• International Journal of Contents
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• v.12 no.1
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• pp.1-5
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• 2016

To manipulate large video contents, effective video indexing and retrieval are required. A large number of video indexing and retrieval algorithms have been presented for frame-wise user query or video content query whereas a relatively few video sequence matching algorithms have been proposed for video sequence query. In this paper, we propose an efficient algorithm that extracts key frames using color histograms and matches the video sequences using edge features. To effectively match video sequences with a low computational load, we make use of the key frames extracted by the cumulative measure and the distance between key frames, and compare two sets of key frames using the modified Hausdorff distance. Experimental results with real sequence show that the proposed video sequence matching algorithm using edge features yields the higher accuracy and performance than conventional methods such as histogram difference, Euclidean metric, Battachaya distance, and directed divergence methods.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.1
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• pp.11-16
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• 2006

To manipulate large video databases, effective video indexing and retrieval are required. A large number of video indexing and retrieval algorithms have been presented for frame-w]so user query or video content query whereas a relatively few video sequence matching algorithms have been proposed for video sequence query. In this paper, we propose an efficient algorithm to extract key frames using color histograms and to match the video sequences using edge features. To effectively match video sequences with low computational load, we make use of the key frames extracted by the cumulative measure and the distance between key frames, and compare two sets of key frames using the modified Hausdorff distance. Experimental results with several real sequences show that the proposed video retrieval algorithm using color and edge features yields the higher accuracy and performance than conventional methods such as histogram difference, Euclidean metric, Battachaya distance, and directed divergence methods.

• Journal of Korea Game Society
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• v.13 no.4
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• pp.15-24
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• 2013

The game of Go, which is originated at least more than 2,500 years ago, is one of the oldest board games in the world. So far the theoretical studies concerning to the Go openings are still insufficient. We applied traditional LDA algorithm to recognize a pro player's opening to a class obtained from the training openings. Both class-independent LDA and class-dependent LDA methods are conducted with the Go game records of the Korean top 10 professional Go players. Experimental result shows that the average recognition rate of class-independent LDA is 14% and class-dependent LDA 12%, respectively. Our research result also shows that in contrary to our common sense the algorithm based on PCA outperforms the algorithm based on LDA and reveals the new fact that the Euclidean distance metric method rarely does not inferior to LDA.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1072-1077
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• 2017

Mean-reverting analysis refers to a way of estimating the underlining tendency after new data has evoked the variation in the equilibrium state. In this paper, we propose a new method to interpret the specular portraits of Premature Ventricular Contraction(PVC) arrhythmia by applying K-means unsupervised learning algorithm on electrocardiogram(ECG) data. Aiming at this purpose, we applied a mean-reverting model to analyse Heart Rate Variability(HRV) in terms of the modified poincare plot by considering PVC rhythm as the component of disrupting the homeostasis state. Based on our experimental tests on MIT-BIH ECG database, we can find the fact that the specular patterns portraited by K-means clustering on mean-reverting HRV data can be more clearly visible and the Euclidean metric can be used to identify the discrepancy between the normal sinus rhythm and PVC beats by the relative distance among cluster-centroids.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.249-252
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• 2001

TCM(Trellis Coded Modulation) has soft decision scheme so that BM(Branch Metric) calculates the ED(Euclidean Distance) between the received signal and each code words in signal space. For computing the ED, square and square root computations increase the hardware complexity. Some simplified method is known for convolutional codes with QPSK(Quadrature Phase Shift Keying), PSK(Phase Shift Keying) modulation. But it is not acceptable for QAM (Quadrature Amplitude Modulation)-TCM scheme. In this paper, we suggest that two modified BM computation methods, which is applicable for QAM-TCM. By comparative study, we also assessed two proposed method in the case of hardware complexity and BER (Bit Error Rate) performance.

