• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1493-1503
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• 2018

We propose an object recognition system and experiential game contents using Kinect to maximize object recognition rate by utilizing underwater robots. we implement an ice hockey game based on object-aware interactive contents to validate the excellence of the proposed system. The object recognition system, which is a preprocessor module, is composed based on Kinect and OpenCV. Network sockets are utilized for object recognition communications between C/S. The problem of existing research, degradation of object recognition at long distance, is solved by combining the system development method suggested in the study. As a result of the performance evaluation, the underwater robot object recognized all target objects (90.49%) with 80% of accuracy from a 2m distance, revealing 42.46% of F-Measure. From a 2.5m distance, it recognized 82.87% of the target objects with 60.5% of accuracy, showing 34.96% of F-Measure. Finally, it recognized 98.50% of target objects with 59.4% of accuracy from a 3m distance, showing 37.04% of F-measure.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.153-160
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• 2011

Recently, the use of security surveillance system including CCTV is increasing due to the increase of terrors and crimes. At the same time, interest of face recognition at a distance using surveillance cameras has been increasing. Accordingly, we analyzed the performance of face recognition according to distance using PCA-based face recognition and interpolation. In this paper, we used Nearest, Bilinear, Bicubic, Lanczos3 interpolations to interpolate face image. As a result, we confirmed that existing interpolation have an few effect on performance of PCA-based face recognition and performance of PCA-based face recognition is improved by including face image according to distance in traning data.

• The Journal of the Acoustical Society of Korea
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• v.11 no.2
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• pp.73-84
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• 1992

This paper studies noise subtraction methods and distance measures for speech recognition in a noisy environment, and investigates noise robustness of the distance measures applied to the problem of isolated word recognition in white Gaussian and colored noise (vehicle noise) environments. Noise subtraction methods which can be used as a pre-processor for the speech recognition system, such as the spectral subtraction method, autocorrelation subtraction method, adaptive noise cancellation and acoustic beamforming are studied, and distance measures such and Log Likelihood Ratio ($d_{LLR}$), cepstral distance measure ($d_{CEP}$), weighted cepstral distance measure ($d_{WCEP}$), spectral slope distance measure ($d_{RPS}$) and cepstral projection distance measure ($d_{CP},\;d_{BCP},\;d_{WCP},\;d_{BWCP}$) are also investigated. Testing of the distance measures for speaker-dependent isolated word recognition in a noisy environment indicate that $d_{RPS}\;and\;d_{WCEP}$ which weigh higher order cepstral coefficients more heavily give considerable performance improvement over $d_{CEP}and\;d_{LLR}$. In addition, when no pre-emphasis is performed, the recognizer can maintain higher performance under high noise conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5474-5490
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• 2017

Although two-dimensional principal component analysis (2DPCA) has been shown to be successful in face recognition system, it is still very sensitive to illumination variations. To reduce the effect of these variations, texture-based techniques are used due to their robustness to these variations. In this paper, we explore several texture-based techniques and determine the most appropriate one to be used with 2DPCA-based techniques for face recognition. We also propose a new distance metric computation in 2DPCA called Row Assembled Matrix Distance (RowAMD). Experiments on Yale Face Database, Extended Yale Face Database B, AR Database and LFW Database reveal that the proposed RowAMD distance computation method outperforms other conventional distance metrics when Local Line Binary Pattern (LLBP) and Multi-scale Block Local Binary Pattern (MB-LBP) are used for face authentication and face identification, respectively. In addition to this, the results also demonstrate the robustness of the proposed RowAMD with several texture-based techniques.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.41-49
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• 2024

Speech recognition technology is continuously advancing and widely used in various fields. In this study, we aimed to investigate the impact of speech data quality on speech recognition models by dividing the dataset into the entire dataset and the top 70% based on Signal-to-Noise Ratio (SNR). Utilizing Seamless M4T and Google Cloud Speech-to-Text, we examined the text transformation results for each model and evaluated them using the Levenshtein Distance. Experimental results revealed that Seamless M4T scored 13.6 in models using data with high SNR, which is lower than the score of 16.6 for the entire dataset. However, Google Cloud Speech-to-Text scored 8.3 on the entire dataset, indicating lower performance than data with high SNR. This suggests that using data with high SNR during the training of a new speech recognition model can have an impact, and Levenshtein Distance can serve as a metric for evaluating speech recognition models.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.151-157
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• 2006

This paper is concerned with the template-based face recognition from robot camera images with illumination and distance variations. The approaches used in this paper consist of Eigenface, Fisherface, and Icaface which are the most representative recognition techniques frequently used in conjunction with face recognition. These approaches are based on a popular unsupervised and supervised statistical technique that supports finding useful image representations, respectively. Thus we focus on the performance comparison from robot camera images with unwanted variations. The comprehensive experiments are completed for a databases with illumination and distance variations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.10
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• pp.1422-1432
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• 1993

Nonlinear mapping function of the HCNN( Hidden Control Neural Network ) can change over time to model the temporal variability of a speech signal by combining the nonlinear prediction of conventional neural networks with the segmentation capability of HMM. We have two things in this paper. first, we showed that the performance of the HCNN is better than that of HMM. Second, the HCNN with its prediction error measure given by weighted distance is proposed to use suitable distance measure for the HCNN, and then we showed that the superiority of the proposed system for speaker-independent speech recognition tasks. Weighted distance considers the differences between the variances of each component of the feature vector extraced from the speech data. Speaker-independent Korean digit recognition experiment showed that the recognition rate of 95%was obtained for the HCNN with Euclidean distance. This result is 1.28% higher than HMM, and shows that the HCNN which models the dynamical system is superior to HMM which is based on the statistical restrictions. And we obtained 97.35% for the HCNN with weighted distance, which is 2.35% better than the HCNN with Euclidean distance. The reason why the HCNN with weighted distance shows better performance is as follows : it reduces the variations of the recognition error rate over different speakers by increasing the recognition rate for the speakers who have many misclassified utterances. So we can conclude that the HCNN with weighted distance is more suit-able for speaker-independent speech recognition tasks.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.3
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• pp.179-184
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• 2013

A voice recognition technology as a technology fundament plays an important role in medical devices with smart functions. This paper describes the implementation of a control system that can be utilized as a part of illumination equipment for medical applications (IEMA) based on a voice recognition. The control system can essentially be divided into five parts, the microphone, training part, recognition part, memory part, and control part. The system was implemented using the RSC-4x evaluation board which is included the micro-controller for voice recognition. To investigate the usefulness of the implemented control system, the experiments of the recognition rate was carried out according to the input distance for voice recognition. As a result, the recognition rate of the control system was more than 95% within a distance between 0.5 and 2m. The result verified that the implemented control system performs well as the smart control system based for an IEMA.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.556-561
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• 2009

In this paper, we propose new method to calculate the distance between intuitionistic fuzzy sets(IFSs) based on the three dimensional representation of IFSs and analyze the relations of similarity measure and distance measure of IFSs. Finally, we apply the proposed measures to pattern recognitions.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.174-182
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• 2015

In this paper, we developed the small size RGB sensor that recognizes a long distance using a low-cost color sensor. Light receiving portion of the sensor was used as a camera lens for far distance recognition, and illuminating unit was increased the strength of the light by using a high-power white LED and a lens mounted on the reflector. RGB color recognition algorithm consists of the learning process and the realtime recognition process. We obtain a normalized RGB color reference data in the learning process using the specimens painted with target colors, and classifies the three colors using the Mahalanobis distance in recognition process. We apply the developed the RGB color recognition sensor to a prototype of the part classification system and evaluate the performance of its.