• Smart Media Journal
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• v.1 no.3
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• pp.8-14
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• 2012

In this paper, we develope the smart phone App. contents of 3D sign language to widen the opportunity of the korean sign language education for the hearing-impaired and normal people. Especially, we propose the sign language conversion algorithm that automatically transform the structure of Korean phrases to the structure of the sign language. Also, we implement the 3D sign language animation DB using motion capture system and data glove for acquiring the natural motions. Finally, UNITY 3D engine is used for the realtime 3D rendering of sign language motion. We are distributing the proposed App. with 3D sign language DB of 1,300 words to the iPhone App. store and Android App. store.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.11
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• pp.511-518
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• 1986

An algorithm for block-decomposition of a linear, time-invariant, discrete large-scale systems is presented, based upon the matrix sign function on Z-plane. The block-decomposition is performed by defining a reference circle, a circular stripe and projection operators. Simulation study shows that the presented algorithm is very useful for multivariable control system's analysis and design.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.562-568
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• 2016

In this paper, we focus on recognition of speed-limit signs among a few types of traffic signs because speed-limit sign is closely related to safe driving of drivers. Although histogram of oriented gradient (HOG) and local binary patterns (LBP) are representative features for object recognition, these features have a weakness with respect to rotation, in that it does not consider the rotation of the target object when generating patterns. Therefore, this paper propose the fast rotation-invariant binary patterns (FRIBP) algorithm to generate a binary pattern that is robust against rotation. The proposed FRIBP algorithm deletes an unused layer of the histogram, and eliminates the shift and comparison operations in order to quickly extract the desired feature. The proposed FRIBP algorithm is successfully applied to German Traffic Sign Recognition Benchmark (GTSRB) datasets, and the results show that the recognition capabilities of the proposed method are similar to those of other methods. Moreover, its recognition speed is considerably enhanced than related works as approximately 0.47second for 12,630 test data.

• Korean Journal of Mathematics
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• v.18 no.3
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• pp.289-298
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• 2010

E$\breve{g}$ecio$\breve{g}$lu and Remmel [1] gave a combinatorial interpretation for the entries of the inverse Kostka matrix $K^{-1}$. Using this interpretation Sagan and Lee [8] constructed a sign reversing involution on special rim hook tableaux. In this paper we generalize Sagan and Lee's algorithm on special rim hook tableaux to give a combinatorial partial proof of $K^{-1}K=I$.

• Journal of Information Technology Applications and Management
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• v.23 no.3
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• pp.87-97
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• 2016

This paper proposes a fast sound source localization method using a TDOA sign-based algorithm. We present an L-shaped microphone set-up which creates four major regions in the range of $0^{\circ}{\sim}360^{\circ}$ by the intersection of the positive and negative regions of the individual microphone pairs. Then, we make an initial source region prediction based on the signs of two TDOA estimates before computing the azimuth value. Also, we apply a threshold and angle comparison to tackle the existing front-back confusion problem. Our experimental results show that the proposed method is comparable in accuracy to previous three microphone array methods; however, it takes a shorter computation time because we compute only two TDOA values.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.8
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• pp.333-340
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• 1986

This paper presents an approach for determining the discrete reduced-order models for largescale system by using matrix sign function. We define projection operators based on the matrix sign function and develop the algorithm for model-reduction by using them. Simulation studies show that the proposed altgorithm is very useful.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.491-493
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• 2011

Traffic sign detection is the domain of automatic driver assistant systems. There are literatures for traffic sign detection using color information, however, color-based method contains ill-posed condition and to extract the region of interest is difficult. In our work, we propose a method for traffic sign detection using k-means clustering method, back-propagation neural network, and projection histogram features that yields the robustness for ill-posed condition. Using the color information of traffic signs enables k-means algorithm to cluster the region of interest for the detection efficiently. In each step of clustering, a cluster is verified by the neural network so that the cluster exactly represents the location of a traffic sign. Proposed method is practical, and yields robustness for the unexpected region of interest or for multiple detections.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1662-1665
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• 2002

This paper proposes a novel semantic feature detection (SFD) method for real-time image transmission of sign language and finger spelling. We extract semantic information as an interlingua from input text by natural language processing, and then transmit the semantic feature detection, which actually is a parameterized action representation, to the 3-D articulated humanoid models prepared in each client in remote locations. Once the SFD is received, the virtual human will be animated by the synthesized SFD. The experimental results based on Japanese sign langauge and Chinese sign langauge demonstrate that this algorithm is effective in real-time image delivery of sign language and finger spelling.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.93-103
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• 2018

This paper presents a method for speed limit sign recognition in images. Conventional sign recognition methods decreases recognition accuracy because they are very sensitive and include repeated features. The proposed method emphasizes color attributes based on the weighted YUV color space. Moreover, the recognition accuracy can be improved by extracting the local region of interest (ROI) in the candidates. The proposed method uses the Haar features and the Adaboost classifier for recognition. Experimental results confirm that the proposed algorithm is superior to conventional algorithms under various speed signs and conditions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.125-134
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• 2004

This paper proposes a method for recognition of traffic signs using wavelet transform and shape information from the segmented traffic sign regions. 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 sign regions based on their symmetries on X- and Y-axes. In the recognition stage, it utilizes shape information including moment edge correlogram and the number of crossings which concentric circular patterns from region center intersects with frequency information extracted by wavelet transform It finally performs recognition by measuring similarity with the templates in the database. The experimental results show the validity of the proposed method from geometric transformations and environmental factors.