• Journal of KIISE:Software and Applications
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• v.30 no.10
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• pp.938-943
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• 2003

In this paper, we Propose a method using the Tikhonov-Miller process to improve the robustness of watermarking under various attacks. A visually recognizable pattern watermark is embedded in the LH2, HL2 and HH2 subband of wavelet transformed domain using threshold and besides watermark is embeded by utilizing HVS(Human Visual System) feature. The pattern watermark was interlaced after random Permutation for a security and an extraction rate. To demonstrate the improvement of robustness and similarity of the proposed method, we applied some basic algorithm of image processing such as scaling, filtering, cropping, histogram equalizing and lossy compression(JPEG, gif). As a result of experiment, the proposed method was able to embed robust watermark invisibility and extract with an excellent normalized correlation of watermark under various attacks.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.171-180
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• 2013

Data security is always an important issue. In particular, the current emerging cloud computing system inevitably raises the issue of data security. However, data security is no longer safe with a simple way, but requires rather advanced method to secure the data. In this paper, instead of exploiting the existing text-based cryptography approach an image-based access control of data content is studied to present a higher level of data security. Color key chain is generated both using histogram value of the original image, and the location information and featured color information extracted by geometric transformation to form the security key to access secure data content. Finally, the paper addresses design interface and implementation for data content access control for evaluation of the proposed scheme.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.923-932
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• 2017

In this paper, we propose a GRNN(: Generalized Regression Neural Network) algorithms for new eyes and face recognition identification system to solve the points that need corrective action in accordance with the existing problems of facial movements gaze upon it difficult to identify the user and. Using a Kalman filter structural information elements of a face feature to determine the authenticity of the face was estimated future location using the location information of the current head and the treatment time is relatively fast horizontal and vertical elements of the face using a histogram analysis the detected. And the light obtained by configuring the infrared illuminator pupil effects in real-time detection of the pupil, the pupil tracking was to extract the text print vector. The abstract is to be in fully-justified italicized text as it is here, below the author information.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.621-625
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• 2010

This paper presents a hand motion tracking method for hand gesture interface using a camera in mobile devices such as a smart phone and PDA. When a camera moves according to the hand gesture of the user, global optical flows are generated. Therefore, robust hand movement estimation is possible by considering dominant optical flow based on histogram analysis of the motion direction. A continuous hand gesture is segmented into unit gestures by motion state estimation using motion phase, which is determined by velocity and acceleration of the estimated hand motion. Feature vectors are extracted during movement states and hand gestures are recognized at the end state of each gesture. Support vector machine (SVM), k-nearest neighborhood classifier, and normal Bayes classifier are used for classification. SVM shows 82% recognition rate for 14 hand gestures.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.163-169
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• 2004

In this paper. we propose a shaded recognition method using symmetric feature. When the existing PCA is applied to shaded face images, the recognition rate is decreased. To improve the recognition rate, we use facial symmetry. If the difference of light and shade is greater than a threshold value, we make a mirror image by replacing the dark side with the bright side symmetrically Then the mirror image is compared with a query image. We compare the performance of the proposed algorithm with the existing algorithms such as PCA, PCA without three eigenfaces and histogram equalization methods. The recognition rate of our method shows $98.889\%$ with the excellent result.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.379-387
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• 2001

We proposed an enhanced extraction method of vehicle plate, in which both the brightness variation of gray and the Hue value of HSI color model were used. For the extraction of the vehicle plate from a vehicle image, first of all, candidate regions for the vehicle plate were extracted from the image by using the property of brightness variation of the image. A real place region was determined among candidate regions by the density of pixels with the Hue value of green and white. For- extracting the feature area containing characters from the extracted vehicle plate, we used the histogram-based approach of individual characters. And we proposed and applied for the recognition of characters the enhanced ART2 algorithm which support the dynamical establishment of the vigilance threshold with the genera]iced union operator of Yager. In addition, we propose an enhanced SOSL algorithm which is integrated both enhanced ART2 and supervised learning methods. The performance evaluation was performed using 100's real vehicle images and the evaluation results demonstrated that the extraction rates of tole proposed extraction method were improved, compared with that of previous methods based un brightness variation, RGB and HSI individually . Furthermore, the recognition rates of the proposed algorithms were improved much more than that of the conventional ART2 and BP algorithms.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.421-429
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• 2015

This paper is about an approach for pedestrian detection and tracking with infrared imagery. We used the CUDA(Computer Unified Device Architecture) that is a parallel processing language in order to improve the speed of video-based pedestrian detection and tracking. The detection phase is performed by Adaboost algorithm based on Haar-like features. Adaboost classifier is trained with datasets generated from infrared images. After detecting the pedestrian with the Adaboost classifier, we proposed a particle filter tracking strategies on HSV histogram feature that exploit adaptively at the same time. The proposed approach is implemented on an NVIDIA Jetson TK1 developer board that is full-featured device ideal for software development within the Linux environment. In this paper, we presented the results of parallel processing with the NVIDIA GPU on the CUDA development environment for detection and tracking of pedestrians. We compared the object detection and tracking processing time for night-time images on both GPU and CPU. The result showed that the detection and tracking speed of the pedestrian with GPU is approximately 6 times faster than that for CPU.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.73-79
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• 2014

In this paper, we proposed the mechanism which can measure traffic flow and vehicle speed on the highway as well as road by using the video and image processing to detect and track cars in a video sequence. The proposed mechanism uses the first few frames of the video stream to estimate the background image. The visual tracking system is a simple algorithm based on the sum of absolute frame difference. It subtracts the background from each video frame to produce foreground images. By thresholding and performing morphological closing on each foreground image, the proposed mechanism produces binary feature images, which are shown in the threshold window. By measuring the distance between the "first white line" mark and the "second white line"mark proceeding, it is possible to find the car's position. Average velocity is defined as the change in position of an object divided by the time over which the change takes place. The results of proposed mechanism agree well with the measured data, and view the results in real time.

• The Journal of the Korea Contents Association
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• v.2 no.3
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• pp.1-15
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• 2002

A noel automated brain region extraction method in single channel MR images for visualization and analysis of a human brain is presented. The method generates a volume of brain masks by automatic thresholding using a dual curve fitting technique and by 3D morphological operations. The dual curve fitting can reduce an error in clue fitting to the histogram of MR images. The 3D morphological operations, including erosion, labeling of connected-components, max-feature operation, and dilation, are applied to the cubic volume of masks reconstructed from the thresholded Drain masks. This method can automatically extract a brain region in any displayed type of sequences, including extreme slices, of SPGR, T1-, T2-, and PD-weighted MR image data sets which are not required to contain the entire brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 of similarity index in comparison with manual drawing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1807-1823
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• 2016

Local Binary Pattern (LBP) and its variants have powerful discriminative capabilities but most of them just consider each LBP code independently. In this paper, we propose sub oriented histograms of LBP for smoke detection and image classification. We first extract LBP codes from an image, compute the gradient of LBP codes, and then calculate sub oriented histograms to capture spatial relations of LBP codes. Since an LBP code is just a label without any numerical meaning, we use Hamming distance to estimate the gradient of LBP codes instead of Euclidean distance. We propose to use two coordinates systems to compute two orientations, which are quantized into discrete bins. For each pair of the two discrete orientations, we generate a sub LBP code map from the original LBP code map, and compute sub oriented histograms for all sub LBP code maps. Finally, all the sub oriented histograms are concatenated together to form a robust feature vector, which is input into SVM for training and classifying. Experiments show that our approach not only has better performance than existing methods in smoke detection, but also has good performance in texture classification.