• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.43-53
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• 2018

As face recognition (FR) has been well studied over the past decades, FR technology has been applied to many real-world applications such as surveillance and biometric systems. However, in the real-world scenarios, FR performances have been known to be significantly degraded owing to variations in face images, such as the pose, illumination, and low-resolution. Recently, visual intelligence technology has been rapidly growing owing to advances in deep learning, which has also improved the FR performance. Furthermore, the FR performance based on deep learning has been reported to surpass the performance level of human perception. In this article, we discuss deep-learning based high-performance FR technologies in terms of representative deep-learning based FR architectures and recent FR algorithms robust to face image variations (i.e., pose-robust FR, illumination-robust FR, and video FR). In addition, we investigate big face image datasets widely adopted for performance evaluations of the most recent deep-learning based FR algorithms.

• Journal of Korean Society of Archives and Records Management
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• v.22 no.4
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• pp.129-149
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• 2022

Records have temporal characteristics, including the past and present; linguistic characteristics not limited to a specific language; and various types categorized in a complex way. Processing records such as text, video, and audio in the life cycle of records' creation, preservation, and utilization entails exhaustive effort and cost. Primary natural language processing (NLP) technologies, such as machine translation, document summarization, named-entity recognition, and image recognition, can be widely applied to electronic records and analog digitization. In particular, Korean deep learning-based NLP technologies effectively recognize various record types and generate record management metadata. This paper provides an overview of Korean NLP technologies and discusses considerations for applying NLP technology in records management. The process of using NLP technologies, such as machine translation and optical character recognition for digital conversion of records, is introduced as an example implemented in the Python environment. In contrast, a plan to improve environmental factors and record digitization guidelines for applying NLP technology in the records management field is proposed for utilizing NLP technology.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.58-63
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• 2022

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

• Convergence Security Journal
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• v.12 no.6
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• pp.31-37
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• 2012

Today, the core technology of an automobile is becoming to IT-based convergence system technology. To cope with many kinds of situations and provide the convenience for drivers, various IT technologies are being integrated into automobile system. In this paper, we propose an convergence system, which is called Augmented Driving System (ADS), to provide high safety and convenience of drivers based on image information processing. From imaging sensor, the image data is acquisited and processed to give distance from the front car, lane, and traffic sign panel by the proposed methods. Also, a converged interface technology with camera for gesture recognition and microphone for speech recognition is provided. Based on this kind of system technology, car accident will be decreased although drivers could not recognize the dangerous situations, since the system can recognize situation or user context to give attention to the front view. Through the experiments, the proposed methods achieved over 90% of recognition in terms of traffic sign detection, lane detection, and distance measure from the front car.

• Korean Institute of Interior Design Journal
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• v.14 no.2 s.49
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• pp.217-224
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• 2005

The lack of objective decisive criteria and the absence of analyzing tools accrued from the experiments on various types developed from space design process makes it difficult to select and execute alternatives for them. As an attempt of coping with these problems, the aims of this study is to establish space analysis' models and to propose possibility of analyzing models by utilizing the technology of image process. It is now under study in the field of artificial intelligence based on the accomplishment of digital images. This study focused on establishment an analysis model based on accomplished digital images and image processing framework. It helps utilize various processing technologies that are currently in use of image processes, and problems of the study can be supplemented through further follow-up studies. Finally, analysis model can be constructed gradually huge design data in the analogue data to the digital image database and be proposed with index in design or evaluation step.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.497-502
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• 2016

Shape Contexts Recognition(SCR) is a technology recognizing shapes such as figures and objects, greatly supporting technologies such as character recognition, motion recognition, facial recognition, and situational recognition. However, generally SCR makes histograms for all contours and maps the extracted contours one to one to compare Shape A and B, which leads to slow progress speed. Thus, this paper has made simple yet more effective algorithm with optimized contour, finding the outlines according to shape figures and using the improved Douglas-Peucker algorithm and Harris corner detector. With this improved method, progress speed is recognized as faster.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.3
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• pp.473-482
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• 2017

Hand gesture recognition is regarded as new Human Computer Interaction (HCI) technologies for the next generation of mobile devices. Previous hand gesture implementation requires a large memory and computation power for hand segmentation, which fails to give real-time interaction with mobile devices to users. Therefore, in this paper, we presents a low latency and memory-efficient hand segmentation architecture for natural hand gesture recognition. To obtain both high memory-efficiency and low latency, we propose a streaming hand contour tracing unit and a fast contour filling unit. As a result, it achieves 7.14 ms latency with only 34.8 KB on-chip memory, which are 1.65 times less latency and 1.68 times less on-chip memory, respectively, compare to the best-in-class.

• Journal of Information Display
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• v.11 no.4
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• pp.169-172
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• 2010

This paper proposes a multi-touch detection technology that can be applied to large LCDs. To achieve this goal, a set of IR LEDs and sensors was used to construct an IR matrix, and a new algorithm based on Hough transform was applied. This approach reduced the "Ghost" response of the multi-touch detection technology to make it better than other IR touch recognition technologies, and showed robust performance in terms of multi-touch recognition.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.69-72
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• 2005

As life style becomes to be complicated, lots of support technologies were developed. The bar code technology is one of them. It was renovating approach to goods industry. However, data storage ability in one dimension bar code came in limit because of industry growth. Two-dimension bar code was proposed to overcome one-dimension bar code. PDF417 bar code most commonly used in standard two-dimension bar codes is well defined at data decoding and error correction area. More works could be done in bar code image acquisition process. Applying various image enhancement algorithms, the recognition rate of PDF417 bar code is improved.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.99-106
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• 2019

In traditional Chinese medical (TCM) science, tongue images can be observed for medical diagnosis; however, the tongue diagnosis of TCM is influenced by the subjective factors of doctors, and the diagnosis results vary from person to person. Quantitative TCM tongue diagnosis can improve the accuracy of diagnosis and increase the application value. In this paper, digital image processing and pattern recognition technologies are employed on mobile device to classify tongue images collected in different health states. First, through grayscale integral projection processing, the trough is found to localize the tongue body. Then the tongue body image is transferred from RGB color space to HSV color space, and the average H and S values are considered as the color features. Finally, the diagnosis results are obtained according to the relationship between the color characteristics and physical symptoms.