• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.216-218
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• 2005

This paper present a new approach to a noise robust recognizer for WPS interface. In noisy environments, performance of speech recognition is decreased rapidly. To solve this problem, We propose the recognition system using vocal-cord signal instead of speech. Vocal-cord signal has low quality but it is more robust to environment noise than speech signal. As a result, we obtained 75.21% accuracy using MFCC with CMS and 83.72% accuracy using ZCPA with RASTA.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.130-136
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• 2015

In this paper, we propose a robust curved lane marking detection method. Several lane detection methods have been proposed, however most of them have considered only straight lanes. Compared to the number of straight lane detection researches, less number of curved-lane detection researches has been investigated. This paper proposes a new curved lane detection and tracking method which is robust to various illumination conditions. First, the proposed methods detect straight lanes using a robust road feature image. Using the geometric relation between a vehicle camera and the road plane, several circle models are generated, which are later projected as curved lane models on the camera images. On the top of the detected straight lanes, the curved lane models are superimposed to match with the road feature image. Then, each curve model is voted based on the distribution of road features. Finally, the curve model with highest votes is selected as the true curve model. The performance and efficiency of the proposed algorithm are shown in experimental results.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.503-506
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• 2013

In this paper, we propose the face recognition method combining the ECSP preprocessing technique which is modified version of previous CS-LBP and the illumination-robust D2D-PCA feature. The performance evaluation of proposed method was carried out using various binary pattern operators and feature extraction algorithms such as well-known PCA and 2D-PCA on the Yale B database. As a results, the proposed method showed the best recognition accuracy compared to different approaches, and we confirmed that the proposed approach is robust to illumination variation.

• Journal of the Semiconductor & Display Technology
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• v.14 no.2
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• pp.53-59
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• 2015

Since the amount of digital image continues to grow in usage, users feel increased difficulty in finding specific images from the image collection. This paper proposes a novel image searching scheme that extracts the image feature using combination of Advanced SURF (Speed-Up Robust Feature) and DCD (Dominant Color Descriptor). The key point of this research is to provide a new feature extraction algorithm to improve the existing SURF method with removal of unnecessary feature in image retrieval, which can be adaptable to mobile system and efficiently run on the mobile environments. To evaluate the proposed scheme, we assessed the performance of simulation in term of average precision and F-score on two databases, commonly used in the field of image retrieval. The experimental results revealed that the proposed algorithm exhibited a significant improvement of over 14.4% in retrieval effectiveness, compared to OpenSURF. The main contribution of this paper is that the proposed approach achieves high accuracy and stability by using ASURF and DCD in searching for natural image on mobile platform.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.118-125
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• 2016

This study evaluates the performance of image to patient registration algorithm by using stereo vision and CT image for facial region surgical navigation. For the process of image to patient registration, feature extraction and 3D coordinate calculation are conducted, and then 3D CT image to 3D coordinate registration is conducted. Of the five combinations that can be generated by using three facial feature extraction methods and three registration methods on stereo vision image, this study evaluates the one with the highest registration accuracy. In addition, image to patient registration accuracy was compared by changing the facial rotation angle. As a result of the experiment, it turned out that when the facial rotation angle is within 20 degrees, registration using Active Appearance Model and Pseudo Inverse Matching has the highest accuracy, and when the facial rotation angle is over 20 degrees, registration using Speeded Up Robust Features and Iterative Closest Point has the highest accuracy. These results indicate that, Active Appearance Model and Pseudo Inverse Matching methods should be used in order to reduce registration error when the facial rotation angle is within 20 degrees, and Speeded Up Robust Features and Iterative Closest Point methods should be used when the facial rotation angle is over 20 degrees.

• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.83-88
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• 2001

In this paper, matched field processing algorithm robust to environmental mismatches in an ocean waveguide based on feature extraction is summarized. However, in applying this processor to localize a source there are two preliminary issues to be resolved. One is the number of eigenvectors to be extracted and the other is the number of environmental samples to be used. To determine these issues, the relation between the number of dominant modes propagating in a given ocean waveguide and that of eigenvectors to be extracted is analyzed. Then, the analysis results are confirmed by the subspace analysis. This analysis quantifies the similarity between the subspace spanned by the signal vectors and that spanned by the eigenvectors to be extracted. The error index is defined as a relative difference between the location estimated by the current processor and the real source location. It is identified that in the case of extracting the largest eigenvectors equal to the number of dominant modes in a given environment, the processor localizes the source successfully. From the numerical simulations, it is shown that use of at least 30 environmental samples guarantee stable performance of the proposed processor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.3
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• pp.613-620
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• 2010

Additive noise and channel distortion strongly degrade the performance of speaker verification systems, as it introduces distortion of the features of speech. This distortion causes a mismatch between the training and recognition conditions such that acoustic models trained with clean speech do not model noisy and channel distorted speech accurately. This paper presents a phase-related feature extraction method in order to improve the robustness of the speaker verification systems. The instantaneous frequency is computed from the phase of speech signals and features from the histogram of the instantaneous frequency are obtained. Experimental results show that the proposed technique offers significant improvements over the standard techniques in both clean and adverse testing environments.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.195-203
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• 2012

This paper proposes ground target classification algorithm based on decision fusion and feature extraction method using multi-sensor images. The decisions obtained from the individual classifiers are fused by applying a weighted voting method to improve target recognition rate. For classifying the targets belong to the individual sensors images, features robust to scale and rotation are extracted using the difference of brightness of CM images obtained from CCD image and the boundary similarity and the width ratio between the vehicle body and turret of target in FLIR image. Finally, we verity the performance of proposed ground target classification algorithm and feature extraction method by the experimentation.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.1-8
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• 2013

This paper proposes a new feature extraction algorithm to improve children's speech recognition in Korean. The proposed feature extraction algorithm combines three methods. The first method is on the vocal tract length normalization to compensate acoustic features because the vocal tract length in children is shorter than in adults. The second method is to use the uniform bandwidth because children's voice is centered on high spectral regions. Finally, the proposed algorithm uses a smoothing filter for a robust speech recognizer in real environments. This paper shows the new feature extraction algorithm improves the children's speech recognition performance.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.328-336
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• 2012

In this paper, we propose a robust fingertip extraction and extended Continuously Adaptive Mean Shift (CAMSHIFT) based robust hand gesture recognition for natural human-like HRI (Human-Robot Interaction). Firstly, for efficient and rapid hand detection, the hand candidate regions are segmented by the combination with robust $YC_bC_r$ skin color model and haar-like features based adaboost. Using the extracted hand candidate regions, we estimate the palm region and fingertip position from distance transformation based voting and geometrical feature of hands. From the hand orientation and palm center position, we find the optimal fingertip position and its orientation. Then using extended CAMSHIFT, we reliably track the 2D hand gesture trajectory with extracted fingertip. Finally, we applied the conditional density propagation (CONDENSATION) to recognize the pre-defined temporal motion trajectories. Experimental results show that the proposed algorithm not only rapidly extracts the hand region with accurately extracted fingertip and its angle but also robustly tracks the hand under different illumination, size and rotation conditions. Using these results, we successfully recognize the multiple hand gestures.