• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.7-15
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• 2014

This paper proposed a recognition algorithm of human facial expressions using the PCA and the template matching. Firstly, face image is acquired using the Haar-like feature mask from an input image. The face image is divided into two images. One is the upper image including eye and eyebrow. The other is the lower image including mouth and jaw. The extraction of facial components, such as eye and mouth, begins getting eye image and mouth image. Then an eigenface is produced by the PCA training process with learning images. An eigeneye and an eigenmouth are produced from the eigenface. The eye image is obtained by the template matching the upper image with the eigeneye, and the mouth image is obtained by the template matching the lower image with the eigenmouth. The face recognition uses geometrical properties of the eye and mouth. The simulation results show that the proposed method has superior extraction ratio rather than previous results; the extraction ratio of mouth image is particularly reached to 99%. The face recognition system using the proposed method shows that recognition ratio is greater than 80% about three facial expressions, which are fright, being angered, happiness.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.378-391
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• 2010

In this paper, it is tested that an individual is able to be identified with finger face images and the results are presented. Special operators, FFG(Facet Function Gradient) masks by which the gradient of a facet function fit on a gray levels of image patches can be computed are used and a new procedure named F-algorithm is introduced to match the finger face images. The finger face image is divided into the equal subregions and each subregions are divided into equal patches with this algorithm. The FFG masks are used for convolution operation over each patch to produce scalar values. These values from a feature matrix, and the identity of fingers is determined by a norm of the elements of the feature matrices. The distribution of the norms shows conspicuous differences between the pairs of hand images of the same persons and the pairs of the different persons. This is a result to prove the ability of discrimination with the finger face image. An identification rate of 95.0% is obtained as a result of the test in which 500 hand images taken from 100 persons are processed through F-algorithm. It is affirmed that the finger face reveals to be such a good biometrics as other hand parts owing to the ability of discrimination and the identification rate.

• Proceedings of the Materials Research Society of Korea Conference
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• 2003.03a
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• pp.138-138
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• 2003

광대역 LiNbO$_3$ 광변조기의 초고속 광 변조 구현을 위해서는 RF/ optical 속도 정합 및 임피던스 매칭 조건 하에서 낮은 구동전압을 얻을 수 있는 ridge 구조의 제작이 필수적이며 이런 구조 제작하기 위해서는 식각 속도와 식각면 거칠기 식각 profile 및 식각 과정에서의 반응물의 감소 등과 같은 개선을 위한 연구가 필요하다. 본 연구에서는 LiNbO$_3$ 기판 위에 메탈 마스크를 형성한 후 비등방성 (anisotropic) 건식 식각 방법인 NLD (Neutral Loop Discharge)로 플라즈마 식각을 하였다. NLD plasma 식각은 1Pa 이하의 압력에서 낮은 전자 온도를 갖는 고밀도 플라즈마를 생성하고 이온 플라즈마를 형성하여 LiNbO$_3$ 표면의 원자와 분자를 이온충돌효과를 이용하여 물리적인 식각과 discharge로 형성된 레디칼 (radical)과의 상호작용에 의한 화학적 식각 메커니즘에 의한 방법으로 plasma에 의한 시편의 손상이 적으며 식각 속도가 또한 높은 것이 특징이다. 본 논문에서는 안테나 파워와 가스의 유량에 따른 LiNbO$_3$ 식각 profile 특성에 관하여 연구 하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.866-872
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• 2004

In fingerprint verification and identification, fingerprint and background region should be segmented. For this purpose, most systems obtain variance of brightness of X and Y direction using Sobel mask. To decide given local region is background or not, the variance is compared with a certain threshold. Although this method is simple, most fingerprint image does not separated with two region of fingerprint and background region. In this paper, we presented a new segmentation algorithm based on run-length connectivity analysis. For a given binary image after thresholding, suggested algorithm calculates RL of X and Y direction. Until the given image is segmented to two regions, small run region is successively inverted. Experimental result show that this algorithm effectively separates fingerprint region and background region.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.207-209
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• 2021

