• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.17-20
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• 2002

The methods that track and detect motion field based on image difference of successive images from camera can separate motion field and background effectively, but because of noise and background images getting proper difference images is hard to achieve. In this paper we propose a method that can improve difference image quality significantly. Three step process is used. At the first step, existence of motion field is determined, the second step is finding proper threshold value using 'Contrast Streching' technique which enables us to find proper motion field even in complex images. At last step, remaining noise is removed and motion field is determined.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.61-71
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• 2004

Recently, various techniques for face detection are studied, but most of them still have problems on processing in real-time. Therefore, in this paper, we propose novel techniques for real-time detection of human faces in sequential images using motion and chroma information. First, background model is used to find a moving area. In this procmoving area. edure, intensity values for reference images are averaged, then skin-color are detected in We use HCr color-space model and adaptive threshold method for detection. Second, binary image labeling is applied to acquire candidate regions for faces. Candidates for mouth and eyes on a face are obtained using differences between green(G) and blue(B), intensity(I) and chroma-red(Cr) value. We also considered distances between eye points and mouth on a face. Experimental results show effectiveness of real-time detection for human faces in sequential images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.267-275
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• 2019

Currently, due to the widespread use of a smartphone, QR codes allow users to access a variety of added services. However, the QR codes posted on curved surfaces tend to be non-uniformly illuminated and bring about the decline of recognition rate. So, in this paper, the block-adaptive binarization policy is adopted to find an optimal threshold appropriate for bimodal image like QR codes. For a large block, its histogram distribution is found to get an initial threshold and then the block is partitioned to reflect the local characteristics of small blocks. Also, morphological operation is applied to their neighboring boundary at the discontinuous at the QR code junction. This paper proposes an authentication method based on the color code, uniquely painted within QR code. Through a variety of practical experiments, it is shown that the proposed algorithm outperforms the conventional method in detecting QR code and also maintains good recognition rate up to 40 degrees on curved surfaces.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1853-1858
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• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.17-22
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• 2016

For the self-adaptive equalization of higher-order QAM systems, this paper proposes a new soft decision-directed (SDD) algorithm that opens the eye patterns quickly as well as significantly reducing the error level in the steady-state when it is applied to the initial equalization stage with completely closed eye patterns. The proposed method for M-QAM application minimized the computational complexity of the existing SDD by the symbol estimated based on the two symbols closest to the observation, and greatly simplified the soft decision independently of the QAM order. Furthermore, in the symbol estimating it increased the reliability of the estimates by applying the superior properties of the sigmoid function and avoiding the erroneous estimation of the threshold function. In addition, the initialization performance was improved when an error is generated to update the equalizer, weighting the symbol decision by the threshold function to the error, resulting in an extension of the range of error fluctuations. As a result, the proposed method improves remarkably the computational complexity and the properties of initialization and convergence of the traditional SDD. Through simulations for 64-QAM and 256-QAM under multipath channel conditions with additive noise, the usefulness of the proposed methods was confirmed by comparing the performance of the proposed 2-SDD and two forms of weighted 2-SDD with CMA.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.87-94
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• 2009

Noise estimation and suppression are a crucial factor of many speech communication and recognition systems. In this paper, proposed algorithm is based on the ratio of variance normalized of noisy power spectrum in time-frequency domain. Our proposed algorithm tracks the threshold and controls the trade-off between residual noise and distortion. This algorithm is evaluated by the ITU-T P.835 signal distortion (SIG) and segment signal to noise ratio (SNR), and is superior to the conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.10
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• pp.2102-2111
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• 1997

Hand written numeral is projected on the 3D space after pre-processing of inputs and it makes a index by tracking of numerals. It computes the distance between extracted every features. It is used by input part of recognition process from the statistical historgram of the normalization of data in order to adaptation from variation. One hundred unmeral patterns have used for making a standard feature map and 100 pattern for the recogintion experiment. The result of it, we have the recoginition rete is 93.5% based on thresholding is 0.20 and 97.5% based on 0.25.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.780-785
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• 2015

Recently HTTP (Hypertext Transfer Protocol) adaptive streaming services have been the subject of much attention. The video quality control scheme of conventional HTTP adaptive streaming services estimates bandwidth using segment throughput and smooths out the sample of segment throughput. However, the conventional scheme has the problem of QoE (Quality of experience) degradation occurring with buffer underflow and frequent quality change due to the fixed number of samples. In order to solve this problem, we propose a video quality control scheme based on segment throughput and buffer occupancy. The proposed scheme determines the number of samples according to the variation of segment throughput. The proposed scheme also controls video quality based on the threshold of bitrate to keep stable buffer occupancy. The simulation results show that proposed scheme improves QoE by preventing buffer underflow and decreasing quality change when compared with the conventional scheme.

• Journal of the Korea Computer Graphics Society
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• v.29 no.1
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• pp.1-11
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• 2023

Deep-learning-based image segmentation is one of the most widely employed lane detection approaches, and it requires a post-process for extracting the key points on the lanes. A general approach for key-point extraction is using a fixed threshold defined by a user. However, finding the best threshold is a manual process requiring much effort, and the best one can differ depending on the target data set (or an image). We propose a novel key-point extraction algorithm that automatically adapts to the target image without any manual threshold setting. In our adaptive key-point extraction algorithm, we propose a line-level normalization method to distinguish the lane region from the background clearly. Then, we extract a representative key point for each lane at a line (row of an image) using a kernel density estimation. To check the benefits of our approach, we applied our method to two lane-detection data sets, including TuSimple and CULane. As a result, our method achieved up to 1.80%p and 17.27% better results than using a fixed threshold in the perspectives of accuracy and distance error between the ground truth key-point and the predicted point.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2004.11a
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• pp.193-196
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• 2004

본 논문에서는 열악한 주파수 선택적 페이딩이나 고속 페이딩 채널 환경에서 ATSC(Advanced Television System Committee) DTV 수신기의 등화 성능 향상을 위해 필터 탭을 선택적으로 사용하는 스파스 적응 등화기 구조를 제안한다. 제안된 등화기는 채널 추정을 수행한 후 등화기 초기화를 위한 탭 계수를 찾는다. 구해진 등화기 탭의 초기화 계수에 대해 특정 임계값을 적용하여 유효한 탭을 선택하여 활성화시킨다. 그리고 활성화 된 탭만을 이용하여 채널 등화를 수행한다. 결과적으로 기존 등화기와 동일한 탭 길이를 가지 고 있지만, 실제 사용하는 탭 수가 작아지므로 등화기의 단계상수를 크게 만들어 고속 페이딩 채널의 변화를 빠르게 추적할 수 있게 된다. 제안된 등화기 알고리듬의 성능 개선을 확인하기 위한 실험으로 ATSC DTV 성능분석 시 일반적으로 사용되는 브라질 채널 및 ATSC등화 성능 요구 조건에 대해 기존의 등화기와 비교 분석하였다. 그 결과 기존의 등화기와 같은 안정성을 가지면서 빠른 수렴 속도를 가지고 고속 페이딩 채널 보상 능력의 큰 향상을 보였다.