• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.25-34
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• 2010

Robust face recognition under various illumination environments is difficult to achieve. For robust face recognition with respect to illumination variations, illumination normalization of face images is usually applied as a preprocessing step. Most of previously proposed illumination normalization methods cannot handle cast shadows in face images effectively. In this paper, We propose a new face illumination normalization method based on the illumination-separated face identity texture subspace. Since the face identity texture subspace is constructed so as to be separated from the effects of illumination variations, the projection of face images into the subspace produces a good illumination-normalized face images. Through experiments, it is shown that the proposed face illumination normalization method can effectively eliminate cast shadows as well as attached shadows and achieves a good face illumination normalization.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4
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• pp.210-216
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• 2005

We Propose a new feature normalization scheme based on eigenspace for achieving robust speech recognition. In general, mean and variance normalization (MVN) is Performed in cepstral domain. However, another MVN approach using eigenspace was recently introduced. in that the eigenspace normalization Procedure Performs normalization in a single eigenspace. This Procedure consists of linear PCA matrix feature transformation followed by mean and variance normalization of the transformed cepstral feature. In this method. 39 dimensional feature distribution is represented using only a single eigenspace. However it is observed to be insufficient to represent all data distribution using only a sin91e eigenvector. For more specific representation. we apply unique na independent eigenspaces to cepstra, delta and delta-delta cepstra respectively in this Paper. We also normalize training data in eigenspace and get the model from the normalized training data. Finally. a feature space rotation procedure is introduced to reduce the mismatch of training and test data distribution in noisy condition. As a result, we obtained a substantial recognition improvement over the basic eigenspace normalization.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.348-366
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• 2007

Capacity of modem storage devices is becoming larger than yesterday and integration of disk is increasing. It refers that physical errors can damage a lot of digital information on storage devices. So we propose file system test framework in this paper to test integrity and robustness of file systems. We develop the tool for generating bad sectors on disks and the tool which creates all physical errors defined in storage devices. We also develop the tool for immediately monitoring the condition of read and write execution on storage devices. So, by integrating those tools, we develop FORT, test framework for confirming robustness of file system. We analyze robustness of ext3 file systems by FORT. Lastly, we present draft of intelligent system merging file system and device driver's layer architecture.

• Journal of Broadcast Engineering
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• v.13 no.4
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• pp.489-500
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• 2008

We propose a novel high-definition content watermarking algorithm that is highly feasible in UCC (User Created Contents) environment. We begin by addressing an association between broadcasting content and UCC in a view of copyrights, then present watermark requirements by analyzing various UCC editing-effect. Also, we provide a brief review of previous watermarking techniques that are supposed to satisfy the requirements. Our proposed scheme inserts a invisible watermark into both DC and AC components on $8{\times}8$ block DCT domain and extracts them after synchronization using DC image. Experimental results show that our technique satisfies the requirements of invisibility and robustness to a variety of attacks such as rotation, scaling, cropping, and JPEG compression, etc. Note that the proposed scheme is highly resilient to UCC edit attacks that are combined by many different types of watermark attacks.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.6
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• pp.527-534
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• 2016

The non-rail mobile rack, which is used for cargo storage, can improve the storage capacities of logistics centers. Furthermore, it has the advantage that it can be used in traditional logistics centers without making any changes or renovation, such as installing rails. However, when the rack is operated by separated drive actuators mounted on the left and the right wheels, precise position control of the wheels is necessary even if the unbalanced cargo weight on the rack would affect the control. Therefore, internal synchronization control for position tracking between the left and right wheels on the non-rail mobile rack is necessary in this study. In addition, external synchronization control for realizing the same straight movements between mobile racks is necessary. For the internal and the external synchronization control, we propose a synchronization control algorithm based on the repetitive control theory. An internal synchronization control algorithm with repetitive control theory requires the application of the robust servo control method owing to parameter variations. In this case, we can set up the gains for the robust servo control system by considering the cargo variations on the mobile rack. Furthermore, for developing the external synchronization control algorithm, we use a double repetitive control system to perform synchronization control between mobile racks. The efficiency of the proposed control algorithm will be verified by simulation and experimental results. The proposed algorithm can be easily applied in the industry.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.215-217
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• 2008

The most widely used method for searching contents of mot ion picture compares contents by extracted cuts. The cut extract ion methods, such as CHD(Color Histogram Difference) or ECR(Edge Change Ratio), are very weak at modifications such as cropping, resizing and low bit rate. The suggested method uses audio contents for indexing and searching to make search be robust against these modification. Scenes of audio contents are extracted for modification-robust search. And based on these scenes, make spectral powers binary on each frequency bin. in the time-frequency domain. The suggested method shows failure rate less than 1% on the false positive error and the true negative error to the modified(using cropping, clipping, row bit rate, addtive frame) contents.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.115-122
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• 2002

This paper proposes a robust speech recognition algorithm based on the wavelet filter banks. Since the proposed algorithm adopts a multiple band decision-making scheme, it performs robustness for noise as the presence of noisy severely degrades the performance of speech recognition system. For evaluating the performance of the proposed scheme, we compared it with the conventional speech recognizer based on the VQ for the 10-isolated korean digits with car noise. Here, the proposed method showed more 9~27% improvement of the recognition rate than the conventional VQ algorithm for the various car noisy environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.4
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• pp.365-370
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• 2013

In this paper, we propose robust blind estimators for the frequency offset of orthogonal frequency division multiplexing in non-Gaussian noise environments. We first propose a maximum likelihood (ML) estimator in non-Gaussian noise modeled as a complex isotropic Cauchy process, and then, a simpler estimator based on the ML estimator is proposed. From numerical results, we confirm that the proposed estimators are robust to the non-Gaussian noise and have a better estimation performance over the conventional estimator in non-Gaussian noise environments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.12
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• pp.7302-7308
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• 2014

Detecting target objects robustly in natural environments is a difficult problem in the computer vision and image processing areas. This paper suggests a method of robustly detecting target objects in the environments where reflection exists. The suggested algorithm first captures scenes with a stereo camera and extracts the line and corner features representing the target objects. This method then eliminates the reflected features among the extracted ones using a homographic transform. Subsequently, the method robustly detects the target objects by clustering only real features. The experimental results showed that the suggested algorithm effectively detects the target objects in reflection environments rather than existing algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.133-138
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• 2010

In this paper, we make up a feature image including spatial properties and statistical properties on image, and format covariance matrices using region variance magnitudes. By using it to texture classification, this paper puts a proposal for tough texture classification way to illumination, noise and rotation. Also we offer a way to minimalize performance time of texture classification using integral image expressing middle image for fast calculation of region sum. To estimate performance evaluation of proposed way, this paper use a Brodatz texture image, and so conduct a noise addition and histogram specification and create rotation image. And then we conduct an experiment and get better performance over 96%.