• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.253-264
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• 2009

This paper presents an algorithm, aims at practical applications, for the high speed processing and performance enhancement of lane detection base on vision processing system. As a preprocessing for high speed lane detection, the vanishing line estimation and the optimal extraction of region of interest for lane boundary (ROI-LB) can be processed to reduction of detection region in which high speed processing is enabled. Image feature information is extracted only in the ROI-LB. Road lane is extracted using a non-parametric model fitting and Hough transform within the ROI-LB. With simultaneous processing of noise reduction and edge enhancement using the Laplacian filter, the reliability of feature extraction can be increased for various road lane patterns. Since outliers of edge at each block can be removed with clustering of edge orientation for each block within the ROI-LB, the performance of lane detection can be greatly improved. The various real road experimental results are presented to evaluate the effectiveness of the proposed method.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.323-331
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• 2005

This paper proposes an improved system for recognizing facial expressions in various internal states that is illumination-invariant and without detectable rue such as a neutral expression. As a preprocessing to extract the facial expression information, a whitening step was applied. The whitening step indicates that the mean of the images is set to zero and the variances are equalized as unit variances, which reduces murk of the variability due to lightening. After the whitening step, we used the facial expression information based on principal component analysis(PCA) representation excluded the first 1 principle component. Therefore, it is possible to extract the features in the lariat expression images without detectable cue of neutral expression from the experimental results, we ran also implement the various and natural facial expression recognition because we perform the facial expression recognition based on dimension model of internal states on the images selected randomly in the various facial expression images corresponding to 83 internal emotional states.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.432-438
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• 2009

In this paper, we evaluated the performance of the automatic classifier applied for the discrimination of acute alcoholic liver injury and chronic liver fibrosis. The classifier uses the discriminant peaks of the preprocessed Raman spectrum as a feature set. In preprocessing step, we subtract baseline and apply Savitzky-Golay smoothing filter which is known to be useful at preserving peaks. After identifying discriminant peaks from the spectra, we carried out the classification experiments using MAP and neural networks. According to the experimental results, the classifier shows the promising results to diagnosis alcoholic liver injury and chronic liver fibrosis. Classification results over 80% means that the peaks used as a feature set is useful for diagnosing liver disease.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.481-483
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• 2018

Recently, as the demand for electronic communication equipment increases, the importance of image and signal processing is increasing. However, noise is generated in digital signal due to various causes during transmission and reception, lowering equipment reliability and causing malfunction. Particularly, since AWGN may be found in most electronic equipments, AWGN removal is mandatorily performed as a preprocessing phase in various fields, such as image recognition, extraction, and segmentation. In the present paper, an AWGN removal algorithm which considers high frequency components is proposed. Conventional methods show relatively inadequate performance in images with high frequency components. To overcome this problem, proposed is a filter algorithm that add or subtract difference images in the local mask. And to verify performance of the proposed algorithm, PSNR and enlarged images are used to compare with the existing methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6C
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• pp.585-593
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• 2007

For the improvement of coding efficiency, the H.264 standard uses new coding tools such as VBS, 1/4-pel accurate ME, multiple references, intra prediction, loop filter, etc. Using these coding tools, H.264 has achieved significant improvements from rate-distortion point of view compared to existing standards. However, the encoder complexity is greatly increased due to these coding tools. We focus on the complexity reduction method of intra-mode decision. Our algorithm first restricts selective prediction modes of Intra4x4 using a simple preprocessing. The prediction modes of Intra4x4 are used for restricting those of the other inter-modes. Simulation results show that the proposed method outperforms other conventional methods and save about 82% of total encoding time.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.435-443
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• 2018

