• Journal of Broadcast Engineering
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• v.18 no.1
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• pp.77-87
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• 2013

In this paper, we present a novel method for local image descriptor called exact order based descriptor (EOD) which is robust to illumination changes and Gaussian noise. Exact orders of image patch is induced by changing discrete intensity value into k-dimensional continuous vector to resolve the ambiguity of ordering for same intensity pixel value. EOD is generated from overall distribution of exact orders in the patch. The proposed local descriptor is compared with several state-of-the-art descriptors over a number of images. Experimental results show that the proposed method outperforms many state-of-the-art descriptors in the presence of illumination changes, blur and viewpoint change. Also, the proposed method can be used for many computer vision applications such as face recognition, texture recognition and image analysis.

• Journal of Korea Technology Innovation Society
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• v.15 no.3
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• pp.604-626
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• 2012

As a society of knowledge and information has been developed rapidly, because of changing from web environment to ubiquitous environment, a lot of countries across the world as well as S. Korea for e-Government have come to use the internet electronic voting for a variety of elections. So this research focused on the strategy consulting of the internet electronic voting of S. Korea with spatial information system analysed by the application of 'scenario planning' as a foresight method. And as a consequence, the strategy formulation of the electronic voting for the future S. Korea is to use the biometrics technology system as vein recognition and face recognition, using a part of the human body like a password, with spatial information system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.144-150
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• 2017

Recently, deep learning is used for intelligent processing and accuracy improvement of data. It is formed calculation model composed of multi data processing layer that train the data representation through an abstraction of the various levels. A category of deep learning, convolution neural network is utilized in various research fields, which are human pose estimation, face recognition, image classification, speech recognition. When using the deep layer and lots of class, CNN that show a good performance on image classification obtain higher classification rate but occur the overfitting problem, when using a few data. So, we design the training network based on convolution neural network and trained our image data set for object classification in few class problem. The experiment show the higher classification rate of 7.06% in average than the previous networks designed to classify the object in 1000 class problem.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.62-78
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• 2002

In this paper, we describe a system that can interact with agents in the virtual space incorporated in the system. This system is constructed by an analysis system for analyzing human gesture and an interact system for interacting with agents in the virtual space using analyzed information. An implemented analysis system for analyzing gesture extracts a head and hands region after taking image sequence of an operator's continuous behavior using CCD cameras. In interact system, we construct the virtual space that exist an avatar which incarnating operator himself, an autonomous object (like a Puppy), and non-autonomous objects which are table, door, window and object. Recognized gesture is transmitted to the avatar in the virtual space, then transit to next state based on state transition diagram. State transition diagram is represented in a graph in which each state represented as node and connect with link. In the virtual space, the agent link an avatar can open and close a window and a door, grab or move an object like a ball, order a puppy to do and respond to the Puppy's behavior as does the puppy.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.59-65
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• 2019

Hospital Information System includes a wide range of information in the medical profession, from the overall administrative work of the hospital to the medical work of doctors. In this paper, we proposed a Vision-based Authentication and Registration of Facial Identity in Hospital Information System using OpenCV. By using the proposed security module program a Vision-based Authentication and Registration of Facial Identity, the hospital information system was designed to enhance the security through registration of the face in the hospital personnel and to process the receipt, treatment, and prescription process without any secondary leakage of personal information. The implemented security module program eliminates the need for printing, exposing and recognizing the existing sticker paper tags and wristband type personal information that can be checked by the nurse in the hospital information system. In contrast to the original, the security module program is inputted with ID and password instead to improve privacy and recognition rate.

• Journal of the Korea Society of Computer and Information
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• v.15 no.3
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• pp.27-35
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• 2010

Accurately extracting skin color regions is very important in various areas such as face recognition and tracking, facial expression recognition, adult image identification, health-care, and so forth. In this paper, we evaluate the performances of several skin color detection algorithms in indoor environments by changing the distance between the camera and the object as well as the background colors of the object. The distance is from 60cm to 120cm and the background colors are white, black, orange, pink, and yellow, respectively. The algorithms that we use for the performance evaluation are Peer algorithm, NNYUV, NNHSV, LutYUV, and Kimset algorithm. The experimental results show that NNHSV, NNYUV and LutYUV algorithm are stable, but the other algorithms are somewhat sensitive to the changes of backgrounds. As a result, we expect that the comparative experimental results of this paper will be used very effectively when developing a new skin color extraction algorithm which are very robust to dynamic real environments.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.249-254
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• 2013