In recent years, with the development of artificial intelligence and IoT technology, automation and unmanned technology are in progress in various fields, and the importance of image processing such as object tracking, medical images and object recognition, which are the basis of this, is increasing. In particular, in systems requiring detailed data processing, noise reduction is used as a pre-processing step, but the existing algorithm has a disadvantage that blurring occurs in the filtering process. Therefore, in this paper, we propose a filter algorithm using modified spatial weights to minimize information loss in the filtering process. The proposed algorithm uses mask matching to remove AWGN, and obtains the output of the filter by adding or subtracting the output of the modified spatial weight. The proposed algorithm has superior noise reduction characteristics compared to the existing method and reconstructs the image while minimizing the blurring phenomenon.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.3
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• pp.249-255
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• 2017

The finger vein recognition technology is a method to acquire a finger vein image by illuminating infrared light to the finger and to authenticate a person through processes such as feature extraction and matching. In order to recognize a finger vein, a 2D mask-based two-dimensional convolution method can be used to detect a finger edge but it takes too much computation time when it is applied to a low cost micro-processor or micro-controller. To solve this problem and improve the recognition rate, this study proposed an extraction method for the region of interest based on virtual core points and moving average filtering based on the threshold and absolute value of difference between pixels without using 2D convolution and 2D masks. To evaluate the performance of the proposed method, 600 finger vein images were used to compare the edge extraction speed and accuracy of ROI extraction between the proposed method and existing methods. The comparison result showed that a processing speed of the proposed method was at least twice faster than those of the existing methods and the accuracy of ROI extraction was 6% higher than those of the existing methods. From the results, the proposed method is expected to have high processing speed and high recognition rate when it is applied to inexpensive microprocessors.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.348-355
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• 2010

In spite of development in speech recognition technology, speech recognition under noisy environment is still a difficult task. To solve this problem, Researchers has been proposed different methods where they have been used visual information except audio information for visual speech recognition. However, visual information also has visual noises as well as the noises of audio information, and this visual noises cause degradation in visual speech recognition. Therefore, it is one the field of interest how to extract visual features parameter for enhancing visual speech recognition performance. In this paper, we propose a method for visual feature parameter extraction based on image-base approach for enhancing recognition performance of the HMM based visual speech recognizer. For experiments, we have constructed Audio-visual database which is consisted with 105 speackers and each speaker has uttered 62 words. We have applied histogram matching, lip folding, RASTA filtering, Liner Mask, DCT and PCA. The experimental results show that the recognition performance of our proposed method enhanced at about 21% than the baseline method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.367-370
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• 2015

This paper proposes an improve method of the detection performance of traffic lights for autonomous driving cars. Earlier detection methods used to adopt color thresholding, template matching and based learning maching methods, but its have some problems such as recognition rate decreasing, slow processing time. The proposed method uses both detection mask and morphological preprocessing. Firstly, input color images are converted to YCbCr image in order to strengthen its illumination, and horizontal edge components are extracted in the Y Channel. Secondly, the region of interest is detected according to morphological characteristics of the traffic lights. Finally, the traffic signal is detected based on color distributions. The proposed method showed that the detection rate and processing time improved rather than the conventional algorithm about some surrounding environments.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.249-255
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• 2004

In fingerprint verification and identification, fingerprint and background region should be segmented. For this purpose, most systems obtain variance of brightness of X and Y direction using Sobel mask. To decide given local region is background or not, the variance is compared with a certain threshold. Although this method is simple, most fingerprint image does not separated with two region of fingerprint and background region. In this paper, we presented a new segmentation algorithm based on Run-Length Connectivity analysis. For a given binary image after thresholding, suggested algorithm calculates RL of X and Y direction. Until the given image is segmented to two regions, small run region is successively inverted. Experimental result show that this algorithm effectively separates fingerprint region and background region.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.55-58
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• 2024

In this paper, we propose a design and implementation of big data-based fine dust anomaly detection machine learning system. The proposed is system that classifies the fine dust air quality index through meteorological information composed of fine dust and big data. This system classifies fine dust through the design of an anomaly detection algorithm according to the outliers for each air quality index classification categories based on machine learning. Depth data of the image collected from the camera collects images according to the level of fine dust, and then creates a fine dust visibility mask. And, with a learning-based fingerprinting technique through a mono depth estimation algorithm, the fine dust level is derived by inferring the visibility distance of fine dust collected from the monoscope camera. For experimentation and analysis of this method, after creating learning data by matching the fine dust level data and CCTV image data by region and time, a model is created and tested in a real environment.