the results showed higher image registration accuracy than the RANSAC-based global transformation.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.367-376
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• 2009

In this paper, we propose a flame detection method using Haar wavelet and moving averages in outdoor infrared video sequences. Our proposed method is composed of three steps which are Haar wavelet decomposition, flame candidates detection, and their tracking and flame classification. In Haar wavelet decomposition, each frame is decomposed into 4 sub- images(LL, LH, HL, HH), and also computed high frequency energy components using LH, HL, and HH. In flame candidates detection, we compute a binary image by thresholding in LL sub-image and apply morphology operations to the binary image to remove noises. After finding initial boundaries, final candidate regions are extracted using expanding initial boundary regions to their neighborhoods. In tracking and flame classification, features of region size and high frequency energy are calculated from candidate regions and tracked using queues, and we classify whether the tracked regions are flames by temporal changes of moving averages.

• The Journal of the Korea Contents Association
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• v.8 no.9
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• pp.1-9
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• 2008

In this paper, we proposed automatic face detection and tracking which is robustness in rotation. To detect a face image in complicated background and various illuminating conditions, we used face skin color detection. we used Harris corner detector for extract facial feature points. After that, we need to track these feature points. In traditional method, Lucas-Kanade feature tracker doesn't delete useless feature points by occlusion in current scene (face rotation or out of camera). So we proposed the estimation function, which delete useless feature points. The method of delete useless feature points is estimation value at each pyramidal level. When the face was occlusion, we deleted these feature points. This can be robustness to face rotation and out of camera. In experimental results, we assess that using estimation function is better than traditional feature tracker.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.823-826
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• 2016

This paper proposes a method for detecting automatically a smartphone camera in order to put a security stickers and detecting security sickers to recognize whether the removable security sticker or not. The method reduce the time that a person put a security stickers and check the stickers whether the removable stickers at rush hour. It is significantly low to detect rate if the image quality is poor low by LED light flickering phenomenon at the time of the camera detection of the smartphone. Therefor, in this paper, it implement the method that detecting camera to inspect frequently degree after applying the adaptive threshold. Further, in order to recognize whether the removable security sticker or not implement the sticker region detection method using a color filter. The proposed method could increase detecting ratio than earlier methods with LED light flickering phenomenon. In addition, it was also high security sticker detection ratio.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1342-1348
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• 2017

In an era where digital data takes on great importance, images are essential to various media. Noise is generated during the acquisition and transmission of such images, caused by a number of external factors. The removal of noise is an essential step in image processing. There are various methods used to remove noise, in accordance with the cause or form of the noise. AWGN is one of the leading methods. As such, this paper applies the edge detection method using the mean of each pixel after categorizing in detail the partial masks into nine areas as part of the preliminary process, in order to minimize noise that had been added to the image. In addition, the paper suggests an algorithm that applies different filters to the partial masks by using the critical mass value of the transfigured edge detection. To verify the competence of the suggested algorithm, it was compared with existing methods by using magnified images and PSNR(peak signal to noise ratio).

• 전자공학회논문지 IE
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• v.45 no.3
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• pp.27-35
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• 2008

In this paper, we propose a minute signal detection algorithm for a development of optical analyzer, using the non-dispersive infrared method with multi gas filter correlation wheel, that can measure various environmental air-pollution materials (CO, SO2, NOx, etc.) in real-time. The MCT(mercury cadmium telluride) sensor can detect minute signals those show and absorption characteristic of each environmental pollution materials. In the proposed method, a corresponding data of each environmental pollution materials can be separated by an external trigger and threshold values in the measured continuous signals.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.713-722
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• 2013

This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.51-60
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• 2003

The watermarking is the technique that embeds or extracts the certain data without the change of the original data for the copyright protection of the multimedia contents. Watermark-embedded contents must not be distinguished by human's eye and must be robust to the various image processing and the intentional distortions. In this paper, we propose a new watermarking technique applied in the wavelet domain which has both the spatial and frequency information of a image. For both the robustness and the invisibility, the positions for embedding the watermark is selected with the multi-threshold. We search the similarity between highly correlated coefficients in the each subband and decide the mark space after verifying the significance in the specified subband. The similarity is represented by the coefficient difference between the subbands and its distribution is used in the watermark embedding and extracting. The embedded watermark can be extracted without the original image using the relationship of the subbands. By these properties the proposed watermarking algorithm has the invisibility and the robustness to the attacks such as JPEG compression and the general image processing.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.47-52
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• 2011

We studied about the damaged tire decision algorithm that measured dominant period of ultrasonic signal due to a foreign material on the friction between tire and road surface. We computed the power spectrum about the envelope of ultrasonic signal acquired from the damaged tire, then proposed the dominant period decision algorithm by statistical power threshold value. As the result of simulation, when driving by the speed of 80km/h, the 100ms of dominant period that measured in the proposed algorithm is more accurate than the 97.6ms of power spectrum peak period referenced on the average period of ultrasonic signal envelope peak, 101.24ms.