• Journal of the Korea Computer Industry Society
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• v.3 no.7
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• pp.871-880
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• 2002

In this paper, I proposed the adaptive watermarking based on the discrete wavelet transform for the very robust watermarking against intentional distortion which attacks on image. A watermark is an binary image such as document(sign), and such watermark image is embedded in the LH and HL band of wavelet transformed domain for copyright protection of image data. To evaluate robustness of the proposed method, we applied some basic algorithm of image processing such as brightening, darkening, sharpening, blurring, cropping, histogram equalizing and lossy compression(JPEG, gif). As a result of experiment, the proposed method has the good image quality and the high perceptibility of watermark. It was demonstrated by experiments that the proposed algorithm can provide an excellent protection under various attacks.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1008-1018
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• 2015

In this paper, we propose a method that constructs a saliency map in which important regions are accurately specified and the colors of the regions are less influenced by the similar surrounding colors. Our method utilizes LBP(Local Binary Pattern) histogram information to compare and analyze texture information of surrounding regions in order to reduce the effect of color information. We extract the saliency of stereoscopic images by integrating a 2D saliency map with depth information of stereoscopic images. We then measure the distance between two different sizes of the LBP histograms that are generated from pixels. The distance we measure is texture difference between the surrounding regions. We then assign a saliency value according to the distance in LBP histogram. To evaluate our experimental results, we measure the F-measure compared to ground-truth by thresholding a saliency map at 0.8. The average F-Measure is 0.65 and our experimental results show improved performance in comparison with existing other saliency map extraction methods.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.3
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• pp.113-120
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• 2003

Plasma display panels normally utilize the binary coded light emission scheme for gray scale expression. Subsequently, this expression method makes dynamic false contours. We propose the "E3DSM(enhanced 3-dimension scattering method)" that improved existing 3-d scattering method and the "HAM(histogram analysis method)" that is decided the driving schemes and subfield selections with histograms of images. Simulation results show the improving image quality.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.111-120
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• 2016

Wire recognition is one of the important tasks in chip reverse engineering since connectivity comes from wires. Recognized wires are used to recover logical or functional representation of the corresponding circuit. Though manual recognition provides accurate results, it becomes impossible, as the number of wires is more than hundreds of thousands. Wires on a chip usually have specific intensity or color characteristics since they are made of specific materials. This paper proposes two stage wire recognition scheme; image binarization and then the process of determining whether regions in binary image are wires or not. We employ existing techniques for two processes. Since the second process requires the characteristics of wires, the users needs to select the typical wire region in the given image. The histogram characteristic of the selected region is used in calculating histogram similarity between the typical wire region and the other regions. The first experiment is to select the most appropriate binarization scheme for the second process. The second experiment on the second process compares three proposed methods employing histogram similarity of grayscale or HSV color since there have not been proposed any wire recognition method comparable by experiment. The best method shows more than 98% of true positive rate for 25 test examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2893-2912
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• 2013

The design of geometrically invariant watermarking is one of the most challenging work in digital image watermarking research area. To achieve the robustness to geometrical attacks, the inherent characteristic of an image is usually used. In this paper, a geometrically invariant image watermarking scheme using connected objects and gravity center is proposed. First, the gray-scale image is converted into the binary one, and the connected objects according to the connectedness of binary image are obtained, then the coordinates of these connected objects are mapped to the gray-scale image, and the gravity centers of those bigger objects are chosen as the feature points for watermark embedding. After that, the line between each gravity center and the center of the whole image is rotated an angle to form a sector, and finally the same version of watermark is embedded into these sectors. Because the image connectedness is topologically invariant to geometrical attacks such as scaling and rotation, and the gravity center of the connected object as feature points is very stable, the watermark synchronization is realized successfully under the geometrical distortion. The proposed scheme can extract the watermark information without using the original image or template. The simulation results show the proposed scheme has a good invisibility for watermarking application, and stronger robustness than previous feature-based watermarking schemes against geometrical attacks such as rotation, scaling and cropping, and can also resist common image processing operations including JPEG compression, adding noise, median filtering, and histogram equalization, etc.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.377-380
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• 2010

This paper proposes texture descriptor for texture classification called Local Neighbor Differences (LND). LND is a high discriminating texture descriptor and also robust to illumination changes. The proposed descriptor utilizes the sign of differences between surrounding pixels in a local neighborhood. The differences of those pixels are thresholded to form an 8-bit binary codeword. The decimal values of these 8-bit code words are computed and they are called LND values. A histogram of the resulting LND values is created and used as feature to describe the texture information of an image. Experimental results, with respect to texture classification accuracies using OUTEX_TC_00001 test suite has been performed. The results show that LND outperforms LBP method, with average classification accuracies of 92.3% whereas that of local binary patterns (LBP) is 90.7%.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.250-257
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• 2011

In this paper, we proposed the method detecting multiple pedestrians using motion information and SVM(Support Vector Machine) from a moving camera image. First, we detect moving pedestrians from both the difference image and the projection histogram which is compensated for the camera ego-motion using corresponding feature sets. The difference image is simple method but it is not detected motionless pedestrians. Thus, to fix up this problem, we detect motionless pedestrians using SVM The SVM works well particularly in binary classification problem such as pedestrian detection. However, it is not detected in case that the pedestrians are adjacent or they move arms and legs excessively in the image. Therefore, in this paper, we proposed the method detecting motionless and adjacent pedestrians as well as people who take excessive action in the image using motion information and SVM The experimental results on our various test video sequences demonstrated the high efficiency of our approach as it had shown an average detection ratio of 94% and False Positive of 2.8%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3534-3549
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• 2020

The traditional method of smoke image recognition has low accuracy. For this reason, we proposed an algorithm based on the good group of IMFOA which is GMFOA to optimize the parameters of SVM. Firstly, we divide the motion region by combining the three-frame difference algorithm and the ViBe algorithm. Then, we divide it into several parts and extract the histogram of oriented gradient and volume local binary patterns of each part. Finally, we use the GMFOA to optimize the parameters of SVM and multiple kernel learning algorithms to Classify smoke images. The experimental results show that the classification ability of our method is better than other methods, and it can better adapt to the complex environmental conditions.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.227-230
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• 2006

A content-based image retrieval system using MPEG-7 is designed and implemented in this thesis. The implemented system uses existing MPEG-7 Visual Descriptors. In addition, a new descriptor for efficient retrieval of bio images is proposed and utilized in the developed content-based image retrieval system. Comparing proposed CBSD(Compact Binary Shape Descriptor) with Edge Histogram Descriptor(EHD) and Region Shape Descriptor(RSD), it shows good retrieval performance in NMRR. The proposed descriptor is robust to large modification of brightness and contrast and especially improved retrieval performance to search images with similar shapes. Also proposed system adopts distributed architecture to solve increased server overload and network delay. Updating module of client efficiently reduces downloading time for metadata. The developed system can efficiently retrieve images without causing server's overload.