The number of channels are increased into several hundreds of channels when coming out the digital broadcasting era. In this environment, viewers searching for programs will be very difficult to do. In addition, recent popularization of smart devices are receiving the services that they previously had not been given to. A TV program recommended a system that has been studied as a way to solve these problems. However, most studies have been studied in most web-based research results when applied to broadcast TV for TV program recommendations. In particular, the combination of the current members who watch TV are not considered. In this paper, the environment and TV viewers are considering a combination of the members of the TV program's recommended system proposal. In order to make a group deal successful, we employ the face recognition.

• Journal of Convergence for Information Technology
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• v.7 no.2
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• pp.31-36
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• 2017

In this paper, we try to search and reproduce the image part of a specific cast from a large number of images. The conventional method must manually set the offset value when searching for a scene or viewing a corner. However, in this paper, the proposed method learns the main character 's face, then finds the main character in the image recognition and moves to the scene where the main character appears to reproduce the image. Data for specific performers is extracted and collected using crawl techniques. Based on the collected data, we learn using convolutional neural network algorithm and perform performance evaluation using it. The performance evaluation measures the accuracy by extracting and judging a specific performer learned in the extracted key frame while playing the drama. The performance confirmation of how quickly and accurately the learned scene is searched has obtained about 93% accuracy. Based on the derived performance, it is applied to the image service such as viewing, searching for person and detailed information retrieval per corner

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.103-111
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• 2023

Recently, many universities have used various electronic attendance systems such as NFC, QR code, Sound-QR, Bluetooth BLE authentication, and face recognition to process attendance. However, existing methods have various problems such as attendance errors due to deformation of authentication signals, mis-recognition attendance from outside the classroom, and difficulty to process seat absence during class. Therefore, this study proposes a high-frequency signal-based electronic attendance system to solve these problems and manage more accurate electronic attendance. As the high-frequency signal replaces the BLE signal, and the transmission range of the signal is limited to the classroom, and the signal value can be immediately changed if a change of the signal is needed. To verify the performance of the proposed system, we conducted a comparative experiment with the Bluetooth based electronic attendance system, and as a result, the proposed method showed high accuracy. Thus, the proposed method will be a useful service that can be immediately used in smart device-based electronic attendance system.

• Journal of Biosystems Engineering
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• v.29 no.4
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• pp.341-346
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• 2004

Recent livestock people concern not only increase of production, but also superior quality of animal-breeding environment. So far, the optimization of the breeding and air environment has been focused on the production increase. In the very near future, the optimization will be emphasized on the environment for the animal welfare and health. Especially, cattle farming demands the precision livestock farming and special attention has to be given to the management of feeding, animal health and fertility. The management of individual animal is the first step for precision livestock farming and animal welfare, and recognizing each individual is important for that. Though electronic identification of a cattle such as RFID(Radio Frequency Identification) has many advantages, RFID implementations practically involve several problems such as the reading speed and distance. In that sense, computer vision might be more effective than RFID for the identification of an individual animal. The researches on the identification of cattle via image processing were mostly performed with the cows having black-white patterns of the Holstein. But, the native Korean and Japanese cattle do not have any definite pattern on the body. The purpose of this research is to identify the Japanese black cattle that does not have a body pattern using computer vision technology and neural network algorithm. Twelve heads of Japanese black cattle have been tested to verify the proposed scheme. The values of input parameters were specified and then computed using the face images of cattle. The images of cattle faces were trained using associate neural network algorithm, and the algorithm was verified by the face images that were transformed using brightness, distortion, and noise factors. As a result, there was difference due to transform ratio of the brightness, distortion, and noise. And, the proposed algorithm could identify 100% in the range from -3 to +3 degrees of the brightness, from -2 to +4 degrees of the distortion, and from 0% to 60% of the noise transformed images. It is concluded that our system can not be applied in real time recognition of the moving cows, but can be used for the cattle being at a standstill.