• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5446-5451
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• 2014

Because there are some mistakes by hand in processing electronic maps using a navigation terminal, this paper proposes an automatic offline recognition for traffic signs, which are considered ingredient navigation information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which have been used widely in the field of 2D face recognition as computer vision and pattern recognition applications, was used to recognize traffic signs. First, using PCA, a high-dimensional 2D image data was projected to a low-dimensional feature vector. The LDA maximized the between scatter matrix and minimized the within scatter matrix using the low-dimensional feature vector obtained from PCA. The extracted traffic signs under a real-world road environment were recognized successfully with a 92.3% recognition rate using the 40 feature vectors created by the proposed algorithm.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.76-87
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• 1998

This paper deals with a system which recognizes dynmic hand gestures, Korean Sign Language(KSL), mixed with static hand gesture, Korean Manual Alphabet(KMA), continuously. Recognition of continuous hand gestures is very difficult for lack of explicit tokens indicating beginning and ending of signs and for complexity of each gesture. In this paper, state automata is used for segmenting sequential signs into individual ones, and basic elements of KSL and KMA, which consist of 14 hand directions, 23 hand postures and 14 hand orientations are used for recognition of complex gestures under consideration of expandability. Using a pair of CyberGlove and Polhemus sensor, this system recognizes 131 Korean signs and 31 KMA's in real-time with recognition rate 94.3% for KSL excluding no recognition case and 96.7% for KMA.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.149-158
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• 2004

This study proposes a method for segmentation and recognition of traffic signs using shape information and edge image in real image. It first segments traffic sign candidate regions by connected component algorithm from binary images, obtained by utilizing the RGB color ratio of each pixel in the image, and then extracts actual traffic signs based on their symmetries on X- and Y-axes. Histogram equalization is performed for unsegmented candidate regions caused by low contrast in the image. In the recognition stage, it utilizes shape information including projection profiles on X- and Y-axes, moment, and the number of crossings and distance which concentric circular patterns and 8-directional rays from region center intersects with edges of traffic signs. It finally performs recognition by measuring similarity with the templates in the database. It will be shown from several experimental results that the system is robust to environmental factors, such as light and weather condition.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.291-298
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• 2020

Sign language was designed for the deaf and dumb people to allow them to communicate with others and connect to the society. However, sign language is uncommon to the rest of the society. The unresolved communication barrier had eventually isolated deaf and dumb people from the society. Hence, this study focused on design and implementation of a wearable sign language interpreter. 6 inertial measurement unit (IMU) were placed on back of hand palm and each fingertips to capture hand and finger movements and orientations. Total of 28 proposed word-based American Sign Language were collected during the experiment, while 156 features were extracted from the collected data for classification. With the used of the long short-term memory (LSTM) algorithm, this system achieved up to 99.89% of accuracy. The high accuracy system performance indicated that this proposed system has a great potential to serve the deaf and dumb communities and resolve the communication gap.

• Archives of design research
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• v.19 no.2 s.64
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• pp.163-172
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• 2006

Sign is one of the important factors in city and national image formation, thus requires high level of quality. However, domestic sign emphasize only the sense of attention that leads to big sized signs, thus often results in a poor coordination with the surrounding space. This situation requires employees in sign business want to learn specialized knowledge about design field. Based on these circumstances, we propose sign design software to employees in sign business field as an aid tool that can help to develop good signs in terms of functionality as well as harmony of design. Thus, in this investigation, sign simulation software application case that can design sign and apply this sign to the actual application site is presented. In order to develop this software, literature survey and preliminary studies were performed to analyze the preparation process and environment, and designed sign design element and software elements, user interrace, and finally Java software were utilized. This developed software can be used as a textbook in sign design related departments in schools, and hopefully to enhance the social recognition of sign as well as academic interest.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.552-554
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• 2019

Deaf mutism (a hearing-impaired person and speech disorders) communicates using sign language. There are difficulties in communicating by voice. However, sign language can only be limited in communicating with people who know sign language because everyone doesn't use sign language when they communicate. In this paper, a finger language translation system is proposed and implemented as a means for the disabled and the non-disabled to communicate without difficulty. The proposed algorithm recognizes the finger language data by leap motion and self-learns the data using machine learning technology to increase recognition rate. We show performance improvement from the simulation results.

• Korean Journal of Radiology
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• v.25 no.2
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• pp.166-178
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• 2024

Objective: This study aimed to determine the predictive performance of non-contrast CT (NCCT) signs for hemorrhagic growth after intracerebral hemorrhage (ICH) when stratified by onset-to-imaging time (OIT). Materials and Methods: 1488 supratentorial ICH within 6 h of onset were consecutively recruited from six centers between January 2018 and August 2022. NCCT signs were classified according to density (hypodensities, swirl sign, black hole sign, blend sign, fluid level, and heterogeneous density) and shape (island sign, satellite sign, and irregular shape) features. Multivariable logistic regression was used to evaluate the association between NCCT signs and three types of hemorrhagic growth: hematoma expansion (HE), intraventricular hemorrhage growth (IVHG), and revised HE (RHE). The performance of the NCCT signs was evaluated using the positive predictive value (PPV) stratified by OIT. Results: Multivariable analysis showed that hypodensities were an independent predictor of HE (adjusted odds ratio [95% confidence interval] of 7.99 [4.87-13.40]), IVHG (3.64 [2.15-6.24]), and RHE (7.90 [4.93-12.90]). Similarly, OIT (for a 1-h increase) was an independent inverse predictor of HE (0.59 [0.52-0.66]), IVHG (0.72 [0.64-0.81]), and RHE (0.61 [0.54-0.67]). Blend and island signs were independently associated with HE and RHE (10.60 [7.36-15.30] and 10.10 [7.10-14.60], respectively, for the blend sign and 2.75 [1.64-4.67] and 2.62 [1.60-4.30], respectively, for the island sign). Hypodensities demonstrated low PPVs of 0.41 (110/269) or lower for IVHG when stratified by OIT. When OIT was ≤ 2 h, the PPVs of hypodensities, blend sign, and island sign for RHE were 0.80 (215/269), 0.90 (142/157), and 0.83 (103/124), respectively. Conclusion: Hypodensities, blend sign, and island sign were the best NCCT predictors of RHE when OIT was ≤ 2 h. NCCT signs may assist in earlier recognition of the risk of hemorrhagic growth and guide early intervention to prevent neurological deterioration resulting from hemorrhagic growth.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.6 no.4
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• pp.44-54
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• 1987

In this paper, the problem of LP-model and it's solution by liftering in cepstrum domain are investigated in speaker independent isolated-word recognition. And, clustering technique is discussed for obtaining the reference template. KMA (K-means iteration with average) method, which is transformed from UWA method and K-iteration method, has been suggested and compared with each other for clustering, the result of recognition experiments shows max. $95\%$ recognition rate when rasied-sign lifter and KMA clustering method is applied.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.69-72
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• 2002

화상처리에 의한 수화인식은 손의 움직임에 대한 추적을 통한 그 궤적의 정보를 이용하여 주로 처리하여 왔다. 본 논문에서는 궤적 정보만으로 정확히 인식할 수 없는 수화 단어에 대하여 국소 특징 인식 기법을 통하여 보다 정확한 인식을 행한다. 제안한 방법에 의하여 95%이상의 인식 결과를 얻을 수 있었다.