• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.563-565
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• 2021

The most important feature of the recently increasing content providing service is that the amount of content increase over time is very large. Accordingly, the importance of user curation is increasing, and various techniques are used to implement it. In this paper, among the techniques for video recommendation, the analysis technique using voice data and subtitles and the video comparison technique based on keyframe extraction are compared with the results of implementing and applying the video content of real big data. In addition, through the comparison result, a video content environment to which each analysis technique can be applied is proposed.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.395-403
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• 2016

Recently, Image processing has been used in many areas. In the image processing techniques that a lot of research is tracking of moving object in real time. There are a number of popular methods for tracking an object such as HOG(Histogram of Oriented Gradients) to track pedestrians, and Codebook to subtract background. However, object extraction has difficulty because that a moving object has dynamic background in the image, and occurs severe lighting changes. In this paper, we propose a method of object extraction using depth image and color image features based on ROI(Region of Interest). First of all, we look for the feature points using the color image after setting the ROI a range to find the location of object in depth image. And we are extracting an object by creating a new contour using the convex hull point of object and the feature points. Finally, we compare the proposed method with the existing methods to find out how accurate extracting the object is.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.401-408
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• 2006

In this paper, we propose a new class of segmentation technique for feature extraction based on the statistical and regional classification at each vertical or horizontal line of digital image data. Data is processed and clustered at each line, different from the point or space process. They are designed to segment gray-scale sectional images using a horizontal and vertical line process due to their statistical and property differences, and to extract the feature. The techniques presented here show efficient results in case of the gray level overlap and not having threshold image. Such images are also not easy to be segmented by the global or local threshold methods. Line pixels inform us the sectionable data, and can be set according to cluster quality due to the differences of histogram and statistical data. The total segmentation on line clusters can be obtained by adaptive extension onto the horizontal axis. Each processed region has its own pixel value, resulting in feature extraction. The advantage and effectiveness of the line-cluster approach are both shown theoretically and demonstrated through the region-segmental carotid artery medical image processing.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.853-867
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• 2010

Recently, thumbnail techniques are required to index a high-performance video at digital convergence-based multimedia service like IPTV and DMB. Therefore a thumbnail extraction method in H.264/AVC bitstreams has been proposed. However, thumbnail quality deterioration problem at converting the general equation of spatial domain to frequency domain which is generated by not considering about H.264/AVC transform and quantization processing and rounding-off operation in intra prediction. In this paper, we propose a new thumbnail extraction method in H.264/AVC bitstreams. The proposed scheme is based on H.264/AVC core-transform for a thumbnail extraction in frequency domain, and probability theory, intra rounding-off error compensation. Through the implementation and performance evaluation, the subjective quality difference between the output of our scheme and the output of reference decoder is negligible and better than the conventional method, and moreover PSNR gain by up to 8.66 dB.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2E
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• pp.26-31
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• 1994

A new feature extraction technique utilizing strange attractor and artificial neural network for speaker recognition is presented. Since many signals change their characteristics over long periods of time, simple time-domain processing techniques should e capable of providing useful information of signal features. In many cases, normal time series can be viewed as a dynamical system with a low-dimensional attractor that can be reconstructed from the time series using time delay. The reconstruction of strange attractor is described. In the technique, the raw signal will be reproduced into a geometric three dimensional attractor. Classification decision for speaker recognition is based upon the processing or sets of feature vectors that are derived from the attractor. Three different methods for feature extraction will be discussed. The methods include box-counting dimension, natural measure with regular hexahedron and plank-type box. An artificial neural network is designed for training the feature data generated by the method. The recognition rates are about 82%-96% depending on the extraction method.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.879-886
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• 2006

The analytical methods of polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans (PCDD/Fs) for water sample with extremely low concentration was developed to extract large volume and to improve fast chromatography for clean up. Semi-automated solid phase extraction (SPE) system and column coupling chromatography using elution pump was optimized and applied to each processes of treatment plant. Results of disk type SPE indicated that this system was applicable below 40 L of aqueous sample with a flow rate of 0.08 ~ 0.2 L/min. Average recoveries of SPE using labeled sampling spike of $^{37}Cl$-2,3,7,8-TCDD was 97%. Column coupling method resulted in reduced clean up time, solvent volume, increased average recoveries with constant elution rate. The combined methods were applied to the monitoring of drinking water treatment plant. Limits of detection (LOD) of each process were calculated. For example, LOD of raw and treated water ranged 0.094~0.968 pg/L and 0.028~0.364 pg/L, respectively. Combined methods of extraction and cleanup techniques provided fast analysis of PCDD/Fs with high accuracy and low LODs for water samples.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.2
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• pp.287-304
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• 2005

The goal of this research is to evaluate the usability of visually browsing the automatically extracted information. A domain-independent information extraction system was used to extract information from news type texts to populate the visually browasable knowledge base. The information extraction system automatically generated Concept-Relation-Concept triples by applying various Natural Language Processing techniques to the text portion of the news articles. To visualize the information stored in the knowledge base, we used PersoanlBrain to develop a visualization portion of the user interface. PersonalBrain is a hyperbolic information visualization system, which enables the users to link information into a network of logical associations. To understand the usability of the visually browsable knowledge base, IS test subjects were observed while they use the visual interface and also interviewed afterward. By applying a qualitative test data analysis method. a number of usability Problems and further research directions were identified.

• Preventive Nutrition and Food Science
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• v.18 no.2
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• pp.133-138
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• 2013

This study was performed to compare the quality and functionality of broccoli juice as affected by extraction method. Broccoli juice was extracted using method I (NUC Kuvings silent juicer), method II (NUC centrifugal juicer), and method III (NUC mixer), and the quality properties of the broccoli juices were analyzed using three different methods. Additionally, the antioxidative, anticancer, and anti-hyperglycemic activities of broccoli juice prepared by the three different methods were investigated in vitro. The broccoli juice made by method I contained the highest polyphenol and flavonoid contents at 1,226.24 mg/L and 1,018.32 mg/L, respectively. Particularly, broccoli juice prepared by method I showed higher DPPH and ABTS radical scavenging activities than those of the other samples. Additionally, broccoli juice made by method I showed the highest growth inhibitory effects against HeLa, A549, AGS, and HT-29 cancer cells. Broccoli juice prepared by method I had the highest ${\alpha}$-glucosidase inhibitory effects. These results indicate that there are important differences in chemical and functional qualities between juice extraction techniques.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.613-620
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• 2008

Noun extraction plays an important part in the fields of information retrieval, text summarization, and so on. In this paper, we present a Korean base-noun extraction system and apply it to text summarization to deal with a huge amount of text effectively. The base-noun is an atomic noun but not a compound noun and we use tow techniques, filtering and segmenting. The filtering technique is used for removing non-nominal words from text before extracting base-nouns and the segmenting technique is employed for separating a particle from a nominal and for dividing a compound noun into base-nouns. We have shown that both of the recall and the precision of the proposed system are about 89% on the average under experimental conditions of ETRI corpus. The proposed system has applied to Korean text summarization system and is shown satisfactory results.