Recently, with the explosive development of computing power, various methods such as RNN and CNN have been proposed under the name of Deep Learning, which solve many problems of Computer Vision have. The Generative Adversarial Network, released in 2014, showed that the problem of computer vision can be sufficiently solved in unsupervised learning, and the generation domain can also be studied using learned generators. GAN is being developed in various forms in combination with various models. Machine learning has difficulty in collecting data. If it is too large, it is difficult to refine the effective data set by removing the noise. If it is too small, the small difference becomes too big noise, and learning is not easy. In this paper, we apply a deep CNN model for extracting facial region in image frame to GAN model as a preprocessing filter, and propose a method to produce composite images of various facial expressions by stably learning with limited collection data of two persons.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.3
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• pp.68-74
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• 2001

A lane detection based on a road model or feature all needs correct acquirement of information on the lane in an image. It is inefficient to implement a lane detection algorithm through the full range of an image when it is applied to a real road in real time because of the calculating time. This paper defines two (other proper terms including"modes") for detecting lanes on a road. First is searching mode that is searching the lane without any prior information of a road. Second is recognition mode, which is able to reduce the size and change the position of a searching range by predicting the position of a lane through the acquired information in a previous frame. It allows to extract accurately and efficiently the edge candidate points of a lane without any unnecessary searching. By means of inverse perspective transform which removes the perspective effect on the edge candidate points, we transform the edge candidate information in the Image Coordinate System(ICS) into the plan-view image in the World Coordinate System(WCS). We define a linear approximation filter and remove faulty edge candidate points by using it. This paper aims at approximating more correctly the lane of an actual road by applying the least-mean square method with the fault-removed edge information for curve fitting.e fitting.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.726-734
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• 2007

Generally, a mobile robot is moved by original input programs. However, it is very hard for a non-expert to change the program generating the moving path of a mobile robot, because he doesn't know almost the teaching command and operating method for driving the robot. Therefore, the teaching method with speech command for a handicapped person without hands or a non-expert without an expert knowledge to generate the path is required gradually. In this study, for easily teaching the moving path of the autonomous mobile robot, the autonomous mobile robot with the function of speech recognition is developed. The use of human voice as the teaching method provides more convenient user-interface for mobile robot. To implement the teaching function, the designed robot system is composed of three separated control modules, which are speech preprocessing module, DC servo motor control module, and main control module. In this study, we design and implement a speaker dependent isolated word recognition system for creating moving path of an autonomous mobile robot in the unknown environment. The system uses word-level Hidden Markov Models(HMM) for designated command vocabularies to control a mobile robot, and it has postprocessing by neural network according to the condition based on confidence score. As the spectral analysis method, we use a filter-bank analysis model to extract of features of the voice. The proposed word recognition system is tested using 33 Korean words for control of the mobile robot navigation, and we also evaluate the performance of navigation of a mobile robot using only voice command.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.608-613
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• 2006

There are many e-showing mall because of revitalization of e-commerce system. It is necessary to recommending system of products that is for saving time and effort of customer. In this paper, we propose the system that is applying classification among data mining techniques to analysis of log data of customer. This log data contains access of user and purchasing of products. The proposed system operates in two phases. The first phase is composed of data filter module and association extraction module among web pages. The second phase is composed of personalization module and rule generation module. Customer can easily know the recommended sites because the proposed system can present rank of the recommended web pages to customer. As a result, the proposed system can efficiently do recommending of products to customer.

• The Journal of Engineering Research
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• v.2 no.1
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• pp.39-47
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• 1997

This paper presents a single board digital signal processing system which can perform two-dimensional (2-D) digital infinite impulse response (IIR) filtering in realtime. We have developed an architecture to provide not only the necessary computational power but also a balance of the system input/output and computational requirements. The architecture achieves large system throughput by using highly parallel processing at both the system and processor levels. It reduces system data communication requirements significantly by taking advantage of a custom-designed processor and by providing each processor with its own input and ouput channel. After system initialization, almost 100 percent of the time is used for data processing. Data transfers occur concurrently with data processing. The functional level simulation reveals that the system throughput can reach as high as one pixel per system cycle. With only 10MHz clock frequency system, it can implement up to fourth order 2-D IIR filters for video-rate data ($512\times512$ pixels per frame at 30 frames per second). If we increase the system frequency, the system can be used for the preprocessing and postprocessing of video signal of HDTV.