• International Journal of Contents
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• v.6 no.3
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• pp.5-9
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• 2010

To manage large database system with video, effective video indexing and retrieval are required. A large number of video retrieval algorithms have been presented for frame-wise user query or video content query, whereas a few video identification algorithms have been proposed for video sequence query. In this paper, we propose an effective video identification algorithm for video sequence query that employs the Cauchy function of histograms between successive frames and the modified Hausdorff distance. To effectively match the video sequences with a low computational load, we make use of the key frames extracted by the cumulative Cauchy function and compare the set of key frames using the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed algorithm for video identification yields remarkably higher performance than conventional algorithms such as Euclidean metric, and directed divergence methods.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2237-2242
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• 2005

The original RRT is iteratively expanded by applying control inputs that drive the system slightly toward randomly-selected states, as opposed to requiring point-to-point convergence, as in the probabilistic roadmap approach. It is generally known that the performance of RRTs can be improved depending on the selection of the metrics in choosing the nearest vertex and bias techniques in choosing random states. We designed a path planning algorithm based on the RRT method for a remote-controlled mobile robot. First, we considered a bias technique that is goal-biased Gaussian random distribution along the command directions. Secondly, we selected the metric based on a weighted Euclidean distance of random states and a weighted distance from the goal region. It can save the effort to explore the unnecessary regions and help the mobile robot to find a feasible trajectory as fast as possible. Finally, the constraints of the actuator should be considered to apply the algorithm to physical mobile robots, so we select control inputs distributed with commanded inputs and constrained by the maximum rate of input change instead of random inputs. Simulation results demonstrate that the proposed algorithm is significantly more efficient for planning than a basic RRT planner. It reduces the computational time needed to find a feasible trajectory and can be practically implemented in a remote-controlled mobile robot.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3A
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• pp.324-330
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• 2004

In this paper, we propose the TCM(Trellis coded modulation) decoding scheme that reduces the number of operations in branch metric with STBC(space time block codes) channel information and present the implementation results. The proposed TCM decoding scheme needs only 1 signal point in each TCM subset. Using bias point scheme, It detects the minimum distance symbol. The proposed TCM decoding scheme can reduce the branch metric calculations. In case of 16QAM 8 subset, the reduction ratio is about 50％ and for 64QAM 8 subset, about 80％ reduction can be obtained. The results of logic synthesis for the TCM and STBC decoder with the proposed scheme are 57.6K gate count.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.6
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• pp.1-8
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• 1994

In this paper, in order to apply the idea MDPSK to TCM, we use signal set expansion and set partition by phase differences. Through this we propose the trellis-coded MDPSK. And the Viterbi decoder containing branch metrics of the squared Euclidean distance of the Lth order phase difference as well as the first order phase difference is introduced in order to improve the bit error rate(BER) in the differential detection of the trellis-coded MDPSK. The proposed Viterbi decoder is conceptually same to the sliding multiple symbol dection method which uses the branch metric with the first and Lth order phase differences. We investigate the performance of the uncoded DQPSK and the trallis-coded D8PSK in additive white Gaussian noise (AWGN) through the Monte Carlo simulation under the two cases of using and not using the Lth order phase difference metric. The study shows that trellis-coded 8DPSK is an attractive scheme for power and bandlimited systems while also improving the BER performance when the Viterbi decoder is employed to the Lth order phase order difference metric. This performance improvement has been obtained without sacrificing the bandwidth or the power efficiency.

• Journal of Korea Multimedia Society
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• v.16 no.10
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• pp.1156-1162
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• 2013

Face verification has been widely studied during the past two decades. One of the challenges is the rising concern about the security and privacy of the template database. In this paper, we propose a secure face verification system which generates a unique secure cryptographic key from a face template. The face images are processed to produce face templates or codes to be utilized for the encryption and decryption tasks. The result identity data is encrypted using Advanced Encryption Standard (AES). Distance metric naming hamming distance and Euclidean distance are used for template matching identification process, where template matching is a process used in pattern recognition. The proposed system is tested on the ORL, YALEs, and PKNU face databases, which contain 360, 135, and 54 training images respectively. We employ Principle Component Analysis (PCA) to determine the most discriminating features among face images. The experimental results showed that the proposed distance measure was one the promising best measures with respect to different characteristics of the biometric systems. Using the proposed method we needed to extract fewer images in order to achieve 100% cumulative recognition than using any other tested distance measure.