• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.505-510
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• 2004

This paper suggests a new CT-image segmentation algorithm. This algorithm uses morphological filters and the watershed algorithms. The proposed CT-image segmentation algorithm consists of six parts: preprocessing, image simplification, feature extraction, decision making, region merging, and postprocessing. By combining spatial and temporal information, we can get more accurate segmentation results. The simulation results illustrate not only the segmentation results of the conventional scheme but also the results of the proposed scheme; this comparison illustrates the efficacy of the proposed technique. Furthermore, we compare the various medical images of the structuring elements. Indeed, to illustrate the improvement of coding efficiency in postprocessing, we use differential chain coding for the shape coding of results.

• Journal of computational fluids engineering
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• v.11 no.2 s.33
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• pp.56-61
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• 2006

As the size and dimension of target problems in the field of computational engineering including CFD gets bigger and higher, it is needed to have more efficient and flexible data visualization environment in terms of software and hardware. Even though it is still manageable to use a mouse in controlling 3-dimensional data visualization, it would be beneficial to use 3-D input device for 3-D visualization. 'Data Glove' is one of the best 3-D input devices, because human hands are best tools for understanding 3-D space and manipulating 3-D objects. Signals coming from 'Data Glove' are analog and very sensitive to finger motions, therefore signal filtering using a digital filter is applied. This paper describes our experience and benefits of using data gloves in controlling 3-dimensional postprocessing softwares.

• The Journal of the Acoustical Society of Korea
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• v.16 no.3E
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• pp.19-23
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• 1997

The goal of postprocessing in automatic speech recognition is to improve recognition performance by utterance verification at the output of recognition stage. It is focused on the effective rejection of out-of vocabulary words based on the confidence score of hypothesized candidate word. We present two methods for computing confidence scores. Both methods are based on the distance between each observation vector and the representative code vector, which is defined by the most likely code vector at each state. While the first method employs simple time normalization, the second one uses a normalization technique based on the concept of on-line garbage mode[1]. According to the speaker independent isolated words recognition experiment with discrete density HMM, the second method outperforms both the first one and conventional likelihood ratio scoring method[2].

• Speech Sciences
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• v.10 no.3
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• pp.225-239
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• 2003

In this paper, we develop a keyword spotting system using vocabulary-independent speech recognition technique, and investigate several non-keyword modeling and post-processing methods to improve its performance. In order to model non-keyword speech segments, monophone clustering and Gaussian Mixture Model (GMM) are considered. We employ likelihood ratio scoring method for the post-processing schemes to verify the recognition results, and filler models, anti-subword models and N-best decoding results are considered as an alternative hypothesis for likelihood ratio scoring. We also examine different methods to construct anti-subword models. We evaluate the performance of our system on the automatic telephone exchange service task. The results show that GMM-based non-keyword modeling yields better performance than that using monophone clustering. According to the post-processing experiment, the method using anti-keyword model based on Kullback-Leibler distance and N-best decoding method show better performance than other methods, and we could reduce more than 50% of keyword recognition errors with keyword rejection rate of 5%.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.1
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• pp.34-42
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• 2013

Human faces have been broadly studied in digital image and video processing fields. An appearance-based method, the adaptive boosting learning algorithm using integral image representations has been successfully employed for face detection, taking advantage of the feature extraction's low computational complexity. In this paper, we propose a face-detection postprocessing method that equalizes instantaneous facial regions in an efficient hardware architecture for use in real-time multimedia applications. The proposed system requires low hardware resources and exhibits robust performance in terms of the movements, zooming, and classification of faces. A series of experimental results obtained using video sequences collected under dynamic conditions are discussed.

• Proceedings of the Korean Society for Language and Information Conference
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• 1995.02a
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• pp.127-132
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• 1995

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1995.06a
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• pp.381-384
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• 1995

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.449-459
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• 2016

This paper proposes a new method to improve the three problems of existing automatic composition method using artificial neural networks. The first problem is that the existing beat post-processing to fit into music theories could not handle all the cases of occurring. The second one is that the pitch space generated by artificial neural networks is distorted because the rest is trained with the pitch on the same neural network with large values. The last problem is caused by the difference between the initial melody and beats given by user and those generated by an artificial neural network in the process of new composition. In order to treat these problems, we propose an enhanced post-processing of beats, initial melody processing, and adoption of artificial neural network for rest. It was found from experiments that the proposed methods totally resolved the three problems.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.263-271
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• 2000

In this paper, an acoustic echo canceller(AEC) is implemented by ADSP-2181. This AEC uses a noise robust adaptive algorithm and a postprocessing method which attenuates residual echo using cross-correlation between estimated error signal and microphone input signal. We propose new postprocessing method that uses two thresholds to prevent signal distortion after postprocessing and to improve the performance of AEC without extra computational burden. Through experiments using a 16 bit fixed-point DSP board (ADSP-2181 EZ-KIT Lite board), it is shown that the noise robust adaptive algorithm performs well in the double-talk situations and the convergence speed is comparable to NLMS. Using the postprocessor, ERLE is improved about 20 dB. As a result, the AEC with a postprocessor shows better performance than conventional ones.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.296-306
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• 2006

We proposed the algorithm for the quantization noise reduction based on variable filter adaptive to edge signal in MPEG postprocessing system. In our algorithm, edge map and local modulus maxima in the decoded images are obtained by using 2D Mallat wavelet tilter. And then, blocking artifacts in inter-block are reduced by Gaussian LPF that is variable to filtering region according to edge map. Ringing artifacts in intra-block are reduced by 2D SAF according to local modulus maxima. Experimental results show that the proposed algorithm was superior to the conventional algorithms as regards PSNR, which was improved by 0.04-0.20 dB, and the subjective image quality.